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A Framework for Prediction of Household Energy Consumption Using Feed Forward Back Propagation Neural Network

1
Department of Computer Engineering, Jeju National University, Jeju City 63243, Korea
2
College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia
3
Department of Mathematics, Kohat University of Science & Technology (KUST), Kohat 26000, Pakistan
4
Department of Computer Engineering, University of Kuala Lumpur (UniKl-MIIT), Kuala Lumpur 50250, Malaysia
*
Author to whom correspondence should be addressed.
Technologies 2019, 7(2), 30; https://doi.org/10.3390/technologies7020030
Received: 25 January 2019 / Revised: 20 March 2019 / Accepted: 26 March 2019 / Published: 1 April 2019
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Abstract

Energy is considered the most costly and scarce resource, and demand for it is increasing daily. Globally, a significant amount of energy is consumed in residential buildings, i.e., 30–40% of total energy consumption. An active energy prediction system is highly desirable for efficient energy production and utilization. In this paper, we have proposed a methodology to predict short-term energy consumption in a residential building. The proposed methodology consisted of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For experimental analysis, real data collected from 4 multi-storied buildings situated in Seoul, South Korea, has been used. The collected data is provided as input to the data acquisition layer. In the pre-processing layer afterwards, several data cleaning and preprocessing schemes are applied to the input data for the removal of abnormalities. Preprocessing further consisted of two processes, namely the computation of statistical moments (mean, variance, skewness, and kurtosis) and data normalization. In the prediction layer, the feed forward back propagation neural network has been used on normalized data and data with statistical moments. In the performance evaluation layer, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) have been used to measure the performance of the proposed approach. The average values for data with statistical moments of MAE, MAPE, and RMSE are 4.3266, 11.9617, and 5.4625 respectively. These values of the statistical measures for data with statistical moments are less as compared to simple data and normalized data which indicates that the performance of the feed forward back propagation neural network (FFBPNN) on data with statistical moments is better when compared to simple data and normalized data. View Full-Text
Keywords: statistical moments; artificial neural network; energy consumption prediction; smart homes; smart buildings statistical moments; artificial neural network; energy consumption prediction; smart homes; smart buildings
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Fayaz, M.; Shah, H.; Aseere, A.M.; Mashwani, W.K.; Shah, A.S. A Framework for Prediction of Household Energy Consumption Using Feed Forward Back Propagation Neural Network. Technologies 2019, 7, 30.

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