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

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.

Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors

“…LMLR model is an MLR model with a Logarithmic transformation which is also used for modeling the energy consumption in residential and commercial sectors [82]. Indeed, in energy consumption modeling, RCEC is the response variable, and POP, GDP, NGP, EP, and RESH are the predictor variables as presented in Equations (2) and (3).…”

“…The developed logarithmic multi-variate linear regression (LMLR) model defines energy consumption in the residential and commercial sectors equation as presented in Equation (3). The coefficients of the variables in the LMLR equation are presented in Table 3.…”

“…Energy is a vital need for contemporary human life and has profound impressions on social security, welfare as well as sturdy economic growth and development of a nation [1,2]. Despite the high cost and scarcity of energy [3], the growing world population has raised the global energy demand, especially in residential and commercial sectors. For example, energy demand had increased 2.3% in 2018 in comparison with 2017 [4].…”

Evaluation of Sequence-Learning Models for Large-Commercial-Building Load Forecasting

“…We first state three key factors identified as drivers of the implementation of a statistical-learning methodology, and algorithms to energy-system modelling and control: Fayaz et al [4] describe an approach and case study for the prediction of household energy consumption using feed-forward back-propagation neural networks. The authors discuss their outcomes based on data collected from four residential buildings in South Korea, including the preprocessing and tuning of the algorithms.…”

Computational Fluid Dynamics Based Fuzzy Control Optimization of Heat Exchanger via Genetic Algorithm

“…It has been utilized for medical use such as arrhythmia problems [1,2], anesthesia [3,4] and blood pressure estimation [5]. Moreover, the AI also has been applied to energy systems [6][7][8], electromagnetic field [9] and shape optimization to increase the aerodynamics of the unmanned aerial vehicle [10].…”