Development of an energy prediction model for residential buildings using Artificial Neural Network

• Published in: IOP conference series. Earth and environmental science Vol. 1279; no. 1; pp. 12006 - 12016
• Main Authors: Nainwal, Rajat, Sharma, Aniket
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.12.2023
• Subjects: Algorithms; Artificial neural networks; Back propagation networks; Energy consumption; Energy demand; Energy efficiency; Multilayer perceptrons; Multilayers; Neural networks; Prediction models; Predictions; Regression analysis; Regression models; Regulatory agencies; Residential areas; Residential buildings; Residential energy; Training
• ISSN: 1755-1307; 1755-1315
• DOI: 10.1088/1755-1315/1279/1/012006

Abstract A model has been developed in this study for predicting the energy consumption of residential building sector using Artificial Neural Network. This model was based on the Multi-Layer Perceptron architecture using feed-forward back propagation algorithm for training. The Levenberg-Marquardt function and the Gradient Descent with momentum function were used as training and learning function, respectively. The mean squared error was used to check the overall performance of the developed model. Trials were performed to finalize the number of hidden layers required for the model. Regression analysis was done between the predicted and actual data to validate the proposed ANN model. The prediction for the same dataset was also performed using the traditional trend extrapolation method, and the predicted results of both were compared with the actual energy consumption data recorded by the electricity regulatory authority of the state. It has been concluded that the accuracy of predicted data using the proposed model was very high (i.e., of 99.54%) as compared to the traditionally used TEM (i.e., 91.07%). MSE achieved for the ANN model was 0.01767% and that of TEM was 0.13115%. The outcome of this study can be used at building level to achieve energy efficiency by predicting the energy consumption and at the level of distribution companies to predict the overall energy demand by the respective sector, and take measures accordingly.