LSTM-based Load Forecasting for Electric Power Generation

Abstract

A thorough analysis of the application of Long Short-Term Memory (LSTM) networks for load forecasting in the production of electricity is provided in this article. The writers of this publication carried out the study. Accurate load forecasting is essential to the efficient operation, planning, and administration of power systems. We provide an LSTM-based model that incorporates historical load data, meteorological information, and socioeconomic factors to anticipate the needed quantity of power in the future. The model's performance is evaluated using data from a regional power system, and the findings show that it performs better than traditional prediction methods. Our research indicates that the LSTM-based method may produce day-ahead forecasts with a mean absolute percentage error (MAPE) of 2.3%, an improvement of up to 18% above benchmark models. The study's conclusions significantly add to the growing corpus of knowledge about deep learning's uses in power systems. Moreover, they provide essential information to grid operators and utilities seeking to enhance their workload forecasting capacities.