Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting

• Autor(a) principal: Jozi, Aria
• Data de Publicação: 2019
• Outros Autores: Pinto, Tiago, Praça, Isabel, Silva, Francisco, Teixeira, Brígida, Vale, Zita
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.22/16783
• topic: Electricity consumption; Forecasting; Fuzzy rule based methods; MOGUL

This paper presents the application of a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach (MOGUL) to forecast energy consumption. Historical data referring to the energy consumption gathered from three groups, namely lights, HVAC and electrical socket, are used to train the proposed approach and achieve forecasting results for the future. The performance of the proposed method is compared to that of previous approaches, namely Hybrid Neural Fuzzy Interface System (HyFIS) and Wang and Mendel’s Fuzzy Rule Learning Method (WM). Results show that the proposed methodology achieved smaller forecasting errors for the following hours, with a smaller standard deviation. Thus, the proposed approach is able to achieve more reliable results than the other state of the art methodologies