Energy demand forecasting of buildings using random neural networks

Ahmad, Jawad; Tahir, Ahsen; Larijani, Hadi; Ahmed, Fawad; Aziz Shah, Syed; Hall, Adam James; Buchanan, William J.

Abstract

Energy uncertainty and ecological pressures have contributed to a high volatility in energy demand and consumption. The building sector accounts for 30 to 40% of the total global energy consumption. There is a high demand for novel techniques and viable energy strategies for reducing energy consumption in this domain. Energy prediction models have the potential to play a pivotal role in optimising energy consumption. The proposed work presents a new and accurate Energy Demand Prediction (EDP) model for large buildings. This approach leverages the Random Neural Network (RNN) prediction methodology. The proposed RNN-based EDP is compared with traditional Artificial Neural Network (ANN), Support Vector Machine (SVM) and linear regression models. A large building is modelled and simulated for one year in the Integrated Environment Solutions Virtual Environment (IES-VE). Several data inputs such as air temperature, internal gain and the number of people (occupancy) are calculated from IES-VE model and provided to traditional ANN and the proposed RNN predictor. A number of test parameters such as Root Mean Square (RMSE), Normalized Root Mean Square (N-RMSE), Mean Absolute Percentage Error (MAPE) and R provide the proposed RNN model with higher accuracy over the traditional ANN, SVM and linear regression. The proposed RNN predictor provides approximately half of the error of the ANN model. The traditional ANN model gives higher error values of 2.07×, 1.83× and 2.35× for RMSE, NRMSE and MAPE, respectively as compared to the proposed RNN model. Furthermore, the error values of SVM and linear regression were also higher than the proposed EDP scheme.

Citation

Ahmad, J., Tahir, A., Larijani, H., Ahmed, F., Aziz Shah, S., Hall, A. J., & Buchanan, W. J. (2020). Energy demand forecasting of buildings using random neural networks. Journal of Intelligent and Fuzzy Systems, 38(4), 4753-4765. https://doi.org/10.3233/jifs-191458

• Journal Article Type: Article
• Publication Date: Apr 30, 2020
• Deposit Date: Jul 1, 2020
• Publicly Available Date: Jul 1, 2020
• Journal: Journal of Intelligent & Fuzzy Systems
• Print ISSN: 1064-1246
• Electronic ISSN: 1875-8967
• Publisher: IOS Press
• Peer Reviewed: Peer Reviewed
• Volume: 38
• Issue: 4
• Pages: 4753-4765
• DOI: https://doi.org/10.3233/jifs-191458
• Keywords: Buildings, energy demand, random neural network, prediction
• Public URL: http://researchrepository.napier.ac.uk/Output/2671124