Abstract
AbstractIn the electricity industry, correct short-term or long-term load forecasting at all times is of extreme importance as it leads to the efficient working of the power system. This paper presents a thorough study performed for electricity demand forecast on the dominion energy data collected from PJM energy market using deep recurrent neural network and its enhanced case, i.e., stacked long short-term memory network, which are some of the powerful deep learning algorithms popularly used for time series forecasting. This paper also sheds light on the importance of normalization and lays down the effect of tuning the model, for obtaining an enhanced accuracy of models. One day ahead, electricity demand forecasting has been done, and evaluation metrics such as R2 score, root mean squared error (RMSE), and mean absolute percentage error are calculated to validate the behavior of the algorithms/models after testing.KeywordsElectricity load forecastingStacked LSTMRNNR2 scoreRMSEMAPEMSE
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