Empowering data-driven load forecasting by leveraging long short-term memory recurrent neural networks - Waqar Waheed; Qingshan Xu; Muhammad Aurangzeb; Sheeraz Iqbal; Saadat Hanif Dar; Z.M.S. Elbarbary (Heliyon)
The integration of renewable energy sources has resulted in an increasing intricacy in the functioning and organization of power systems. Accurate load forecasting, particularly taking into account dynamic factors like as climatic and socioeconomic impacts, is essential for effective management. Conventional statistical analysis and machine learning methods struggle with accurately capturing the intricate temporal relationships present in load data. Recurrent neural networks (RNNs), specifically long short-term memory (LSTM) networks, demonstrate potential in representing and interpreting sequences of data. This study presents an approach that employs an LSTM-RNN model for load forecast prediction. This study highlights the importance of this technology in combining effective demand response with distributed renewable energy sources, which are crucial for the stability of smart grids and accurate power demand estimation. The LSTM-RNN model has outstanding accuracy, with a Mean Absolute Percentage Error (MAPE) of 1.5% and a Root Mean Squared Error (RMSE) of 26.5 for hourly forecasts. Additionally, it achieves a MAPE of 1.77% and an RMSE of 30 for yearly load estimations. The hourly LSTM-RNN load forecast model outperforms the yearly LSTM-RNN load forecast model, as evidenced by lower error rates. Importantly, this model demonstrates robustness even when the inputs are inadequate or noisy. To summarize, this research suggests that LSTM-RNN is a practical and efficient approach for precisely forecasting load in power systems. This underscores its capacity to enhance operational efficiency and resilience in power systems.