Integrating the industrial consumer into smart grid by load curve forecasting using machine learning

Integration of industrial consumers into the smart grid concept can be facilitated by optimizing short and very short-term forecasts of load curves for industrial consumers. Minimizing forecast errors can improve the supplier-consumer relationship by reducing supplier balancing costs, which can lower the energy bill, and anticipate possible network faults or network congestion which means improved operation across the entire power system. The present paper aims at demonstrating the usefulness of energy forecasting by using machine learning methods. In the context of installing monitoring systems with load curve recording data at short intervals of time, huge amounts of data are obtained that need to be capitalized in real time (automated), otherwise the investments necessary to modernize the way in which the energy is used 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 detail at large industrial consumers where forecasts can be made on certain areas inside factories or electric equipment with high installed electric capacity. The use of smart grid technologies removes the current limits for monitoring, processing and communication between consumption and production. To be able to capitalize on the forecasts, a very important aspect is the automation of their realization and quick access to the decision makers who may be human or other smart grid integrated systems. That is why the present paper proposes the use of machine learning methods in the short and very short forecast, and the results will be compared with the classical forecasting methods. Hourly load curves from the industrial consumers will be used for forecasts.