2023
DOI: 10.48550/arxiv.2302.12168
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A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers

Abstract: Short-term load forecasting (STLF) is vital for the daily operation of power grids. However, the nonlinearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. To that end, different forecasting methods have been proposed in the literature for day-ahead load forecasting, including a variety of deep learning models that are currently considered to achieve state-of-the-art performance. In order to compare the accuracy of such models, we focus on nati… Show more

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