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

 

E. T. Sivadasan1, 2,*, N. Mohana Sundaram1, R. Santhosh1

1Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India

2Vidya Academy of Science and Technology, Thrissur, Kerala, India

Emails: santhoshrd@gmail.com; itismemohan@gmail.com; etsivadasan@gmail.com

Text Box: Abstract 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.

 

Received: February 07, 2024 Revised: April 20, 2024 Accepted: July 04, 2024

Keywords: Time Series; Energy Forecasting; MIC; GRU; LSTM