Deep Learning for Energy Forecasting Using Gated Recurrent Units and Long Short-Term Memory

aspg-18-3100-1792574095 20261021091455 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 14 1 Deep Learning for Energy Forecasting Using Gated Recurrent Units and Long Short-Term Memory E. T. Sivadasan Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India; Vidya Academy of Science and Technology, Thrissur, Kerala, India N. Mohana Sundaram Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India R. Santhosh Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India Forecasting energy demand is essential for efficient grid management as it promotes steady operations, efficient markets, and sustainable energy practices. In this study, previously observed, evenly spaced energy consumption data are analysed using recurrent neural networks based on Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures to extract important insights, features, and remarkable patterns. First, the study examines the influence of meteorological features on energy consumption. The most significant meteorological features are determined by computing the MIC and Pearson's correlation coefficient. The selected features are then combined with historical energy consumption data to feed the neural network. Second, to improve and optimise the performance of the proposed models, two technical indicators - the daily energy usage average and the simple moving average - are considered. The following are some instances of comparisons in terms of prediction accuracy: (1) The MAPE of the proposed model is 2.47, whereas that of the current model is 4.03. (2) The MAPE of the existing model is 25.83, whereas the proposed solution is 18.68. (3) The MAPE of the suggested model is 24.8, while the MAPE of the current model is 26.6. (4) The MAPE of the present model is 4.77, whereas the suggested approach's is 4.42. 2025 90 101 3100 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.140107 https://www.americaspg.com/journal/18/article/3100 https://www.americaspg.com/storage/31723153223.pdf