Predicting energy consumption of mosque buildings during the operation stage using deep learning approach

Abstract

The energy consumption resulting from the construction sector is increasing rapidly. Different building types have different energy consumption schemes, especially during their operational stage. Among these types of buildings, mosques are considered one of the intermittent building types that suffer from huge energy wastage. That’s why there is a need to monitor their operational performance through retrofitting or changing their operational scheme to enhance their operational performance and examine the effect of different alterations on the annual energy consumption. However, using traditional methods and simulation models to examine the impact of different alterations on the annual energy consumption of the mosque is considered difficult and time-consuming. Using machine learning and deep learning models, energy prediction models facilitated energy consumption predictions for improving building energy utilization. Therefore, this paper proposes an efficient approach for predicting mosques’ energy consumption using a deep learning approach. Convolution Neural Network (CNN) deep learning model is developed to predict the annual energy consumption of mosque buildings based on various operational Scenarios. 3D Laser scanning and a detailed energy analysis model with various simulation results are used to generate the mosque’s simulated energy consumption dataset. Different operational variables, such as the operational schedule, division of the mosque into zones, utilization of dimmers, presence of cooling system and the setting point, and the power consumption of the lighting system and appliances, have been utilized to estimate the annual energy consumption for the mosque to formulate the database. Using the mosque’s simulated energy consumption dataset, the CNN model was trained and tested to obtain the best model configuration with regard to the defined performance metrics. Besides, the performance of the developed model has been compared with the Support Vector Regression (SVR) model. The developed model has achieved results of 4.5% and 0.98 for the MAPE and R2, respectively. The results revealed the developed CNN model's effectiveness for predicting energy consumption for mosque buildings. This model will help compare different operational strategies for mosque buildings and determine energy consumption and energy-efficient options based on different operational characteristics.