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• W40390932 abstract "In the paper a fault detection analysis through neural ensembling approaches is presented. Experimentation was carried out over two months monitoring data sets for the lighting energy consumption of an actual office building located at ENEA ‘Casaccia’ Research Centre. Using a fault free data set for the training, the Artificial Neural Networks Ensembling (ANNE) were used for the estimation of hourly lighting energy consumption in normal operational conditions. The fault detection was performed through the analysis of the magnitude of residuals using a peak detection method. Moreover the peak detection method was applied directly to the testing data set. Finally a majority voting method to ensemble the results of different ANN classifiers was performed. Experimental results show the effectiveness of ensembling approaches in automatic detection of abnormal building lighting energy consumption. In the energy optimization field, the evaluation of an actual building energy consumption data is a demandable and emerging area of building energy analysis. Therefore, developing automatic, accurate and reliable fault detection and diagnosis (FDD) methods is necessary in order to ensure the optimal operations of systems and to save energy. Different intelligent methods have been used to obtain useful information from building energy consumption data. A number of papers on the application of Artificial Neural Networks(ANNs) for FDD have been published. In this paper the capability of different Artificial Neural Networks Ensembling (ANN) approaches for artificial lighting fault detection of a real office building is demonstrated. The fault detection has been performed analyzing the magnitude of the residual generated by ANNE using a peak detection method. Moreover a majority voting method has been performed to ensemble the results of different ANN classifiers. In the first part of the paper a brief theoretical description of the methods analyzed is presented. Then the application of a fault detection analysis for the lighting energy consumption is showed with the aim to compare the capability of neural ensembling approaches in detecting two artificial faults created in the testing period. 2. Consumption modeling by Artificial Neural Network basic ensembling method" @default.
• W40390932 created "2016-06-24" @default.
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• W40390932 date "2013-01-01" @default.
• W40390932 modified "2023-09-26" @default.
• W40390932 title "Building Energy Consumption Modeling with Neural Ensembling Approaches for Fault Detection Analysis" @default.
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