SHORT TERM LOAD FORECASTING USING RECURRENT AND SPATIAL DEEP LEARNING MODEL FORECASTING
Abstract
Short-term load forecasting is critical to optimizing energy use and maintaining the stability of the power network. This study proposes an LSTM-based deep learning model for electricity demand forecasting, incorporating 21 input features, including time of day, day of week, and holiday factors within a 24-hour sequence to capture temporal consumption patterns. The model consists of two LSTM layers with 50 units each, applying dropout regularization and the Adam optimizer to improve training efficiency. When evaluated on the AEP dataset, the model achieves a MAPE of 0.65 percent, demonstrating its accuracy in handling nonstationary and seasonal variations. Comparative analysis with GRU, BiGRU, BiLSTM, and 1D CNN confirms that LSTM consistently achieves lower error rates. These findings highlight LSTM’s effectiveness in improving STLF accuracy, contributing to better energy planning and power network stability.
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