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

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.

[1]  Do-Hyeun Kim,et al.  A Prediction Methodology of Energy Consumption Based on Deep Extreme Learning Machine and Comparative Analysis in Residential Buildings , 2018, Electronics.

[2]  William E. Roper,et al.  Energy demand estimation of South Korea using artificial neural network , 2009 .

[3]  Jesús M. Zamarreño,et al.  Prediction of hourly energy consumption in buildings based on a feedback artificial neural network , 2005 .

[4]  Abdul Salam Shah,et al.  A Simple and Easy Approach for Home Appliances Energy Consumption Prediction in Residential Buildings Using Machine Learning Techniques , 2017 .

[5]  J. B. Nixon,et al.  Investigation into the relationship between chlorine decay and water distribution parameters using data driven methods , 2006, Math. Comput. Model..

[6]  Eric Zamai,et al.  Realtimes dynamic optimization for demand-side load management , 2008 .

[7]  Yi-Chung Hu,et al.  Electricity consumption prediction using a neural-network-based grey forecasting approach , 2017, J. Oper. Res. Soc..

[8]  Vivek Srikumar,et al.  Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks , 2018 .

[9]  Jianqiang Yi,et al.  Building Energy Consumption Prediction: An Extreme Deep Learning Approach , 2017 .

[10]  Stéphane Ploix,et al.  A prediction system for home appliance usage , 2013 .

[11]  Simona Vasilica Oprea,et al.  Electricity Consumption and Generation Forecasting with Artificial Neural Networks , 2017 .

[12]  Julio Bros Williamson,et al.  Visualising Energy Use for Smart Homes and Informed Users , 2015 .

[13]  Fazli Wahid,et al.  Short-Term Energy Consumption Prediction in Korean Residential Buildings Using Optimized Multi-Layer Perceptron , 2017 .

[14]  Ping-Huan Kuo,et al.  A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting , 2018 .

[15]  Melvin Robinson,et al.  Prediction of residential building energy consumption: A neural network approach , 2016 .

[16]  Abdul Salam Shah,et al.  A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments , 2019, Inf..

[17]  Guohai Liu,et al.  Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis , 2015 .

[18]  Abdul Salam Shah,et al.  Statistical Features Based Approach (SFBA) for Hourly Energy Consumption Prediction Using Neural Network , 2017 .