Electricity consumption forecasting using neural networks for low-carbon power systems planning

Abstract

Power systems require the continuous balance of energy supply and demand for their appropriate functioning, which makes electricity forecast a necessary process for the successful planning of operation and expansion of modern power systems, especially with the increase of renewable energy resources to be accommodated in order to realize low-carbon power systems. The task of predicting electricity consumption is complex because electricity demand patterns are intricate and involve various factors such as weather conditions. Recurring Neural Networks (RNN), such as Long Short-Term Memory (LSTM) networks, can learn long sequence patterns and make multi-step forecasts at once considering several variables, which can be especially useful for time series forecasts such as electricity consumption. This paper presents the application and assessment of a multivariate multi-step times series forecasting model based on LSTM neural networks for short-term prediction of electricity consumption using a dataset that encompasses data on energy load and meteorological elements from Belgorod Oblast in Russia as a case study.