diff --git a/autotest/test_market_data.csv b/autotest/test_market_data.csv new file mode 100644 index 000000000..d7ec1e997 --- /dev/null +++ b/autotest/test_market_data.csv @@ -0,0 +1,288 @@ +2022-02-22 00:00:00,50.0,21372.0 +2022-02-22 00:05:00,50.9,21372.0 +2022-02-22 00:10:00,50.3,21372.0 +2022-02-22 00:15:00,49.8,21372.0 +2022-02-22 00:20:00,49.4,21372.0 +2022-02-22 00:25:00,63.0,21372.0 +2022-02-22 00:30:00,62.5,21372.0 +2022-02-22 00:35:00,63.5,21372.0 +2022-02-22 00:40:00,52.3,21372.0 +2022-02-22 00:45:00,49.4,21372.0 +2022-02-22 00:50:00,49.0,21372.0 +2022-02-22 00:55:00,49.2,21372.0 +2022-02-22 01:00:00,52.9,20856.0 +2022-02-22 01:05:00,53.4,20856.0 +2022-02-22 01:10:00,51.1,20856.0 +2022-02-22 01:15:00,50.7,20856.0 +2022-02-22 01:20:00,50.5,20856.0 +2022-02-22 01:25:00,49.3,20856.0 +2022-02-22 01:30:00,49.7,20856.0 +2022-02-22 01:35:00,47.6,20856.0 +2022-02-22 01:40:00,46.5,20856.0 +2022-02-22 01:45:00,46.3,20856.0 +2022-02-22 01:50:00,46.1,20856.0 +2022-02-22 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23:45:00,51.3,23622.0 +2022-02-22 23:50:00,48.8,23622.0 +2022-02-22 23:55:00,49.7,23622.0 diff --git a/autotest/test_market_data.glm b/autotest/test_market_data.glm new file mode 100644 index 000000000..2acc10082 --- /dev/null +++ b/autotest/test_market_data.glm @@ -0,0 +1,32 @@ +// example market_data from CAISO + +#ifexist ../test_market_data.csv +#define DIR=.. +#endif + +#python -m market_data -m=CAISO -d=0096WD_7_N001 -s=20220222 -e=20220223 -c=test_market_data.csv -g=test_market_data_opt.glm + +clock +{ + timezone "PST+8PDT"; + starttime "2022-02-22 00:00:00 PST"; + stoptime "2022-02-23 00:00:00 PST"; +} + +module tape +{ + csv_header_type NAME; +} + +#include "test_market_data_opt.glm" + +object recorder +{ + parent ${MARKET_DATA}; + property "LMP,MW"; + file "test_market_data_out.csv"; +} + +#ifexist ../test_market_data.csv +#on_exit 0 diff ../test_market_data.csv test_market_data.csv > gridlabd.diff +#endif diff --git a/autotest/test_market_data_opt.glm b/autotest/test_market_data_opt.glm new file mode 100644 index 000000000..eaabcaa08 --- /dev/null +++ b/autotest/test_market_data_opt.glm @@ -0,0 +1,24 @@ +class market_data +{ + char32 market; + char256 node; + double LMP; + double MW; +} +module tape; +#ifdef MARKET_DATA +#set MARKET_DATA=$MARKET_DATA market_data@caiso_0096WD_7_N001_20220222-20220223 +#else +#define MARKET_DATA=market_data@caiso_0096WD_7_N001_20220222-20220223 +#endif +object market_data +{ + name "market_data@caiso_0096WD_7_N001_20220222-20220223"; + market caiso; + node 0096WD_7_N001; + object player + { + file "test_market_data.csv"; + property "LMP,MW"; + }; +} diff --git a/autotest/test_market_data_out.csv b/autotest/test_market_data_out.csv new file mode 100644 index 000000000..2130f1e21 --- /dev/null +++ b/autotest/test_market_data_out.csv @@ -0,0 +1,282 @@ +timestamp,LMP,MW +2022-02-22 00:00:00 PST,+50,+21372 +2022-02-22 00:05:00 PST,+50.9,+21372 +2022-02-22 00:10:00 PST,+50.3,+21372 +2022-02-22 00:15:00 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22:55:00 PST,+65.7,+25064 +2022-02-22 23:00:00 PST,+75.8,+23622 +2022-02-22 23:05:00 PST,+76.4,+23622 +2022-02-22 23:10:00 PST,+76.5,+23622 +2022-02-22 23:15:00 PST,+73.1,+23622 +2022-02-22 23:20:00 PST,+67.8,+23622 +2022-02-22 23:25:00 PST,+64.3,+23622 +2022-02-22 23:30:00 PST,+58.3,+23622 +2022-02-22 23:35:00 PST,+57.3,+23622 +2022-02-22 23:40:00 PST,+57.1,+23622 +2022-02-22 23:45:00 PST,+51.3,+23622 +2022-02-22 23:50:00 PST,+48.8,+23622 +2022-02-22 23:55:00 PST,+49.7,+23622 diff --git a/autotest/test_market_model.glm b/autotest/test_market_model.glm new file mode 100644 index 000000000..224047518 --- /dev/null +++ b/autotest/test_market_model.glm @@ -0,0 +1,2 @@ +#python -m market_data -m=CAISO -d=0096WD_7_N001 -s=20220101 -e=20220201 -c=caiso_20220101-20220201.csv +#python -m market_model -f=caiso_20220101-20220201.csv -i=LMP -o=MW -k=24 -l=3 diff --git a/docs/Install/Docker.md b/docs/Install/Docker.md index 7cbd22db1..eff92ae15 100644 --- a/docs/Install/Docker.md +++ b/docs/Install/Docker.md @@ -107,7 +107,13 @@ bash$ docker build --build-arg "BRANCH=develop" . # Caveat -Docker has a known issue on some versions of Linux that if your system goes to sleep while the daemon is running. After your system wakes up, containers may run a lot slower. The only known solution at this time is to restart the docker daemon. See [SourceForge Forum](https://sourceforge.net/p/gridlab-d/discussion/842561/thread/ef4a34cb98) for details. +Docker has a known issue on some versions of Linux running on Macs if your system goes to sleep while the daemon is running. After your system wakes up, containers may run a lot slower. You can fix this problem by adding the following line to the file `/Applications/Docker.app/Contents/Resources/linuxkit/cmdline`: + +~~~ +tsc=reliable +~~~ + +See [SourceForge Forum](https://sourceforge.net/p/gridlab-d/discussion/842561/thread/ef4a34cb98) for details. # See also diff --git a/docs/Utilities/Market_data.md b/docs/Utilities/Market_data.md index 0bb11cdfe..81178e06c 100644 --- a/docs/Utilities/Market_data.md +++ b/docs/Utilities/Market_data.md @@ -67,5 +67,8 @@ option. # See also +* [[/Utilities/Market_model]] + +ISO Data Sources: * [CAISO](https://caiso.com/) * [ISONO](https://isone.com/) diff --git a/docs/Utilities/Market_model.md b/docs/Utilities/Market_model.md new file mode 100644 index 000000000..bffad3f08 --- /dev/null +++ b/docs/Utilities/Market_model.md @@ -0,0 +1,99 @@ +[[/Utilities/Market_model]] -- Create market model from ISO data + +# Synopsis + +Shell: + + sh$ gridlabd python market_model [-f|--file=DATACSV] [-i|--input=INPUTCOL(S)] [-o|--output=OUTPUTCOL] [ + -l|--lookahead=LOOKAHEAD] [-k|--order] [-g|--glm=GLMNAME] [-I|--init=ISODATETIME] [-M|--module=MODULENAME] [ + -C|--class=CLASSNAME] [-N|--name=OBJNAME] [-P|--player[=PLAYERCSV]] [-R|--recorder=RECORDERCSV] [--{by,with}_{hour, + day,month,year}] [-v|--verbose] [-w|--warning] [-q|--quiet] [-d|--debug] [-h|--help|help] + +GLM: + + #python -m market_model [-f|--file=DATACSV] [-i|--input=INPUTCOL(S)] [-o|--output=OUTPUTCOL] [ + -l|--lookahead=LOOKAHEAD] [-k|--order] [-g|--glm=GLMNAME] [-I|--init=ISODATETIME] [-M|--module=MODULENAME] [ + -C|--class=CLASSNAME] [-N|--name=OBJNAME] [-P|--player[=PLAYERCSV]] [-R|--recorder=RECORDERCSV] [--{by,with}_{hour, + day,month,year}] [-v|--verbose] [-w|--warning] [-q|--quiet] [-d|--debug] + + +# Description + +The `market_model` utility creates NxM filters from N input and M output +channels of data by fitting a Kth-order discrete-time transfer functions to +the data using a least-squares linear regressions of the equation: + + Y0 = -a1*Y1 - a2*Y2 - ... - aK*Yk + b0*U0 + b1*U1 + ... + bK*Uk + +where + + Y0 is the output channel 0 at time t + Y1 is the output channel 0 at time t-1 + ... + YK is the output channel 0 at time t-K + + U0 is the input channel 0 at time t + U1 is the output channel 0 at time t-1 + ... + UK is the input channel 0 at time t-K + + a and b are the coefficients of the transfer functions + +If a GLMNAME and CLASSNAME are specified, then an object of that class will be +created with the filter as inputs and object properties as outputs. If the +module is not specified, the class is defined as a runtime class with all +inputs and outputs specified as doubles. If OBJNAME is specified the object is +given that name, otherwise a random object name is generated and appended to the +OBJNAME global variable. + +If PLAYERCSV is specified, then any created object will have the inputs +assigned from that file. If RECORDERCSV is specified, then any object created will +have the outputs recorded to that file. + +If --with_{hour,day,month,year} is specified, then an hour, day, month, or +year column is added to the input data. If --by_{hour,day,month,year} is +specified, then the input data is grouped by hour, day, month, or year. If +--with_time is specified, then the elapsed time in the specified units is +included in a column. + +## Configuration + +By default all the columns of the input and output files are used in the +filter model identification. Only columns listed in the `converters` class will be used to generate filters. +The class `converters` must be defined, and contain the properties `cols`. These must be +dictionaries where the keys are the column names, and values of the +converters to be used for those columns. + +For the example below, the following converter files can be used: + + from datetime import datetime + class converters: + def real(x): + try: + return float(x) + except: + return float('NAN') + + def datetime(x): + return datetime.strptime(x, "%Y-%m-%d %H:%M:%S") + + cols = {"START_TIME_LOCAL": datetime, 'MW': real, 'LMP': real} + +Note that using a converter often speeds up the CSV read process because the +date/time format is specified and the loader does not need to deduce the +format automatically, which can take significant additional time. + +To limit which columns of the input or output files are used, it is sufficient +to omit them from the converter list. For example, + + cols = {"START_TIME_LOCAL":datetime, "MW":real} + +omits the LMP data in the input file. + +# Example + + $ python market_model -f=caiso_20220101-20220201.csv -i=LMP -o=MW -k=24 -l=3 + +# See also + +* [[/Utilities/Market_data]] diff --git a/install b/install new file mode 100755 index 000000000..89735944b --- /dev/null +++ b/install @@ -0,0 +1,361 @@ +#!