Residential lighting load profile modelling

Abstract

Occupant dynamic presence and characteristics associated with lighting loads/usage in residential buildings are not replicated in most practices currently adopted in modelling lighting profile. This study involves the use of adaptive neural fuzzy inference system (ANFIS) for lighting load profile prediction. Natural light, occupancy (active) and income level are the characterization (variables) factors considered in this investigation. The accuracy of the developed prediction models in relation to various income earners groups were analyzed using statistical measures; correlation output of the ANFIS approach and the impact of the characteristics on the lighting profile development in relation to trend analysis were also employed. Results obtained after validation of the developed models using investigative data, metering data and regression model showed a better correlation and root mean square error (RMSE) in comparison with actual values. The intelligence approach showed a better correlation of fit and good learning predictive accuracy in terms of behavioural and environmental variableness; and presents its output according to the complex nature of lighting usage in relation to the variables. The efficacy of the method was also validated.