Smart home energy management processes support through machine learning algorithms
Nikolaos E. Koltsaklis, Ioannis P. Panapakidis, Georgios C. Christoforidis, Jaroslav Knápek
Abstract
Smart Home Energy Management Systems can manifest energy consumption reduction targets in the residential sector and can be viewed as an approach to transform the consumer into an active prosumer. The present paper presents a smart home energy management system that includes flexible appliances, electric vehicles, and energy storage units. Efficient forecasting algorithms support the robust operation of the smart home energy management system. Specifically, the smart home energy management system receives as inputs forecasts of demand, renewable energy sources including photovoltaics and Wind Turbine generations, and real-time prices. In order to minimize energy costs, a variety of algorithms is compared to provide highly accurate forecasts.