/bin/bash + +# set the path to use during installation +export PATH=/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin + +# check sudo +if [ "$(whoami)" == "root" ]; then + function sudo () + { + $* + } +else + sudo --version >/dev/null 2>&1 || (echo "$0: sudo is required"; exit 1) +fi + +# local folder +VAR="/usr/local/var/gridlabd" +if [ ! -d "$VAR" ]; then + mkdir -p $VAR || ( sudo mkdir -p $VAR && sudo chown ${USER:-root} $VAR ) +fi + +# setup logging +LOG="$VAR/install.log" +function log() +{ + case "$*" in + ("clear") + rm -f $LOG + shift 1 + ;; + (*) + echo "$*" >> $LOG + return + ;; + esac +} + +# setup exit handling +function on_exit() +{ + log "STOP: $(date)" + log "STATUS: ${STATUS:-ok}" + sleep 1 + if [ "$VERSION" == "yes" ]; then + kill %1 + fi +} +trap on_exit EXIT + +# setup error handling +function error() +{ + [ "${SILENT:-no}" == "no" -a "${VERBOSE:-no}" == "no" ] && echo "ERROR: $*" > /dev/stderr + echo "ERROR: $*" >> $LOG + STATUS="error" + exit 1 +} + +# check for commands that absolutely necessary to proceed +function require() +{ + $1 ${2:---version} > /dev/null 2>&1 || error "$1 is required" +} + +# check workdir +require dirname - +cd $(dirname $0) +if [ ! -f "install.sh" -o ! -d "build-aux" ]; then + error "$0 must be run from the source root" +fi + +# load the configuration +if [ ! -f "install.conf-default" ]; then + error "missing install.conf-default" +fi +source "install.conf-default" +if [ -f "install.conf" ]; then + source "install.conf" +fi + +# define commands that used by command line options +function info() +{ + echo "CHECK=$CHECK" + echo "DOCS=$DOCS" + echo "FORCE=$FORCE" + echo "INDEX=$INDEX" + echo "LINK=$LINK" + echo "PARALLEL=$PARALLEL" + echo "PREFIX=$PREFIX" + echo "QUICK=$QUICK" + echo "SETUP=$SETUP" + echo "SILENT=$SILENT" + echo "TEST=$TEST" + echo "UPDATE=$UPDATE" + echo "VERBOSE=$VERBOSE" +} + +function help() +{ + cat <<-END + Syntax: $0 [] [] + Install options: + -h --help Print this helpful output + --info Print information about this install + -c --no-check Do not check system for requirements + -d --no-docs Do not install documentation + -f --force Force install into existing target folder + -i --no-index Do not index data archives + -l --no-link Do not link new install to activate it + -t --no-test Do not run validation tests + -u --no-update Do not update system to meet requirements + -p --parallel Enable parallelism when possible + --prefix Set install prefix + --save Save the current configuration as default + --no-setup Perform system setup + --reset Reset the configuration to default + -q --quick Run only updates instead of a clean install + -s --silent Run without showing commands + -v --verbose Run showing log output + --validate Run validation tests + --version Override the default version name + END +} + +# process command line options +while [ $# -gt 0 ]; do + case "$1" in + (-q|--quick) + QUICK="yes" + ;; + (--validate) + TEST="yes" + ;; + (-i|--no-index) + INDEX="no" + ;; + (-c|--no-check) + CHECK="no" + ;; + (-t|--no-test) + TEST="no" + ;; + (-l|--no-link) + LINK="no" + ;; + (-d|--no-docs) + DOCS="no" + ;; + (-u|--no-update) + UPDATE="no" + ;; + (-s|--silent) + SILENT="yes" + ;; + (-v|--verbose) + VERBOSE="yes" + ;; + (-p|--parallel) + PARALLEL="yes" + ;; + (--prefix) + PREFIX="$2" + if [ ! -d "$PREFIX" ]; then + error "$PREFIX does not exist" + fi + INSTALL="$PREFIX/$VERSION" + shift 1 + ;; + (--no-setup) + SETUP="no" + ;; + (-f|--force) + FORCE="yes" + ;; + (--save) + info > "install.conf" + exit 0 + ;; + (--reset) + rm -f "install.conf" + exit 0 + ;; + (-h|--help) + help + exit 0 + ;; + (--info) + info + exit 0 + ;; + (--version) + VERSION="$2" + shift 1 + ;; + (--install) + INSTALL="$2" + shift 1 + ;; + (*) + error "'$1' is not a valid option" + exit 1 + ;; + esac + shift 1 +done + +# start logging +log clear +log "START: $(date)" +log "COMMAND: $0 $*" +log "CONTEXT: ${USER:-unknown}@${HOSTNAME:-localhost}:$PWD" +log "SYSTEM: $(uname -a)" +log "$(info | sed -e '1,$s/=/: /')" + +# define functions used during install processing +function run () +{ + if [ "$SILENT" == "no" -a "$VERBOSE" == "no" ]; then + echo $* + fi + NAME=${USER:-nobody} + CMD=($(ps -ocommand $$ | tail -n 1)) + CMD=$(basename ${CMD[0]}) + if [ $(whoami) == "root" ]; then + NAME="root" + fi + echo "RUN: [$CMD://$NAME@$HOSTNAME$PWD] $*" >> $LOG + T0=$(date '+%s') + $* >> $LOG 2>&1 || error "$0 failed -- see $LOG for details " + T1=$(date '+%s') + log "OK: done in $((T1-T0)) seconds" +} + +# enable verbose logging +if [ "$VERBOSE" == "yes" ]; then + tail -f -n 10000 $LOG 2>/dev/null & +fi + +# run setup +if [ "$SETUP" == "yes" ]; then + if [ ! -f "build-aux/setup.sh" ]; then + error "build-aux/setup.sh not found" + fi + SOK="$VAR/setup.ok" + if [ ! -f "$SOK" -o "$FORCE" == "yes" ]; then + run build-aux/setup.sh + date > "$SOK" + elif [ -f "$SOK" ]; then + log "SETUP: already completed at $(cat $SOK)" + else + log "SETUP: skipping" + fi +fi + +# dynamic variables +require git +VERSION=${VERSION:-`build-aux/version.sh --name`} +INSTALL=${INSTALL:-$PREFIX/opt/gridlabd/$VERSION} + +# run checks +if [ "$LINK" == "yes" -a -f "$PREFIX/bin/gridlabd" -a ! -L "$PREFIX/bin/gridlabd" ]; then + OLDVER="$(gridlabd --version | cut -f2 -d' ')-saved_$(date '+%y%m%d')" + VDIR=$OLDVER + while [ -e $PREFIX/opt/gridlabd/$VDIR ]; do + VDIR=${OLDVER}_${VNUM:-0} + VNUM=$((${VNUM:-0}+1)) + done + log "BACKUP: saving $OLDVER to $PREFIX/opt/gridlabd/$VDIR and linking it back to current version" + run mkdir -p $PREFIX/opt/gridlabd/$VDIR || error "unable to create $VDIR to save current version" + run ln -sf $PREFIX/opt/gridlabd/$VDIR $PREFIX/opt/gridlabd/current + for item in bin include lib share; do + [ ! -d $PREFIX/opt/gridlabd/$VDIR/$item ] && run mkdir -p $PREFIX/opt/gridlabd/$VDIR/$item + [ ! -d $PREFIX/opt/gridlabd/$VDIR/$item ] && run mv $PREFIX/$item/gridlabd* $PREFIX/opt/gridlabd/$VDIR/$item/ + run ln -sf $PREFIX/opt/gridlabd/$VDIR/$item/gridlabd $PREFIX/opt/gridlabd/current/$item/gridlabd + run ln -sf $PREFIX/opt/gridlabd/current/$item /$PREFIX/$item/gridlabd + done + run ln -s $PREFIX/opt/gridlabd/current/bin/gridlabd.bin $PREFIX/bin/gridlabd.bin + error "stopping here for debugging reasons -- this error message should be deleted" +fi +if [ "$CHECK" == "yes" ]; then + if [ "$LINK" == "yes" -a -d "$PREFIX/gridlabd" -a ! -L "$PREFIX/gridlabd" ]; then + error "$PREFIX/gridlabd exists but it is not a symbolic link" + fi + if [ "$DOCS" == "yes" ]; then + require doxygen + require mono + require natural_docs + fi +fi + +# run autoconf +if [ "$QUICK" == "no" ]; then + if [ ! -f "build-aux/ltmain.sh" ]; then + require libtoolize + run libtoolize + fi + require autoscan + run autoscan +fi +if [ ! -d "autom4te.cache" -o "$QUICK" == "no" ]; then + require autoreconf + run autoreconf -isf +fi + +# prep install dir +log "VERSION: $VERSION" +log "INSTALL: $INSTALL" +if [ -e "$INSTALL" -a "$FORCE" == "no" ]; then + error "$INSTALL already exists, please delete it first" +fi +mkdir -p "$INSTALL" || ( run sudo mkdir -p "$INSTALL" && run sudo chown -R "${USER:-root}" "$INSTALL" ) +if [ ! -f "configure" -o "$QUICK" == "no" ]; then + run ./configure --prefix="$INSTALL" $* +fi + +NPROC=1 +SYSTEM=$(uname -s) +if [ "$PARALLEL" == "yes" ]; then + if [ "$SYSTEM" == "Linux" ]; then + NPROC=$(lscpu | grep '^CPU(s):' | cut -f2- -d' ') + elif [ "$SYSTEM" == "Darwin" ]; then + NPROC=$(sysctl -n machdep.cpu.thread_count) + fi +fi + +# build everything +export PATH=/usr/local/bin:/usr/bin:/bin +run make -j$((3*$NPROC)) system + +if [ "$DOCS" == "yes" ]; then + run make html + run cp -r "documents/html" "$INSTALL" + run cp "documents/index.html" "$INSTALL/html" +fi +if [ "$INDEX" == "yes" ]; then + run make index +fi +if [ "$TEST" == "yes" ]; then + export PATH=$INSTALL/bin:$PATH + export GLPATH=$INSTALL/share/gridlabd/weather/US + export LD_LIBRARY_PATH=.:${LD_LIBRARY_PATH:-.} + run $INSTALL/bin/gridlabd -T $NPROC --validate +fi + +# activate this version +if [ -x "$INSTALL/bin/gridlabd-version" -a "$TEST" == "yes" ]; then + log "ACTIVATE: automatic" + run $INSTALL/bin/gridlabd version set "$VERSION" +elif [ "$LINK" == "yes" ]; then + log "ACTIVATE: manual" + [ ! -L "$PREFIX/opt/gridlabd/current" ] && run sudo rm -f "$PREFIX/opt/gridlabd/current" + run sudo ln -sf "$INSTALL" "$PREFIX/opt/gridlabd/current" + for dir in bin lib include share; do + run sudo ln -sf $PREFIX/opt/gridlabd/current/$dir/gridlabd $PREFIX/$dir/gridlabd + done + run sudo ln -sf $PREFIX/opt/gridlabd/current/bin/gridlabd.bin $PREFIX/bin/gridlabd.bin +fi + +# all done :-) +exit 0 \ No newline at end of file diff --git a/market_data.py b/market_data.py new file mode 100644 index 000000000..8b1df2214 --- /dev/null +++ b/market_data.py @@ -0,0 +1,651 @@ +"""ISO Market Data tool + +SYNOPSIS + +Shell: + + bash$ gridlabd market_data [-m|--market=MARKETNAME] [-d|--node=NODE] [-s|--startdate=STARTDATE] + [-e|--enddate=ENDDATE] [-h|--help|help] [--credentials[=FILENAME.json]] + [-n|--name=OBJNAME] [-c|--csv=CSVFILE] [-g|--glm=GLMFILE] + +GLM: + + #market_data [-m|--market=MARKETNAME] [-d|--node=NODE] [-s|--startdate=STARTDATE] + [-e|--enddate=ENDDATE] [-h|--help|help] [--credentials[=FILENAME.json]] + [-n|--name=OBJNAME] [-c|--csv=CSVFILE] [-g|--glm=GLMFILE] + +Python: + + bash$ gridlabd python + # >>> import market_data as md + # >>> data = md.get_market_data(MARKETNAME,NODE,STARTDATE,ENDDATE[,CREDENTIALS]) + # >>> md.write_csv(data,CSVNAME) + # >>> md.write_glm(data,GLMNAME,[NAME]) + +DESCRIPTION + +The market_data tool obtain wholesale electricity market price data from +independent system operators (ISO). The ISO must be specified using