International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 9, No. 5, 8 May 2017
Cover page and Table of Contents: PDF (size: 389KB)
Statistical Features Based Approach (SFBA) for Hourly Energy Consumption Prediction Using Neural Network
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Author(s)
Fazli Wahid
1,*
Rozaida Ghazali
1
Muhammad Fayaz
2
Abdul Salam Shah
3
Index Terms
Energy Efficiency, Energy Prediction, Energy Management, Multi-Layer Perceptron, Neural Network, Residential Buildings
Abstract
In this paper, new statistical features based approach (SFBA) for hourly energy consumption prediction using Multi-Layer Perceptron is presented. The model consists of four stages: data retrieval, data pre-processing, feature extraction and prediction. In the data retrieval stage, historical hourly consumed energy data has been retrieved from the database. During data pre-processing, filters have been applied to make the data more suitable for further processing. In the feature extraction stage, mean, variance, skewness, and kurtosis are extracted. Finally, Multi-Layer Perceptron has been used for prediction. For experimentation with Multi-Layer Perceptron with different training algorithms, a final model of the network was designed in which the scaled conjugate gradient (trainscg) was used as a network training function, tangent sigmoid (Tansig) as a hidden layer transfer function and linear function as an output layer transfer function. For hourly energy consumption prediction, a total of six weeks data of ten residential buildings has been used. To evaluate the performance of the proposed approach, Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), evaluation measurements were applied.
Cite This Paper
Fazli Wahid, Rozaida Ghazali, Muhammad Fayaz, Abdul Salam Shah, "Statistical Features Based Approach (SFBA) for Hourly Energy Consumption Prediction Using Neural Network", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.5, pp.23-30, 2017. DOI:10.5815/ijitcs.2017.05.04
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