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        <h1 style="margin-bottom: 40px;">PSML: A Multi-scale Time-series Dataset for Machine Learning in Decarbonized Energy Grids</h1>
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            <!-- <h2 class="header-light regular-pad">PSML</h2> -->
              <p class="lead">The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable energy resources and electrified transportation, the reliable and secure operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML) based approaches towards the reliable operation of future electric grids. The dataset is generated through a novel transmission + distribution (T+D) co-simulation designed to capture the increasingly important interactions and uncertainties of the grid dynamics, containing electric load, renewable generation, weather, voltage, and current measurements at multiple spatio-temporal scales. We envision that this dataset will enable advances for ML in dynamic systems, while simultaneously allowing ML researchers to contribute towards carbon-neutral electricity and mobility. </p>
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            <h3 class="text-center">Event Detection, Classification and Localization</h3>
            <p>Monitor the health of power grid by recognizing and localizing disturbances from sensors such as synchrophasors/phasor measurement units (PMUs).</p>
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            <h3 class="text-center">Load and Renewable Energy Forecasting</h3>
            <p>Robust hierarchical forecasting of load and renewable energy with the presence of uncertainties and extreme events.</p>
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            <h3 class="text-center">Synthetic Time-series Generation</h3>
            <p>Multi-channel time series generation with disturbance-induced dynamic voltage, current and power measurements from power grids.</p>
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