Machine Learning Short-Term Energy Consumption Forecasting for Microgrids in a Manufacturing Plant

Abstract

Energy production and supply are important challenges for civilisation. Renewable energy sources present an increased share of the energy supply. Under these circumstances, small-scale grids operating in small areas as fully functioning energy systems are becoming an interesting solution. One crucial element to the success of micro-grid structures is the accurate forecasting of energy consumption by large customers, such as factories. This study aimed to develop a universal forecasting tool for energy consumption by end-use consumers. The tool estimates energy use based on real energy-consumption data obtained from a factory or a production machine. This model allows the end-users to be equipped with an energy demand prediction, enabling them to participate more effectively in the smart grid energy market. A single, long short-term memory (LSTM)-layer-based artificial neural network model for short-term energy demand prediction was developed. The model was based on a manufacturing plant’s energy consumption data. The model is characterised by high prediction capability, and it predicted energy consumption, with a mean absolute error value of 0.0464. The developed model was compared with two other methodologies.