A Prediction Methodology of Energy Consumption Based on Deep Extreme Learning Machine and Comparative Analysis in Residential Buildings
Open Access
- 27 September 2018
- journal article
- research article
- Published by MDPI AG in Electronics
- Vol. 7 (10), 222
- https://doi.org/10.3390/electronics7100222
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.This publication has 41 references indexed in Scilit:
- Prediction-Learning Algorithm for Efficient Energy Consumption in Smart Buildings Based on Particle Regeneration and Velocity Boost in Particle Swarm Optimization Neural NetworksEnergies, 2018
- A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov ModelEnergies, 2018
- Energy Consumption Optimization and User Comfort Management in Residential Buildings Using a Bat Algorithm and Fuzzy LogicEnergies, 2018
- A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic BuildingEnergies, 2016
- A multi-criteria approach toward discovering killer IoT application in KoreaTechnological Forecasting and Social Change, 2016
- A review on applications of ANN and SVM for building electrical energy consumption forecastingRenewable and Sustainable Energy Reviews, 2014
- A review on the basics of building energy estimationRenewable and Sustainable Energy Reviews, 2014
- A review on the prediction of building energy consumptionRenewable and Sustainable Energy Reviews, 2012
- A review on buildings energy consumption informationEnergy and Buildings, 2008
- Artificial neural networks in energy applications in buildingsInternational Journal of Low-Carbon Technologies, 2006