the +MARKETNAME parameter. Currently supported markets are "CAISO" and "ISONE". + +In addition, the NODE must be specified when obtaining locational price data. +Lists of node names may be obtained from the market websites. For CAISO see +http://www.caiso.com/TodaysOutlook/Pages/prices.html for a map of available +price nodes. For ISONE see https://www.iso-ne.com/markets-operations/settlements/pricing-node-tables/ + +The STARTDATE and ENDDATE value must be specified in the form "YYYYMMDD". + +When the `--credentials` option is used without parameters, the user is +prompted for a username and password. When run from a terminal window, a +browser window is automatically opened for the specific ISO website to +facilitate obtaining the username and password. The result is stored in the +default credentials file for GridLAB-D ISO credentials +(i.e., `$HOME/.gridlabd/iso_credentials.json`). When FILENAME is provided +with the credentials option, the credentials are loaded from the specified +file instead. Currently only the ISONE API requires credentials. For +example, to generate credentials for ISONE, use the command + + bash$ gridlabd market_data -m=ISONE --credentials + +By default the CSV data is output to the stdout, which may be redirected to a +file. If no GLM file is specified, then the CSV will contain a header row +indicating which columns contain the price and quantity data. If a GLM file +is specified, then the CSV data must be output to a file. By default the +GridLAB-D object name for the market data is generated automatically to +ensure it is unique. The object name may be specified using the `--name` +option. + +EXAMPLES + + bash$ gridlabd market_data -m=CAISO -d=0096WD_7_N001 -s=20220222 -e=20220223 + START_TIME_LOCAL,LMP,MW + 2022-02-22 00:00:00,50.0,21372.0 + 2022-02-22 00:05:00,50.9,21372.0 + 2022-02-22 00:10:00,50.3,21372.0 + 2022-02-22 00:15:00,49.8,21372.0 + ... + +SEE ALSO + +* [CAISO](https://caiso.com/) +* [ISONO](https://isone.com/) +""" + +import datetime as dt +import time +import sys +import os +import re +import requests +from requests.auth import HTTPBasicAuth +import pytz +import getpass +import json +import pandas as pd +from io import BytesIO +from pandas.api.types import is_datetime64_any_dtype as is_datetime + +from urllib.request import urlopen +from zipfile import ZipFile + +verbose_enable = False +warning_enable = True +quiet_enable = False +debug_enable = False +credentials_path = f"{os.getenv('HOME')}/.gridlabd/iso_credentials.json" + +def main(argv): + market = None + node = None + start_date = None + end_date = None + glm = None + name = None + csv = None + + if len(argv) == 1: + syntax(1) + + for arg in argv[1:]: + args = arg.split("=") + if type(args) is list and len(args) > 1: + token = args[0] + value = args[1] + elif type(args) is list: + token = args[0] + value = None + else: + token = args + value = None + + if token in ["-h", "--help", "help"]: + syntax() + elif token in ["-v","--verbose"]: + verbose_enable = True + elif token in ["--debug"]: + debug_enable = True + elif token in ["-w","--warning"]: + warning_enable = False + elif token in ["-q","--quiet"]: + quiet_enable = True + elif token in ["-m", "--market"]: + market = value.lower() + elif token in ["-d", "--node"]: + node = value + elif token in ["-s", "--startdate"]: + if bool(re.match(r"\d{8}", value)): + start_date = value + else: + error("Date syntax: YYYYMMDD", code=1) + elif token in ["-e", "--enddate"]: + if bool(re.match(r"\d{8}", value)): + end_date = value + else: + error("Date syntax: YYYYMMDD", code=1) + elif token in ["--credentials"]: + if not value: + if not market: + error(f"no market specified",code=1) + if not market in ["isone"]: + error(f"{market.upper()} does not require credentials",code=1) + elif sys.stdin.isatty(): + if os.system("open 'https://www.iso-ne.com/isoexpress/login?p_p_id=58&p_p_lifecycle=0&p_p_state=normal&p_p_mode=view" + "&saveLastPath=0&_58_struts_action=%2Flogin%2Fcreate_account'") != 0: + error( + "unable to open a browser window to get credentials, please visit \n " + "https://www.iso-ne.com/isoexpress/login?p_p_id=58&p_p_lifecycle=0&p_p_state=normal&p_p_mode=view" + "&saveLastPath=0&_58_struts_action=%2Flogin%2Fcreate_account" + ) + else: + print(f"Opening a browser window to register your credentials for {market.upper()}.") + user = input("Username: ") + pwd = getpass.getpass(prompt="Password: ") + global credentials_path + with open(credentials_path, "w") as outfile: + credentials = { + market : {"username": user, "password": pwd} + } + json.dump(credentials, outfile) + exit(0) + else: + error("cannot get credentials (not a tty)",code=2) + else: + credentials_path = value + + elif token in ["-g", "--glm"]: + glm = value + elif token in ["-n", "--name"]: + name = value + elif token in ["-c", "--csv"]: + csv = value + else: + error(f"option '{token}' is not valid") + + if None not in (market, node, start_date, end_date): + + try: + + market_data_df = get_market_data( + market=market, node=node, start_date=start_date, end_date=end_date + ) + + except Exception as err: + + etype, evalue, etrace = sys.exc_info() + error(f"unable to get market data ({etype.__name__} {evalue})",code=2) + + writeglm( + market = market, + node = node, + start_date = start_date, + end_date = end_date, + market_data = market_data_df, + csv = csv, + glm = glm, + name = name + ) + + else: + + error( + "market (-m), node (-d), start date (-s), and end date (-e) are required parameters" + ) + + if market is None: + + error("Markets can be: 'caiso', 'isone'", code=1) + + elif node is None: + + error( + "User must indicate a node. For a list of CAISO nodes, download the latest 'Full Network Model " + "Pricing Node Mapping' from the CAISO website. For a list of ISONE nodes, downloaded the latest " + "'PNODE Table' from the ISONE website.", + code=1, + ) + +def error(msg, code=None, exception=None): + """Display and error message and exit if code is a number.""" + if debug_enable: + raise Exception(msg) from exception + if not quiet_enable: + print(f"ERROR [market_data]: {msg}", file=sys.stderr) + if type(code) is int: + exit(code) + +def warning(msg): + if warning_enable: + print(f"VERBOSE [market_data]: {msg}",file=sys.stderr) + +def verbose(msg): + if verbose_enable: + print(f"VERBOSE [market_data]: {msg}",file=sys.stderr) + +def syntax(code=0): + """Display docs (code=0) or syntax help (code!=0) and exit.""" + if code == 0: + print(__doc__) + else: + print( + f"Syntax: {os.path.basename(sys.argv[0]).replace('.py', '')} [-m|--market=MARKETNAME] [-d|--node=NODE] [" + f"-s|--startdate=STARTDATETIME] [-e|--enddate=ENDDATETIME] [-h|--help|help]", file=sys.stderr + ) + exit(code) + +def get_credentials(market): + """ + Get the username and password from the local credentials.json file + :return: dict - Dictionary with username and password + """ + try: + with open(credentials_path) as credentials_file: + credentials_dict = json.load(credentials_file) + user = credentials_dict[market]["username"] + pwd = credentials_dict[market]["password"] + except Exception as err: + error(f"unable to load credentials for {market} from {credentials_path} ({msg})",code=1) + return {"user": user, "pwd": pwd} + + +def convert_timezone(market, date_str): + """ + Convert timestamp into timezone required by API. + Assumes that user input timestamp in local time (ex. PST for CAISO) + :param market: str - Market of pulled data ('caiso' or 'isone') + :param date_str: str - Date in format entered by user (YYYYMMDD) + :return: str - Date in required timezone (YYYYMMDD-HHmm) + """ + naive_dt = dt.datetime.strptime(date_str, "%Y%m%d") + + # CAISO API uses UTC time and requires an hour/minute stamp + if market == "caiso": + local_tz = pytz.timezone("US/Pacific") + local_dt = local_tz.localize( + naive_dt, is_dst=None + ) # Setting to none will raise errors for ambiguous or non-existent times + converted_dt = local_dt.astimezone(pytz.utc) + + # ISONE API uses local time and takes only date (not time) + elif market == "isone": + converted_dt = naive_dt + + return converted_dt + + +def format_date(market, date_dt): + """ + Reformat the date string into the format required by the API. + :param market: str - Market of pulled data + :param date_dt: datetime - Date in format returned by convert_timezone function + :return: str - Date in format required by the API + """ + if market == "caiso": + formatted_str = date_dt.strftime("%Y%m%dT%H:%M-0000") + elif market == "isone": + formatted_str = date_dt.strftime("%Y%m%d") + return formatted_str + + +def get_dates(market,start_date_str, end_date_str, max_interval): + """ + Returns a list of dicts, where each dict has 2 keys: 'start_date_str' and 'end_date_str'. The difference between + the two is the max interval of dates allowed in an API query (ex. 31 days). Each dict in the list should be used + to formulate a separate query to the API. + :param start_date_str: str - Date in format entered by user (YYYYMMDD) + :param end_date_str: str - Date in format entered by user (YYYYMMDD) + :param max_interval: int - Max number of days the API allows in a single query + :return: dates_list: list - List of dicts with start and end dates + """ + dates_list = [] + start_date_dt = dt.datetime.strptime(start_date_str, "%Y%m%d") + end_date_dt = dt.datetime.strptime(end_date_str, "%Y%m%d") + start_date_temp_dt = start_date_dt + + while (end_date_dt - start_date_temp_dt).days > 0: + end_date_temp_dt = start_date_temp_dt + dt.timedelta(days=max_interval) + if end_date_dt < end_date_temp_dt: + end_date_temp_dt = end_date_dt + start_date_temp_str = format_date( + market, convert_timezone(market, start_date_temp_dt.strftime("%Y%m%d")) + ) + end_date_temp_str = format_date( + market, convert_timezone(market, end_date_temp_dt.strftime("%Y%m%d")) + ) + dates_list.append( + {"start_date_str": start_date_temp_str, "end_date_str": end_date_temp_str} + ) + + start_date_temp_dt = end_date_temp_dt + + return dates_list + + +def extract_zip(url): + """ + Get a zip file from a url and read it into a dataframe. + :param url: str - URL to query + :param folder: str - folder to save contents of zip file to + :return: df - dataframe of extracted contents from url + """ + # Download the file from the URL + zip_resp = urlopen(url) + zf = ZipFile(BytesIO(zip_resp.read())) + data = pd.read_csv(zf.open(ZipFile.namelist(zf)[0])) + + # Wait + time.sleep(3) + + return data + + +def check_missing_data(datetime_col, start_datetime, end_datetime, freq): + """ + Check whether there are any missing timestamps in the pulled data. + :param datetime_col: pd.Series - column of datetimes from pulled data + :start_datetime: pd.datetime - starting datetime of range + :end_datetime: pd.datetime - ending datetime of range + :freq: str - frequency of data (5T = 5 min, H = hourly) + """ + missing_timestamps = (pd.date_range(start_datetime, end_datetime, freq=freq).difference(datetime_col)) + if len(missing_timestamps) > 0: + error(f'Missing timestamps {missing_timestamps} in data', 1) + + +def clean_market_data(market, lmp_df, demand_df): + """ + Merge the LMP and demand dataframes into a cleaned, single dataframe + :param market: str - Market of pulled data + :param lmp_df: dataframe - LMP df directly from API query + :param demand_df: dataframe - Demand df directly from API query + :return: + """ + if market == "caiso": + + # Only consider LMP data + if not "XML_DATA_ITEM" in lmp_df.columns: + data = ''.join(lmp_df.columns) + ''.join(lmp_df.iloc[:,0].values) + try: + import xml.etree.ElementTree as xt + xml = xt.fromstring(data) + except: + xml = None + pass + if not xml: + error(f"error data not formatted as expected ({data}",code=3) + else: + err_desc = [x for x in xml.iter('{http://www.caiso.com/soa/OASISReport_v1.xsd}ERR_DESC')] + err_code = [x for x in xml.iter('{http://www.caiso.com/soa/OASISReport_v1.xsd}ERR_CODE')] + error(f"OASIS report '{err_desc[0].text}' (code {err_code[0].text})",code=3) + + lmp_df = lmp_df[lmp_df["XML_DATA_ITEM"] == "LMP_PRC"].reset_index(drop=True) + + # Only consider CAISO-wide demand + demand_df = demand_df[demand_df["TAC_AREA_NAME"] == "CA ISO-TAC"].reset_index(drop=True) + + # Convert timestamp to local timezone and check for missing timestamps + lmp_df["INTERVALSTARTTIME_GMT"] = pd.to_datetime( + lmp_df["INTERVALSTARTTIME_GMT"] + ) + lmp_df["START_TIME_LOCAL"] = lmp_df["INTERVALSTARTTIME_GMT"].dt.tz_convert( + "US/Pacific" + ) + check_missing_data(lmp_df['START_TIME_LOCAL'], lmp_df['START_TIME_LOCAL'].iloc[0], lmp_df['START_TIME_LOCAL'].iloc[-1], '5T') + + demand_df["INTERVALSTARTTIME_GMT"] = pd.to_datetime( + demand_df["INTERVALSTARTTIME_GMT"] + ) + demand_df["START_TIME_LOCAL"] = demand_df["INTERVALSTARTTIME_GMT"].dt.tz_convert( + "US/Pacific" + ) + check_missing_data(demand_df['START_TIME_LOCAL'], demand_df['START_TIME_LOCAL'].iloc[0], demand_df['START_TIME_LOCAL'].iloc[-1], 'H') + + # Drop unnecessary columns + lmp_df = lmp_df.rename(columns={"VALUE": "LMP"}) + lmp_df = lmp_df[["START_TIME_LOCAL", "LMP"]] + + demand_df = demand_df[["START_TIME_LOCAL", "MW"]] + + # Merge lmp and demand dfs + market_data_df = pd.merge(lmp_df, demand_df, how="outer", on="START_TIME_LOCAL") + market_data_df = market_data_df.set_index("START_TIME_LOCAL").sort_index() + + # Front fill data + market_data_df = market_data_df.ffill() + + elif market == "isone": + + # Convert timestamp to local timezone and check for missing timestamps + lmp_df["START_TIME_LOCAL"] = pd.to_datetime(lmp_df["BeginDate"]) + check_missing_data(lmp_df['START_TIME_LOCAL'], lmp_df['START_TIME_LOCAL'].iloc[0], lmp_df['START_TIME_LOCAL'].iloc[-1], '5T') + + demand_df["START_TIME_LOCAL"] = pd.to_datetime(demand_df["BeginDate"]) + check_missing_data(demand_df['START_TIME_LOCAL'], demand_df['START_TIME_LOCAL'].iloc[0], demand_df['START_TIME_LOCAL'].iloc[-1], '5T') + + # Drop unnecessary columns + lmp_df = lmp_df.rename(columns={"LmpTotal": "LMP"}) + lmp_df = lmp_df[["START_TIME_LOCAL", "LMP"]] + + demand_df = demand_df.rename(columns={"LoadMw": "MW"}) + demand_df = demand_df[["START_TIME_LOCAL", "MW"]] + + # Merge lmp and demand dfs + market_data_df = pd.merge(lmp_df, demand_df, how="outer", on="START_TIME_LOCAL") + market_data_df = market_data_df.set_index("START_TIME_LOCAL").sort_index() + + return market_data_df + + +def get_caiso_data(node, start_date, end_date): + """ + Extract CAISO LMP and demand data for the given node and date range. + :param node: str - CAISO node as input by the user + :param start_date: str - Start date of data to pull + :param end_date: str - End date of data to pull + :return: dict - Keys are the names of the dfs, values are the dfs (lmp_df, demand_df) + """ + verbose(f"Pulling CAISO data from {start_date} to {end_date}") + + # Formulate components of the URL request + base_url = "http://oasis.caiso.com/oasisapi/SingleZip" + result_format = "6" + + # If start and end date are >31 days apart, break them up (CAISO API requirement) + lmp_df_list = [] + demand_df_list = [] + dates_list = get_dates("caiso", + start_date_str=start_date, end_date_str=end_date, max_interval=31 + ) + + for dates_dict in dates_list: + temp_start_date = dates_dict["start_date_str"] + temp_end_date = dates_dict["end_date_str"] + + # Get LMP data + query_name = "PRC_INTVL_LMP" + market_run_id = "RTM" + version = "3" + lmp_url = f"{base_url}?resultformat={result_format}&queryname={query_name}&startdatetime={temp_start_date}&enddatetime={temp_end_date}&version={version}&market_run_id={market_run_id}&node={node}" + lmp_df = extract_zip(url=lmp_url) + lmp_df_list.append(lmp_df) + + # Get demand data + query_name = "SLD_FCST" + market_run_id = "ACTUAL" + version = "1" + demand_url = f"{base_url}?resultformat={result_format}&queryname={query_name}&startdatetime={temp_start_date}&enddatetime={temp_end_date}&version={version}&market_run_id={market_run_id}" + demand_df = extract_zip(url=demand_url) + demand_df_list.append(demand_df) + + market_data_df = clean_market_data( + market="caiso", + lmp_df=pd.concat(lmp_df_list), + demand_df=pd.concat(demand_df_list), + ) + return market_data_df + + +def get_isone_data(node, start_date, end_date): + """ + Extract ISO-NE LMP and demand data for the given node and date range. + :param node: str - CAISO node as input by the user + :param start_date: str - Start date of data to pull + :param end_date: str - End date of data to pull + """ + verbose(f"Pulling ISONE data from {start_date} to {end_date}") + credentials_dict = get_credentials("isone") + + # Formulate components of the URL request + base_url = "https://webservices.iso-ne.com/api/v1.1" + file_type = ".json" + + # Loop through all dates in range, one day at a time (ISONE API requirement) + lmp_df_list = [] + demand_df_list = [] + dates_list = get_dates("isone", + start_date_str=start_date, end_date_str=end_date, max_interval=1 + ) + + for dates_dict in dates_list: + temp_start_date = dates_dict["start_date_str"] + + # Get LMP data + endpoint = f"/fiveminutelmp/day/{temp_start_date}/location/{node}" + response = requests.get( + base_url + endpoint + file_type, + auth=HTTPBasicAuth(credentials_dict["user"], credentials_dict["pwd"]), + ) + response_dict = response.json()["FiveMinLmps"] + lmp_df = pd.concat( + [pd.DataFrame(v) for k, v in response_dict.items()], keys=response_dict + ) + lmp_df_list.append(lmp_df) + + # Get 5 min RT demand data + endpoint = f"/fiveminutesystemload/day/{temp_start_date}" + response = requests.get( + base_url + endpoint + file_type, + auth=HTTPBasicAuth(credentials_dict["user"], credentials_dict["pwd"]), + ) + response_dict = response.json()["FiveMinSystemLoads"] + demand_df = pd.concat( + [pd.DataFrame(v) for k, v in response_dict.items()], keys=response_dict + ) + demand_df_list.append(demand_df) + + market_data_df = clean_market_data( + market="isone", + lmp_df=pd.concat(lmp_df_list), + demand_df=pd.concat(demand_df_list), + ) + return market_data_df + + +def get_market_data(market, node, start_date, end_date): + """ + Get market data (LMP and demand data) for the given market, node, and date range. + :param market: str - Market of pulled data + :param node: str - Node as input by the user + :param start_date: str - Start date of data to pull + :param end_date: str - End date of data to pull + :return: dataframe - df of complete market data + """ + if market == "caiso": + market_data_df = get_caiso_data( + node=node, start_date=start_date, end_date=end_date + ) + elif market == "isone": + market_data_df = get_isone_data( + node=node, start_date=start_date, end_date=end_date + ) + return market_data_df + + +def writeglm( + market, node, start_date, end_date, market_data, csv=None, glm=None, name=None): + """Write market data object. + name is name of obj in gld + Default GLM and CSV values are handled as follows + GLM CSV Output + ------ ------ ------ + None None CSV->stdout + GLM None GLM, CSV->GLM/.glm/.csv + None CSV GLM->stdout, CSV + GLM CSV GLM, CSV + The default name is "market_data@__-" + """ + float_format = "%.1f" + date_format = "%Y-%m-%d %H:%M:%S" + if not csv: + csv = "/dev/stdout" + if not glm: + glm = "/dev/null" + if not name: + name = f"market_data@{market}_{node}_{start_date}-{end_date}" + if csv == "/dev/stdout" and glm != "/dev/null": + error("cannot create GLM when CSV is not a file",code=1) + + with open(glm, "w") as f: + f.write(f"class market_data\n{{\n") + f.write("\tchar32 market;\n"); + f.write("\tchar256 node;\n"); + for column in market_data.columns: + dtype = market_data[column].dtype + if dtype == object: + gld_dtype = "char1024" + elif dtype == int: + gld_dtype = "int64" + elif dtype == float: + gld_dtype = "double" + elif is_datetime(market_data[column]): + gld_dtype = "timestamp" + else: + error( + f"Datatype <{dtype}> in column <{column}> does not have a corresponding datatype for GLD." + ) + f.write(f"\t{gld_dtype} {column};\n") + f.write("}\n") + market_data.columns = list(map(lambda x: x.split("[")[0], market_data.columns)) + f.write("module tape;\n") + f.write(f"#ifdef MARKET_DATA\n") + f.write( + f"#set MARKET_DATA=$MARKET_DATA {name}\n" + ) # add curly braces around MARKET_DATA + f.write("#else\n") + f.write(f"#define MARKET_DATA={name}\n") + f.write("#endif\n") + f.write(f"object market_data\n{{\n") + f.write(f'\tname "{name}";\n') + f.write(f"\tmarket {market};\n") + f.write(f"\tnode {node};\n") + f.write("\tobject player\n\t{\n") + f.write(f'\t\tfile "{csv}";\n') + f.write(f"\t\tproperty \"{','.join(market_data.columns)}\";\n") + f.write("\t};\n") + f.write("}\n") + + market_data.to_csv(csv, float_format=float_format, date_format=date_format,header=(glm=="/dev/null")) + + return dict(glm=glm, csv=csv, name=name) + +if __name__ == "__main__": + main(sys.argv) diff --git a/market_model.py b/market_model.py new file mode 100644 index 000000000..ba3c0adae --- /dev/null +++ b/market_model.py @@ -0,0 +1,530 @@ +"""Create market model from ISO data + +SYNTAX + + $ python market_model.py [-f|--file=DATACSV] [-i|--input=INPUTCOL(S)] [-o|--output=OUTPUTCOL] [ + -l|--lookahead=LOOKAHEAD] [-k|--order] [-g|--glm=GLMNAME] [-I|--init=ISODATETIME] [-M|--module=MODULENAME] [ + -C|--class=CLASSNAME] [-N|--name=OBJNAME] [-P|--player[=PLAYERCSV]] [-R|--recorder=RECORDERCSV] [--{by,with}_{hour, + day,month,year}] [-v|--verbose] [-w|--warning] [-q|--quiet] [-d|--debug] [-h|--help|help] + +DESCRIPTION + +The `market_model` utility creates NxM filters from N input and M output +channels of data by fitting a Kth-order discrete-time transfer functions to +the data using a least-squares linear regressions of the equation: + + Y0 = -a1*Y1 - a2*Y2 - ... - aK*Yk + b0*U0 + b1*U1 + ... + bK*Uk + +where + + Y0 is the output channel 0 at time t + Y1 is the output channel 0 at time t-1 + ... + YK is the output channel 0 at time t-K + + U0 is the input channel 0 at time t + U1 is the output channel 0 at time t-1 + ... + UK is the input channel 0 at time t-K + + a and b are the coefficients of the transfer functions + +If a GLMNAME and CLASSNAME are specified, then an object of that class will be +created with the filter as inputs and object properties as outputs. If the +module is not specified, the class is defined as a runtime class with all +inputs and outputs specified as doubles. If OBJNAME is specified the object is +given that name, otherwise a random object name is generated and appended to the +OBJNAME global variable. + +If PLAYERCSV is specified, then any created object will have the inputs +assigned from that file. If RECORDERCSV is specified, then any object created will +have the outputs recorded to that file. + +If --with_{hour,day,month,year} is specified, then an hour, day, month, or +year column is added to the input data. If --by_{hour,day,month,year} is +specified, then the input data is grouped by hour, day, month, or year. If +--with_time is specified, then the elapsed time in the specified units is +included in a column. + +CONFIGURATION + +By default all the columns of the input and output files are used in the +filter model identification. Only columns listed in the `converters` class will be used to generate filters. +The class `converters` must be defined, and contain the properties `cols`. These must be +dictionaries where the keys are the column names, and values of the +converters to be used for those columns. + +For the example below, the following converter files can be used: + + from datetime import datetime + class converters: + def real(x): + try: + return float(x) + except: + return float('NAN') + + def datetime(x): + return datetime.strptime(x, "%Y-%m-%d %H:%M:%S") + + cols = {"START_TIME_LOCAL": datetime, 'MW': real, 'LMP': real} + +Note that using a converter often speeds up the CSV read process because the +date/time format is specified and the loader does not need to deduce the +format automatically, which can take significant additional time. + +To limit which columns of the input or output files are used, it is sufficient +to omit them from the converter list. For example, + + cols = {"START_TIME_LOCAL":datetime, "MW":real} + +omits the LMP data in the input file. + +EXAMPLE + + $ python market_model -f=caiso_20220101-20220201.csv -i=LMP -o=MW -k=24 -l=3 + +""" + +import os, sys +import numpy as np +import pandas as pd +from datetime import datetime, timedelta +import random + +np.set_printoptions( + edgeitems=12, linewidth=1000, formatter={"float_kind": (lambda x: f"{x:7.4g}")} +) + + +def look_ahead(M, Y, K, l): + """Modify the Y and M matrices to forecast for 'l' days ahead.""" + Y = Y[:-K] + for n in range(l - 1): + Y = np.insert(Y, 0, M[:, n].T, axis=1) + M = M[:, l - 1 :] + return Y, M + + +def identify(Y, U, K=24, l=0): + """ + Fit a discrete time transfer function of the form + Y(z) = -a1*Y(z^-1) - a2*Y(z^-2) - ... - aK*Y(z^-K) + b0*U(z) + b1*U(z-1) + ... + bK*U(z^-k) + to known value of U and Y. + + ARGUMENTS: + + Y (matrix) is the output (Lx1) + U (matrix) are the inputs (LxKxM) where M is the number of input variables + K (int) is the desired order of the transfer function + """ + length = len(Y) + if length < K: + warning("There is insufficient data for the specified K value.") + + M = np.hstack( + [ + np.hstack([Y[n : length - K + n] for n in range(1, K + 1)]), + np.hstack([U[n : length - K + n] for n in range(K + 1)]), + ] + ) + + Y, M = look_ahead(M, Y, K, l) + x = np.linalg.lstsq(M, Y)[0] + + if debug_enabled: + print(np.hstack([M, Y[K + 1 :]]), file=sys.stderr) + print(x.transpose(), file=sys.stderr) + + return x, Y, M + + +def plot_results(Y, U, K): + """Plot the predicted vs actual results when model is trained on first 75% of data, and tested on last 25%. Use a + lookahead value of 3.""" + l = 1 + + # Split into train and test + train_size = int(len(Y) * 0.75) + Y_train = Y[:train_size] + Y_test = Y[train_size:] + U_train = np.hstack(U)[:train_size, :] + U_test = np.hstack(U)[train_size:, :] + + # Calculate x vector and M for train set + x_train, Y_train, M_train = identify(Y_train, U_train, K, l) + + # Calculate x vector and M for test set + x_test, Y_test, M_test = identify(Y_test, U_test, K, l) + + # Use the train x vector on the test M to calculate predicted y + Y_pred = np.matmul(M_test, x_train) + Y_pred_df = pd.DataFrame( + Y_pred, columns=[f"{n}hr_ahead_pred" for n in range(1, l + 1)] + ) + Y_test_df = pd.DataFrame( + Y_test, columns=[f"{n}hr_ahead_actual" for n in range(1, l + 1)] + ) + + # Plot + colors = [ + "tab:blue", + "tab:orange", + "tab:green", + "tab:blue", + "tab:orange", + "tab:green", + ] + styles = ["-", "-", "-", ":", ":", ":"] + Y_test_df.join(Y_pred_df).plot(color=colors, style=styles) + + +if __name__ == "__main__": + + basename = os.path.basename(sys.argv[0]).replace(".py", "") + + K = 24 + inputs = None + outputs = None + glmname = "/dev/stdout" + modulename = None + classname = None + objname = None + playername = None + recordername = None + lookahead = 1 + verbose_enabled = False + warning_enabled = True + debug_enabled = False + quiet_enabled = False + init_time = None + constraint = None + resolution = None + with_hour = False + with_day = False + with_weekend = False + with_month = False + with_year = False + by_hour = False + by_day = False + by_month = False + by_year = False + + # exit codes + E_OK = 0 # no error + E_INPUT = 1 # input file error + E_OUTPUT = 2 # output file error + E_INVALID = 3 # invalid arguments + E_CONFIG = 4 # invalid configuration + E_OPTION = 5 # invalid options + + def error(msg, code=None): + if not quiet_enabled: + print(f"ERROR [{basename}]: {msg}", file=sys.stderr) + if not debug_enabled and code != None: + exit(code) + elif debug_enabled: + raise + + def syntax(code=0): + if code == 0: + print(__doc__, file=sys.stderr) + else: + print( + f"Syntax: {basename} [-f | --file = DATACSV] [-i | --input = INPUTCOL(S)] [-o | --output = OUTPUTCOL] " + f"[-l | --lookahead = LOOKAHEAD] [-k | --order] [-g | --glm = GLMNAME] [-I | --init = ISODATETIME] [" + f"-M | --module = MODULENAME] [-C | --class =CLASSNAME][-N | --name=OBJNAME] [-P | --player[" + f"=PLAYERCSV]][-R | --recorder=RECORDERCSV] [--{{by, with}}_{{hour, day, month, year}}] [-v | " + f"--verbose][-w | --warning][-q | --quiet][-d | --debug] [-h | --help | help]" + ) + exit(code) + + def verbose(msg): + if verbose_enabled: + print(f"VERBOSE [{basename}]: {msg}", file=sys.stderr) + + def warning(msg): + if warning_enabled: + print(f"WARNINGS [{basename}]: {msg}", file=sys.stderr) + + class converters: + def real(x): + try: + return float(x) + except: + return float("NAN") + + def datetime(x): + return datetime.strptime(x, "%Y-%m-%d %H:%M:%S") + + cols = {"START_TIME_LOCAL": datetime, "MW": real, "LMP": real} + + for arg