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• W2893898383 abstract "In this paper, we have proposed a methodology for energy consumption prediction in residential buildings. The proposed method consists of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For experimental analysis, we have collected real data from four multi-storied residential building. The collected data are provided as input for the acquisition layer. In the pre-processing layer, several data cleaning and preprocessing schemes were deployed to remove abnormalities from the data. In the prediction layer, we have used the deep extreme learning machine (DELM) for energy consumption prediction. Further, we have also used the adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) in the prediction layer. In the DELM different numbers of hidden layers, different hidden neurons, and various types of activation functions have been used to achieve the optimal structure of DELM for energy consumption prediction. Similarly, in the ANN, we have employed a different combination of hidden neurons with different types of activation functions to get the optimal structure of ANN. To obtain the optimal structure of ANFIS, we have employed a different number and type of membership functions. In the performance evaluation layer for the comparative analysis of three prediction algorithms, we have used the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results indicate that the performance of DELM is far better than ANN and ANFIS for one-week and one-month hourly energy prediction on the given data." @default.
• W2893898383 created "2018-10-05" @default.
• W2893898383 creator A5004507358 @default.
• W2893898383 creator A5008406878 @default.
• W2893898383 date "2018-09-28" @default.
• W2893898383 modified "2023-10-16" @default.
• W2893898383 title "A Prediction Methodology of Energy Consumption Based on Deep Extreme Learning Machine and Comparative Analysis in Residential Buildings" @default.
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• W2893898383 doi "https://doi.org/10.3390/electronics7100222" @default.
• W2893898383 hasPublicationYear "2018" @default.
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