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Multiple Households Very Short-Term Load Forecasting using Bayesian Networks

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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.

Author(s):

Michel Bessani    
Federal University of Minas Gerais
Brazil

Julio Massignan    
University of São Paulo
Brazil

Talysson M. O. Santos    
University of São Paulo
Brazil

João London Jr.    
University of São Paulo
Brazil

Carlos Maciel    
University of São Paulo
Brazil

 

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