in sys.argv[1:]: + args = arg.split("=") + if type(args) is list and len(args) > 1: + token = args[0] + value = "=".join(args[1:]) + elif type(args) is list: + token = args[0] + value = None + else: + token = args + value = None + if token in ["-h", "--help", "help"]: + syntax() + elif token in ["-B", "--bits"]: + resolution = int(value) + elif token in ["-C", "--class"]: + classname = value + elif token in ["-f", "--file"]: + data_file = value + elif token in ["-d", "--debug"]: + debug_enabled = not debug_enabled + elif token in ["-i", "--input"]: + inputs = value.split(",") + elif token in ["-I", "--init"]: + init_time = datetime.fromisoformat(value) + elif token in ["-g", "--glm"]: + glmname = value + elif token in ["-k", "--order"]: + K = int(value) + elif token in ["-l", "--lookahead"]: + lookahead = int(value) + elif token in ["-M", "--module"]: + modulename = value + elif token in ["-N", "--name"]: + objname = value + elif token in ["-o", "--output"]: + outputs = value.split(",") + elif token in ["-P", "--player"]: + playername = value + elif token in ["-q", "--quiet"]: + quiet_enabled = not quiet_enabled + elif token in ["-R", "--recorder"]: + recordername = value + elif token in ["-t", "--type"]: + modeltype = value + elif token in ["-v", "--verbose"]: + verbose_enabled = not verbose_enabled + elif token in ["-w", "--warning"]: + warning_enabled = not warning_enabled + elif token in ["--stdev"]: + constraint = int(value) + elif token in ["--with_hour"]: + with_hour = True + elif token in ["--with_day"]: + with_day = True + elif token in ["--with_weekend"]: + with_weekend = True + elif token in ["--with_month"]: + with_month = True + elif token in ["--with_year"]: + with_year = True + elif token in ["--by_hour"]: + by_hour = True + elif token in ["--by_weekday"]: + by_day = True + elif token in ["--by_month"]: + by_month = True + elif token in ["--by_year"]: + by_year = True + else: + error(f"option '{token}' is not valid", E_OPTION) + + # processing requirements met + if inputs and outputs: + + # set output + if glmname != "/dev/stdout": + if not glmname.endswith(".glm"): + error( + f"output file '{glmname}' is not a supported file format", E_INVALID + ) + try: + sys.stdout = open(glmname, "w") + except Exception as err: + error(err, E_OUTPUT) + + # load data + try: + original_data = pd.read_csv( + data_file, usecols=converters.cols.keys(), converters=converters.cols + ) + outputs.insert(0, "START_TIME_LOCAL") + output_data = original_data[outputs] + output_names = output_data.columns[1:] + ndx = list( + map( + lambda t: datetime(t.year, t.month, t.day, t.hour), + pd.DatetimeIndex(output_data[output_data.columns[0]]), + ) + ) + data = pd.DataFrame(output_data.groupby(ndx)[output_data.columns[1]].mean()) + verbose(data) + + inputs.insert(0, "START_TIME_LOCAL") + input_data = original_data[inputs] + col0 = input_data.columns[0] + if with_day: + weekdays = list(map(lambda x: x.weekday(), input_data[col0])) + for weekday in set(weekdays): + input_data[f"weekday{weekday}"] = list( + map(lambda x: int(x == weekday), weekdays) + ) + if with_weekend: + weekdays = list(map(lambda x: x.weekday(), input_data[col0])) + input_data["weekday"] = list(map(lambda x: int(x < 5), weekdays)) + if with_hour: + hours = list(map(lambda x: x.hour, input_data[col0])) + for hour in set(hours): + input_data[f"hour{hour}"] = list( + map(lambda x: int(x == hour), hours) + ) + if with_month: + months = list(map(lambda x: x.month, input_data[col0])) + for month in set(months): + input_data[f"month{month}"] = list( + map(lambda x: int(x == month), months) + ) + input_names = input_data.columns[1:] + ndx = list( + map( + lambda t: datetime(t.year, t.month, t.day, t.hour), + pd.DatetimeIndex(input_data[input_data.columns[0]]), + ) + ) + data = input_data.groupby(ndx).mean().join(data).dropna() + data = data.sort_index(ascending=False) + verbose(data) + + except Exception as err: + + print(f"#error {basename} {err}") + error(err, E_INPUT) + + if init_time: + back_time = init_time - timedelta(hours=K + 1) + print( + f"\n// initial input and state vector from {back_time} to {init_time}" + ) + U0 = data[input_names][back_time : init_time - timedelta(hours=1)].to_dict( + "list" + ) + for name in input_names: + print( + f"#define U0_{name}={','.join(list(map(lambda z: str(z), U0[name])))}" + ) + print(f"#define T0_{name}={data.index[0]}") + print(f"#define TN_{name}={data.index[-1]}") + X0 = data[output_names][back_time : init_time - timedelta(hours=1)].to_dict( + "list" + ) + for name in output_names: + print( + f"#define X0_{name}={','.join(list(map(lambda z: str(z), X0[name])))}" + ) + print(f"#define T0_{name}={data.index[0]}") + print(f"#define TN_{name}={data.index[-1]}") + + for output_name in output_names: + + # perform model fit + try: + + Y = np.matrix(data[output_name]).transpose() + U = [] + for input_name in input_names: + U.append(np.matrix(data[input_name]).transpose()) + plot_results(Y, U, K) + + x, Y, M = identify(Y, np.hstack(U), K, lookahead) + # pd.DataFrame(x).to_csv('x.csv') + # pd.DataFrame(Y).to_csv('Y.csv') + # pd.DataFrame(M).to_csv('M.csv') + + except Exception as err: + + print(f"#error {basename} {err}") + error(err, E_INVALID) + + if debug_enabled: + print("") + print(f"// input = {M}".replace("\n", "\n// ")) + print(f"// output = {Y[K + 1:]}".replace("\n", "\n// ")) + print( + f"\n// model = [{','.join(list(map(lambda z: str(z), x.transpose().round(2).tolist()[0])))}]" + ) + + opts = ["0"] + if resolution: + opts.append(f"resolution={resolution}") + if constraint: + mean = data[output_name].mean() + std = data[output_name].std() + opts.append( + f"minimum={mean - constraint * std},maximum={mean + constraint * std}" + ) + + n_inputs = len(data.columns) - 1 + for n in range(n_inputs): + # TODO: Review formulas + print(f"\n// {data.columns[n]} --> {output_name}") + print( + f"filter {output_name}_{data.columns[n]}(z,1h,{','.join(opts)}) = (", + end="", + ) + for k in range(0, K + 1): + print(f"{x[-k * n_inputs - 1, 0]:+f}z^{K - k:.0f}", end="") + print(f"{x[K + n, 0]:+f} ) / (z^{K:.0f}", end="") + for k in range(1, K): + print(f"{-x[K - k, 0]:+f}z^{K - k:.0f}", end="") + print(f"{-x[0, 0]:+f})", end=";\n") + + if playername: + print("module tape;") + print("class", "input", "{") + for name in input_names: + print(" ", "double", name, end=";\n") + print("}") + input_object = f"input_{random.randint(1e15, 1e16):x}" + print("#define", f"INPUTNAME={input_object}") + print("object input {") + print(" ", "name", input_object, end=";\n") + print(" ", "object", "player", "{") + print(" ", " ", "file", f'"{playername}"', end=";\n") + print(" ", "}", end=";\n") + print("}") + + if classname: + if modulename: + print("module", modulename, end=";\n") + print("class", classname, "{") + for name in output_names: + print(" ", "double", name, end=";\n") + print("}") + + if not objname: + objname = f"{classname}_{random.randint(1e15, 1e16):x}" + + print("#define", f"OUTPUTNAME={objname}") + print("object", classname, "{") + print(" ", "name", f"{objname}", end=";\n") + for input_name in input_names: + print( + " ", + output_name, + f"{output_name}_{input_name}({input_object}:{input_name},:U0_{input_name},:X0_{output_name})", + end=";\n", + ) + if recordername: + print(" ", "object", "recorder", "{") + print(" ", " ", "file", recordername, end=";\n") + print( + " ", " ", "property", ",".join(output_names), end=";\n" + ) + print(" ", " ", "interval", -1, end=";\n") + print(" ", "}", end=";\n") + print("}") + + else: + syntax(1) diff --git a/python_extras/example/.gitignore b/python_extras/example/.gitignore new file mode 100644 index 000000000..b3f248c87 --- /dev/null +++ b/python_extras/example/.gitignore @@ -0,0 +1,2 @@ +caiso_20220222-20220223.csv +caiso_20220101-20220201.csv diff --git a/tools/Makefile.mk b/tools/Makefile.mk index e3f7f037e..aa51d3c9d 100644 --- a/tools/Makefile.mk +++ b/tools/Makefile.mk @@ -1,3 +1,4 @@ +dist_pkgdata_DATA += tools/create_filter.py dist_pkgdata_DATA += tools/create_player.py dist_pkgdata_DATA += tools/create_poles.py dist_pkgdata_DATA += tools/eia_recs.py @@ -6,6 +7,7 @@ dist_pkgdata_DATA += tools/gridlabd-editor.png dist_pkgdata_DATA += tools/gridlabd-editor.py dist_pkgdata_DATA += tools/insights.py dist_pkgdata_DATA += tools/market_data.py +dist_pkgdata_DATA += tools/market_model.py dist_pkgdata_DATA += tools/metar2glm.py dist_pkgdata_DATA += tools/noaa_forecast.py dist_pkgdata_DATA += tools/nsrdb_weather.py diff --git a/tools/autotest/test_create_filter.glm b/tools/autotest/test_create_filter.glm new file mode 100644 index 000000000..0842b1b80 --- /dev/null +++ b/tools/autotest/test_create_filter.glm @@ -0,0 +1,6 @@ + +#python -m create_filter test 1 1,0.2 -c -r=8 -m=0 -M=1 -c > test_create_filter_opt.glm + +#ifexist ../test_create_filter_opt.glm +#on_exit 0 diff -I '^[#/].