Intelligent Electricity Load Forecasting Method using ARIMA-LSTM-Random Forest

Abstract

The instability of energy systems caused by internal economic factors and external challenges, including geopolitical conflicts, significantly complicates the process of planning and managing energy resources. An essential tool for implementing energy-saving measures is introducing modern computer technologies, including artificial intelligence systems, in the energy sector. Intelligent technologies make it possible to use methods for predicting electrical load, including artificial intelligence algorithms. This paper proposes a combined ARIMA-LSTM-Random Forest model for forecasting electric load. The combination of the approaches allows considering both linear and nonlinear dependencies in the data, which is critical to ensure the accuracy of forecasts. Using data for the previous seven days provides enough information to identify seasonal trends and fluctuations, which makes this a promising prospect for medium-term forecasting in energy monitoring tasks. Thus, combining the ARIMA, LSTM, and Random Forest methods achieves high accuracy in forecasting electricity consumption. The proposed approach is an optimal solution since it combines the advantages of each model and compensates for their shortcomings. The proposed ARIMA-LSTM-Random Forest method significantly improved the results: MSE = 0.27, RMSE = 0.23, MAPE = 0.35%. The method minimized absolute and relative errors, confirming its advantage for this forecasting task. The results are promising for practical application in the load management of electric networks.