Multiple households very short-term load forecasting using bayesian networks *

Autor(es)

Bessani, Michel ; Massignan, Julio A. D. ; Santos, Talysson M. O. ; London Jr, Joao B. A. ; Maciel, Carlos D.

• Tipo de documento: Artigo Científico
• Fonte: Electric Power Systems Research; v. 189, p. 7-pg., 2020-12-01.

Resumo

Load forecasting is essential for different activities on power systems, and there is extensive research on approaches for forecasting in different time horizons, from next-hour to decades. However, because of higher uncertainty and variability compared to aggregated or medium and high voltage, the forecasting of the individual household load is a current challenge. This paper presents a load forecasting for multiple households using Bayesian networks. Our model, which is multivariate, uses past consumption, temperature, socioeconomic and electricity usage aspects to forecast the next instant household load value. It was tested using real data from the Irish smart meter project and its performance was compared with other forecasting methods. Results have shown that the proposed approach provides consistent single forecast model for hundreds of households with different consumption patterns, showing a generalisation capability in an efficient manner. (AU)

• Processo FAPESP: 16/19646-6 - Estimador de estado trifásico multiárea para sistemas de distribuição de larga escala
• Beneficiário: Julio Augusto Druzina Massignan
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 14/50851-0 - INCT 2014: Instituto Nacional de Ciência e Tecnologia para Sistemas Autônomos Cooperativos Aplicados em Segurança e Meio Ambiente
• Beneficiário: Marco Henrique Terra
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 18/00214-4 - Estimadores de estado para sistemas de distribuição: abordagens clássicas e novos algoritmos para o Smart Grid
• Beneficiário: Julio Augusto Druzina Massignan
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado