Abstract
In this paper, we have proposed a methodology for energy consumption prediction in residential buildings. The proposed method consists of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For experimental analysis, we have collected real data from four multi-storied residential building. The collected data are provided as input for the acquisition layer. In the pre-processing layer, several data cleaning and preprocessing schemes were deployed to remove abnormalities from the data. In the prediction layer, we have used the deep extreme learning machine (DELM) for energy consumption prediction. Further, we have also used the adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) in the prediction layer. In the DELM different numbers of hidden layers, different hidden neurons, and various types of activation functions have been used to achieve the optimal structure of DELM for energy consumption prediction. Similarly, in the ANN, we have employed a different combination of hidden neurons with different types of activation functions to get the optimal structure of ANN. To obtain the optimal structure of ANFIS, we have employed a different number and type of membership functions. In the performance evaluation layer for the comparative analysis of three prediction algorithms, we have used the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results indicate that the performance of DELM is far better than ANN and ANFIS for one-week and one-month hourly energy prediction on the given data.
Highlights
The energy consumption in residential buildings has significantly increased in the last decade.Energy is an essential part of our lives and almost all things in some way are associated with electricity [1,2]
We have proposed a methodology based on a deep extreme learning machine (DELM) for energy consumption prediction in REVIEW
The energy consumption for each hour is recorded for a year, and the unit used for measurement is Kilowatt hour
Summary
The energy consumption in residential buildings has significantly increased in the last decade.Energy is an essential part of our lives and almost all things in some way are associated with electricity [1,2]. The energy consumption in residential buildings has significantly increased in the last decade. Smart solutions are required to certify the proper use of energy [4]. An energy consumption prediction is very significant to achieve efficient energy maintenance and reduce environmental effect [5,6,7]. In residential buildings, it is quite challenging as there are many types of buildings and different forms of energy. Many factors are involved to influence the energy behaviour of the building structures, such as weather circumstances, the physical material used in the building construction, company behaviour, sub-level systems, i.e., lighting, heating, ventilating, and air-conditioning (HVAC) systems, and the execution and routines of the sub-level components [8]
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