Forecasting highly fluctuating electricity load using machine learning models based on multimillion observations

Mohamed Abdallah, Manar Abu Talib, Mariam Hosny, Omnia Abu Waraga, Qassim Nasir, Muhammad Arbab Arshad

Abstract: Highlights•50 million observations were used to model monthly electricity consumption.•Effect of external features on electricity consumption was modeled and investigated.•Randomly split and temporally ordered datasets were used to examine the AI models.•Random forests achieved the highest accuracy when trained using both datasets.•Rolling and moving cross-validation were implemented to analyze the models.•Neural network was the most stable model with minimal variation in predicted values.