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A platform for research: civil engineering, architecture and urbanism
Residential lighting load profile modelling
Highlights Good learning predictive accuracy in terms of behavioural and environmental variableness. Derives meaning from complexities associated with lighting usage and extract patterns in such. Income as an additional characterization factor will improve lighting usage modelling. Minimal reduction in repeated models for daily time of use periods. The model showed a good computation time capability.
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.
Residential lighting load profile modelling
Highlights Good learning predictive accuracy in terms of behavioural and environmental variableness. Derives meaning from complexities associated with lighting usage and extract patterns in such. Income as an additional characterization factor will improve lighting usage modelling. Minimal reduction in repeated models for daily time of use periods. The model showed a good computation time capability.
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.
Residential lighting load profile modelling
Popoola, O. (author) / Munda, J. (author) / Mpanda, A. (author)
Energy and Buildings ; 90 ; 29-40
2015-01-05
12 pages
Article (Journal)
Electronic Resource
English
Residential lighting load profile modelling
| Online Contents | 2015
Online Contents | 2004
| British Library Online Contents | 1992
Engineering Index Backfile | 1964