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.
Highlights
Technological advancement in the Internet of Things (IoT) has opened the doors to smart buildings which are gaining popularity throughout the world [1]
The feed forward back propagation neural network (FFBPNN) is a supervised machine learning algorithm; it is desirable to divide the data into a specific ratio for training and testing
As in this work we have carried out energy consumption prediction in residential building for a different period, we have divided the data into different training and testing ratios
Summary
Technological advancement in the Internet of Things (IoT) has opened the doors to smart buildings which are gaining popularity throughout the world [1]. The devices in smart buildings consume more energy compared to ordinary buildings due to the installation of sensors and other devices [3]. The energy production requires a lot of financial resources due to which there is a need to produce the energy as per the requirements of the building or the customers. For the energy consumption prediction, mostly different approaches have been adopted using machine learning techniques, prediction algorithms, and neural network-based approaches [4]
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