*' ../test_create_filter_opt.glm test_create_filter_opt.glm > gridlabd.diff +#endif diff --git a/tools/autotest/test_create_filter_opt.glm b/tools/autotest/test_create_filter_opt.glm new file mode 100644 index 000000000..70bd132c4 --- /dev/null +++ b/tools/autotest/test_create_filter_opt.glm @@ -0,0 +1,2 @@ +// generated by `/usr/local/opt/gridlabd/4.2.31-220308-develop_add_market_model/share/gridlabd/create_filter.py test 1 1,0.2 -c -r=8 -m=0 -M=1 -c` +filter test(z,1,0,resolution=8,minimum=0.0,maximum=1.0) = (+1-0.818731z) / (+0.906346); diff --git a/tools/autotest/test_market_model.csv b/tools/autotest/test_market_model.csv new file mode 100644 index 000000000..9c45964a9 --- /dev/null +++ b/tools/autotest/test_market_model.csv @@ -0,0 +1,8929 @@ +START_TIME_LOCAL,LMP,MW +2022-01-01 00:00:00,61.1,22128.0 +2022-01-01 00:05:00,63.2,22128.0 +2022-01-01 00:10:00,63.2,22128.0 +2022-01-01 00:15:00,63.1,22128.0 +2022-01-01 00:20:00,62.4,22128.0 +2022-01-01 00:25:00,61.8,22128.0 +2022-01-01 00:30:00,61.5,22128.0 +2022-01-01 00:35:00,61.2,22128.0 +2022-01-01 00:40:00,60.7,22128.0 +2022-01-01 00:45:00,59.7,22128.0 +2022-01-01 00:50:00,59.7,22128.0 +2022-01-01 00:55:00,59.5,22128.0 +2022-01-01 01:00:00,58.2,21394.0 +2022-01-01 01:05:00,58.2,21394.0 +2022-01-01 01:10:00,59.1,21394.0 +2022-01-01 01:15:00,59.0,21394.0 +2022-01-01 01:20:00,58.8,21394.0 +2022-01-01 01:25:00,58.8,21394.0 +2022-01-01 01:30:00,58.6,21394.0 +2022-01-01 01:35:00,58.7,21394.0 +2022-01-01 01:40:00,58.2,21394.0 +2022-01-01 01:45:00,57.3,21394.0 +2022-01-01 01:50:00,58.0,21394.0 +2022-01-01 01:55:00,58.0,21394.0 +2022-01-01 02:00:00,61.2,21004.0 +2022-01-01 02:05:00,62.1,21004.0 +2022-01-01 02:10:00,61.2,21004.0 +2022-01-01 02:15:00,61.8,21004.0 +2022-01-01 02:20:00,61.8,21004.0 +2022-01-01 02:25:00,62.3,21004.0 +2022-01-01 02:30:00,62.1,21004.0 +2022-01-01 02:35:00,62.8,21004.0 +2022-01-01 02:40:00,62.7,21004.0 +2022-01-01 02:45:00,62.8,21004.0 +2022-01-01 02:50:00,62.2,21004.0 +2022-01-01 02:55:00,62.3,21004.0 +2022-01-01 03:00:00,61.4,20881.0 +2022-01-01 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a/tools/autotest/test_market_model.glm b/tools/autotest/test_market_model.glm new file mode 100644 index 000000000..25e8c77bf --- /dev/null +++ b/tools/autotest/test_market_model.glm @@ -0,0 +1,9 @@ +#ifexist ../test_market_model.csv +#define DIR=.. +#endif + +#python -m market_model -f=${DIR:-.}/test_market_model.csv -i=LMP -o=MW -k=24 -l=3 -g=test_market_model_opt.glm + +#ifexist ../test_market_model_opt.glm +#on_exit 0 diff ../test_market_model_opt.glm test_market_model_opt.glm > gridlabd.diff +#endif diff --git a/tools/autotest/test_market_model_opt.glm b/tools/autotest/test_market_model_opt.glm new file mode 100644 index 000000000..4ab60b27e --- /dev/null +++ b/tools/autotest/test_market_model_opt.glm @@ -0,0 +1,3 @@ + +// LMP --> MW +filter MW_LMP(z,1h,0) = (-1.117597z^24-4.357008z^23-2.889171z^22+5.006946z^21-5.407557z^20+5.581075z^19+0.028384z^18-5.631783z^17-2.400548z^16+9.474545z^15+1.420422z^14-5.249877z^13+6.340522z^12-1.653639z^11-2.220970z^10+4.038458z^9-2.253462z^8+3.986717z^7-3.997662z^6+7.884185z^5-14.650178z^4+1.498585z^3+3.938674z^2+5.070250z^1+2.774227z^0+3.938674 ) / (z^24-5.070250z^23-2.774227z^22-0.088543z^21-0.068133z^20+0.201224z^19-0.308082z^18+0.231848z^17-0.069741z^16+0.060805z^15-0.207111z^14+0.438895z^13-0.520654z^12+0.362031z^11-0.093934z^10+0.044339z^9-0.232001z^8+0.361887z^7-0.310918z^6+0.126706z^5-0.068516z^4+0.282665z^3-0.812875z^2+1.584616z^1-1.905473); diff --git a/python_extras/create_filter.py b/tools/create_filter.py similarity index 100% rename from python_extras/create_filter.py rename to tools/create_filter.py diff --git a/python_extras/example/isone_data.glm b/tools/example/isone_data.glm similarity index 100% rename from python_extras/example/isone_data.glm rename to tools/example/isone_data.glm diff --git a/python_extras/example/load_model.glm b/tools/example/load_model.glm similarity index 100% rename from python_extras/example/load_model.glm rename to tools/example/load_model.glm diff --git a/python_extras/example/load_model_config.py b/tools/example/load_model_config.py similarity index 100% rename from python_extras/example/load_model_config.py rename to tools/example/load_model_config.py diff --git a/python_extras/example/nsrdb_weather.glm b/tools/example/nsrdb_weather.glm similarity index 100% rename from python_extras/example/nsrdb_weather.glm rename to tools/example/nsrdb_weather.glm diff --git a/tools/market_model.py b/tools/market_model.py new file mode 100644 index 000000000..121a985cf --- /dev/null +++ b/tools/market_model.py @@ -0,0 +1,524 @@ +"""Create market model from ISO data + +SYNTAX + + $ python market_model.py [-f|--file=DATACSV] [-i|--input=INPUTCOL(S)] [-o|--output=OUTPUTCOL] [ + -l|--lookahead=LOOKAHEAD] [-k|--order] [-g|--glm=GLMNAME] [-I|--init=ISODATETIME] [-M|--module=MODULENAME] [ + -C|--class=CLASSNAME] [-N|--name=OBJNAME] [-P|--player[=PLAYERCSV]] [-R|--recorder=RECORDERCSV] [--{by,with}_{hour, + day,month,year}] [-v|--verbose] [-w|--warning] [-q|--quiet] [-d|--debug] [-h|--help|help] + +DESCRIPTION + +The `market_model` utility creates NxM filters from N input and M output +channels of data by fitting a Kth-order discrete-time transfer functions to +the data using a least-squares linear regressions of the equation: + + Y0 = -a1*Y1 - a2*Y2 - ... - aK*Yk + b0*U0 + b1*U1 + ... + bK*Uk + +where + + Y0 is the output channel 0 at time t + Y1 is the output channel 0 at time t-1 + ... + YK is the output channel 0 at time t-K + + U0 is the input channel 0 at time t + U1 is the output channel 0 at time t-1 + ... + UK is the input channel 0 at time t-K + + a and b are the coefficients of the transfer functions + +If a GLMNAME and CLASSNAME are specified, then an object of that class will be +created with the filter as inputs and object properties as outputs. If the +module is not specified, the class is defined as a runtime class with all +inputs and outputs specified as doubles. If OBJNAME is specified the object is +given that name, otherwise a random object name is generated and appended to the +OBJNAME global variable. + +If PLAYERCSV is specified, then any created object will have the inputs +assigned from that file. If RECORDERCSV is specified, then any object created will +have the outputs recorded to that file. + +If --with_{hour,day,month,year} is specified, then an hour, day, month, or +year column is added to the input data. If --by_{hour,day,month,year} is +specified, then the input data is grouped by hour, day, month, or year. If +--with_time is specified, then the elapsed time in the specified units is +included in a column. + +CONFIGURATION + +By default all the columns of the input and output files are used in the +filter model identification. Only columns listed in the `converters` class will be used to generate filters. +The class `converters` must be defined, and contain the properties `cols`. These must be +dictionaries where the keys are the column names, and values of the +converters to be used for those columns. + +For the example below, the following converter files can be used: + + from datetime import datetime + class converters: + def real(x): + try: + return float(x) + except: + return float('NAN') + + def datetime(x): + return datetime.strptime(x, "%Y-%m-%d %H:%M:%S") + + cols = {"START_TIME_LOCAL": datetime, 'MW': real, 'LMP': real} + +Note that using a converter often speeds up the CSV read process because the +date/time format is specified and the loader does not need to deduce the +format automatically, which can take significant additional time. + +To limit which columns of the input or output files are used, it is sufficient +to omit them from the converter list. For example, + + cols = {"START_TIME_LOCAL":datetime, "MW":real} + +omits the LMP data in the input file. + +EXAMPLE + + $ python market_model -f=caiso_20220101-20220201.csv -i=LMP -o=MW -k=24 -l=3 + +""" + +import os, sys +import numpy as np +import pandas as pd +from datetime import datetime, timedelta +import random + +np.set_printoptions( + edgeitems=12, linewidth=1000, formatter={"float_kind": (lambda x: f"{x:7.4g}")} +) + + +def look_ahead(M, Y, K, l): + """Modify the Y and M matrices to forecast for 'l' days ahead.""" + Y = Y[:-K] + for n in range(l - 1): + Y = np.insert(Y, 0, M[:, n].T, axis=1) + M = M[:, l - 1 :] + return Y, M + + +def identify(Y, U, K=24, l=0): + """ + Fit a discrete time transfer function of the form + Y(z) = -a1*Y(z^-1) - a2*Y(z^-2) - ... - aK*Y(z^-K) + b0*U(z) + b1*U(z-1) + ... + bK*U(z^-k) + to known value of U and Y. + + ARGUMENTS: + + Y (matrix) is the output (Lx1) + U (matrix) are the inputs (LxKxM) where M is the number of input variables + K (int) is the desired order of the transfer function + """ + length = len(Y) + if length < K: + warning("There is insufficient data for the specified K value.") + + M = np.hstack( + [ + np.hstack([Y[n : length - K + n] for n in range(1, K + 1)]), + np.hstack([U[n : length - K + n] for n in range(K + 1)]), + ] + ) + + Y, M = look_ahead(M, Y, K, l) + x = np.linalg.lstsq(M, Y)[0] + + if debug_enabled: + print(np.hstack([M, Y[K + 1 :]]), file=sys.stderr) + print(x.transpose(), file=sys.stderr) + + return x, Y, M + + +def plot_results(Y, U, K): + """Plot the predicted vs actual results when model is trained on first 75% of data, and tested on last 25%. Use a + lookahead value of 3.""" + l = 3 + + # Split into train and test + train_size = int(len(Y) * 0.75) + Y_train = Y[:train_size] + Y_test = Y[train_size:] + U_train = np.hstack(U)[:train_size, :] + U_test = np.hstack(U)[train_size:, :] + + # Calculate x vector and M for train set + x_train, Y_train, M_train = identify(Y_train, U_train, K, l) + + # Calculate x vector and M for test set + x_test, Y_test, M_test = identify(Y_test, U_test, K, l) + + # Use the train x vector on the test M to calculate predicted y + Y_pred = np.matmul(M_test, x_train) + Y_pred_df = pd.DataFrame( + Y_pred, columns=[f"{n}hr_ahead_pred" for n in range(1, l + 1)] + ) + Y_test_df = pd.DataFrame( + Y_test, columns=[f"{n}hr_ahead_actual" for n in range(1, l + 1)] + ) + + # Plot + colors = [ + "tab:blue", + "tab:orange", + "tab:green", + "tab:blue", + "tab:orange", + "tab:green", + ] + styles = ["-", "-", "-", ":", ":", ":"] + Y_test_df.join(Y_pred_df).plot(color=colors, style=styles) + + +if __name__ == "__main__": + + basename = os.path.basename(sys.argv[0]).replace(".py", "") + + K = 24 + inputs = None + outputs = None + glmname = "/dev/stdout" + modulename = None + classname = None + objname = None + playername = None + recordername = None + verbose_enabled = False + warning_enabled = True + debug_enabled = False + quiet_enabled = False + init_time = None + constraint = None + resolution = None + with_hour = False + with_day = False + with_weekend = False + with_month = False + with_year = False + by_hour = False + by_day = False + by_month = False + by_year = False + + # exit codes + E_OK = 0 # no error + E_INPUT = 1 # input file error + E_OUTPUT = 2 # output file error + E_INVALID = 3 # invalid arguments + E_CONFIG = 4 # invalid configuration + E_OPTION = 5 # invalid options + + def error(msg, code=None): + if not quiet_enabled: + print(f"ERROR [{basename}]: {msg}", file=sys.stderr) + if not debug_enabled and code != None: + exit(code) + elif debug_enabled: + raise + + def syntax(code=0): + if code == 0: + print(__doc__, file=sys.stderr) + else: + print( + f"Syntax: {basename} [-f | --file = DATACSV] [-i | --input = INPUTCOL(S)] [-o | --output = OUTPUTCOL] " + f"[-l | --lookahead = LOOKAHEAD] [-k | --order] [-g | --glm = GLMNAME] [-I | --init = ISODATETIME] [" + f"-M | --module = MODULENAME] [-C | --class =CLASSNAME][-N | --name=OBJNAME] [-P | --player[" + f"=PLAYERCSV]][-R | --recorder=RECORDERCSV] [--{{by, with}}_{{hour, day, month, year}}] [-v | " + f"--verbose][-w | --warning][-q | --quiet][-d | --debug] [-h | --help | help]" + ) + exit(code) + + def verbose(msg): + if verbose_enabled: + print(f"VERBOSE [{basename}]: {msg}", file=sys.stderr) + + def warning(msg): + if warning_enabled: + print(f"WARNINGS [{basename}]: {msg}", file=sys.stderr) + + class converters: + def real(x): + try: + return float(x) + except: + return float("NAN") + + def datetime(x): + return datetime.strptime(x, "%Y-%m-%d %H:%M:%S") + + cols = {"START_TIME_LOCAL": datetime, "MW": real, "LMP": real} + + for arg in sys.argv[1:]: + args = arg.split("=") + if type(args) is list and len(args) > 1: + token = args[0] + value = "=".join(args[1:]) + elif type(args) is list: + token = args[0] + value = None + else: + token = args + value = None + if token in ["-h", "--help", "help"]: + syntax() + elif token in ["-B", "--bits"]: + resolution = int(value) + elif token in ["-C", "--class"]: + classname = value + elif token in ["-f", "--file"]: + data_file = value + elif token in ["-d", "--debug"]: + debug_enabled = not debug_enabled + elif token in ["-i", "--input"]: + inputs = value.split(",") + elif token in ["-I", "--init"]: + init_time = datetime.fromisoformat(value) + elif token in ["-g", "--glm"]: + glmname = value + elif token in ["-k", "--order"]: + K = int(value) + elif token in ["-l", "--lookahead"]: + lookahead = int(value) + elif token in ["-M", "--module"]: + modulename = value + elif token in ["-N", "--name"]: + objname = value + elif token in ["-o", "--output"]: + outputs = value.split(",") + elif token in ["-P", "--player"]: + playername = value + elif token in ["-q", "--quiet"]: + quiet_enabled = not quiet_enabled + elif token in ["-R", "--recorder"]: + recordername = value + elif token in ["-t", "--type"]: + modeltype = value + elif token in ["-v", "--verbose"]: + verbose_enabled = not verbose_enabled + elif token in ["-w", "--warning"]: + warning_enabled = not warning_enabled + elif token in ["--stdev"]: + constraint = int(value) + elif token in ["--with_hour"]: + with_hour = True + elif token in ["--with_day"]: + with_day = True + elif token in ["--with_weekend"]: + with_weekend = True + elif token in ["--with_month"]: + with_month = True + elif token in ["--with_year"]: + with_year = True + elif token in ["--by_hour"]: + by_hour = True + elif token in ["--by_weekday"]: + by_day = True + elif token in ["--by_month"]: + by_month = True + elif token in ["--by_year"]: + by_year = True + else: + error(f"option '{token}' is not valid", E_OPTION) + + # processing requirements met + if inputs and outputs: + + # set output + if glmname != "/dev/stdout": + if not glmname.endswith(".glm"): + error( + f"output file '{glmname}' is not a supported file format", E_INVALID + ) + try: + sys.stdout = open(glmname, "w") + except Exception as err: + error(err, E_OUTPUT) + + # load data + try: + original_data = pd.read_csv( + data_file, usecols=converters.cols.keys(), converters=converters.cols + ) + outputs.insert(0, "START_TIME_LOCAL") + output_data = original_data[outputs] + output_names = output_data.columns[1:] + ndx = list( + map( + lambda t: datetime(t.year, t.month, t.day, t.hour), + pd.DatetimeIndex(output_data[output_data.columns[0]]), + ) + ) + data = pd.DataFrame(output_data.groupby(ndx)[output_data.columns[1]].mean()) + verbose(data) + + inputs.insert(0, "START_TIME_LOCAL") + input_data = original_data[inputs] + col0 = input_data.columns[0] + if with_day: + weekdays = list(map(lambda x: x.weekday(), input_data[col0])) + for weekday in set(weekdays): + input_data[f"weekday{weekday}"] = list( + map(lambda x: int(x == weekday), weekdays) + ) + if with_weekend: + weekdays = list(map(lambda x: x.weekday(), input_data[col0])) + input_data["weekday"] = list(map(lambda x: int(x < 5), weekdays)) + if with_hour: + hours = list(map(lambda x: x.hour, input_data[col0])) + for hour in set(hours): + input_data[f"hour{hour}"] = list( + map(lambda x: int(x == hour), hours) + ) + if with_month: + months = list(map(lambda x: x.month, input_data[col0])) + for month in set(months): + input_data[f"month{month}"] = list( + map(lambda x: int(x == month), months) + ) + input_names = input_data.columns[1:] + ndx = list( + map( + lambda t: datetime(t.year, t.month, t.day, t.hour), + pd.DatetimeIndex(input_data[input_data.columns[0]]), + ) + ) + data = input_data.groupby(ndx).mean().join(data).dropna() + data = data.sort_index(ascending=False) + verbose(data) + + except Exception as err: + + print(f"#error {basename} {err}") + error(err, E_INPUT) + + if init_time: + back_time = init_time - timedelta(hours=K + 1) + print( + f"\n// initial input and state vector from {back_time} to {init_time}" + ) + U0 = data[input_names][back_time : init_time - timedelta(hours=1)].to_dict( + "list" + ) + for name in input_names: + print( + f"#define U0_{name}={','.join(list(map(lambda z: str(z), U0[name])))}" + ) + print(f"#define T0_{name}={data.index[0]}") + print(f"#define TN_{name}={data.index[-1]}") + X0 = data[output_names][back_time : init_time - timedelta(hours=1)].to_dict( + "list" + ) + for name in output_names: + print( + f"#define X0_{name}={','.join(list(map(lambda z: str(z), X0[name])))}" + ) + print(f"#define T0_{name}={data.index[0]}") + print(f"#define TN_{name}={data.index[-1]}") + + for output_name in output_names: + + # perform model fit + try: + + Y = np.matrix(data[output_name]).transpose() + U = [] + for input_name in input_names: + U.append(np.matrix(data[input_name]).transpose()) + x, Y, M = identify(Y, np.hstack(U), K, lookahead) + + except Exception as err: + + print(f"#error {basename} {err}") + error(err, E_INVALID) + + if debug_enabled: + print("") + print(f"// input = {M}".replace("\n", "\n// ")) + print(f"// output = {Y[K + 1:]}".replace("\n", "\n// ")) + print( + f"\n// model = [{','.join(list(map(lambda z: str(z), x.transpose().round(2).tolist()[0])))}]" + ) + + opts = ["0"] + if resolution: + opts.append(f"resolution={resolution}") + if constraint: + mean = data[output_name].mean() + std = data[output_name].std() + opts.append( + f"minimum={mean - constraint * std},maximum={mean + constraint * std}" + ) + + n_inputs = len(data.columns) - 1 + for n in range(n_inputs): + # TODO: Review formulas + print(f"\n// {data.columns[n]} --> {output_name}") + print( + f"filter {output_name}_{data.columns[n]}(z,1h,{','.join(opts)}) = (", + end="", + ) + for k in range(0, K + 1): + print(f"{x[-k * n_inputs - 1, 0]:+f}z^{K - k:.0f}", end="") + print(f"{x[K + n, 0]:+f} ) / (z^{K:.0f}", end="") + for k in range(1, K): + print(f"{-x[K - k, 0]:+f}z^{K - k:.0f}", end="") + print(f"{-x[0, 0]:+f})", end=";\n") + + if playername: + print("module tape;") + print("class", "input", "{") + for name in input_names: + print(" ", "double", name, end=";\n") + print("}") + input_object = f"input_{random.randint(1e15, 1e16):x}" + print("#define", f"INPUTNAME={input_object}") + print("object input {") + print(" ", "name", input_object, end=";\n") + print(" ", "object", "player", "{") + print(" ", " ", "file", f'"{playername}"', end=";\n") + print(" ", "}", end=";\n") + print("}") + + if classname: + if modulename: + print("module", modulename, end=";\n") + print("class", classname, "{") + for name in output_names: + print(" ", "double", name, end=";\n") + print("}") + + if not objname: + objname = f"{classname}_{random.randint(1e15, 1e16):x}" + + print("#define", f"OUTPUTNAME={objname}") + print("object", classname, "{") + print(" ", "name", f"{objname}", end=";\n") + for input_name in input_names: + print( + " ", + output_name, + f"{output_name}_{input_name}({input_object}:{input_name},:U0_{input_name},:X0_{output_name})", + end=";\n", + ) + if recordername: + print(" ", "object", "recorder", "{") + print(" ", " ", "file", recordername, end=";\n") + print( + " ", " ", "property", ",".join(output_names), end=";\n" + ) + print(" ", " ", "interval", -1, end=";\n") + print(" ", "}", end=";\n") + print("}") + + else: + syntax(1)