Industrial load forecasting using machine learning in the context of smart grid

Abstract

Integration of industrial consumers into the smart grid concept can be facilitated by optimizing load forecasting for industrial consumers. Minimizing forecast errors can improve the supplier-consumer relationship by reducing balancing costs and anticipate possible network faults. The present paper aims to research the efficiency of machine learning applied for industrial load. The dataset consists of hourly recorded values for electricity consumption generated by a meat processing facility. In the context of installing complex monitoring systems with high frequency recording intervals, huge amounts of data will be generated that require detalied analysis and real time processing, otherwise the investments in the smart grid are not justified, consequently disfavouring the development and digitization of electrical networks. Integration of the industrial consumer into the smart grid concept can be applied in great detail at large industrial consumers through robust forecasting. Forecasting the energy behaviour of a industrial consumer is a difficult task, high forecasting errors have been obtained due to the unpredictability of the consumer.