Home energy forecast performance tool for smart living services suppliers under an energy 4.0 and CPS framework

• Autor(a) principal: RODRIGUES, FILIPE
• Data de Publicação: 2022
• Outros Autores: Cardeira, Carlos, Calado, João Manuel Ferreira, Melicio, Rui
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.21/14302
• topic: Industry 4.0; Energy management; Smart grids; Artificial neural networks; Smart home; Smart meter; Forecasting

Industry 4.0 is a paradigm consisting of cyber-physical systems based on the interconnection between all sorts of machines, sensors, and actuators, generally known as things. The combination of energy technology and information and technology communication (ICT) enables measure ment, control, and automation to be performed across the distributed grid with high time resolution. Through digital revolution in the energy sector, the term Energy 4.0 emerges in the future electric sector. The growth outlook for appliance usage is increasing and the appearance of renewable energy sources on the electric grid requires strategies to control demand and peak loads. Potential feedback for energy performance is the use of smart meters in conjunction with smart energy man agement; well-designed applications will successfully inform, engage, empower, and motivate con sumers. This paper presents several hands-on tools for load forecasting, comparing previous works and verifying which show the best energy forecasting performance in a smart monitoring system. Simulations were performed based on forecasting of the hours ahead of the load for several households. Special attention was given to the accuracy of the forecasting model for weekdays and weekends. The development of the proposed methods, based on artificial neural networks (ANN), pro vides more reliable forecasting for a few hours ahead and peak loads.