Effective Electrical Load Balancing Using RNNs

The increasing global need for electricity, particularly in agriculture, has been a serious challenge for farmers to accurately predict and manage their energy requirement. This research addresses the critical task of electrical load forecasting utilizing the latest machine learning techniques, specifically Recurrent Neural Networks (RNNs), which include Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. The research utilizes the five-year time series data of two leading datasets: the Electrical Reliability Council of Texas (ERCOT) and Réseau de transport d'électricité (RTE) France. The fundamental objective of this research work was to develop artificial intelligence models that are skilled at performing accurate load balancing on time series data, considering various environmental variables impacting electricity consumption. The models are successful in analyzing trends and forecast future load requirements with the aim of improving the performance over traditional methods such as ARIMA and simple LSTM models. The performance of the proposed models was confirmed through mean absolute percentage error (MAPE) and mean absolute error (MAE) measures.