Buildings’ Heating and Cooling Load Prediction for Hot Arid Climates: A Novel Intelligent Data-Driven Approach

• Published in: Buildings (Basel) Vol. 12; no. 10; p. 1677
• Main Authors: Irshad, Kashif, Zahir, Md. Hasan, Shaik, Mahaboob Sharief, Ali, Amjad
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Algorithms; Arid climates; Aridity; Artificial intelligence; building; Buildings; Climate change; Construction; Construction industry; cooling load; Cooling loads; Cooling systems; data-driven; Energy consumption; Energy efficiency; Forecasting; Fuzzy logic; Green buildings; Heating; Heating load; Investigations; Load; Machine learning; Model accuracy; Neural networks; Optimization; Optimization techniques; Parameters; Performance prediction; prediction; Prediction models; Residential areas; Residential buildings; Root-mean-square errors; Saudi Arabia
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings12101677

An important aspect in improving the energy efficiency of buildings is the effective use of building heating and cooling load prediction models. A lot of studies have been undertaken in recent years to anticipate cooling and heating loads. Choosing the most effective input parameters as well as developing a high-accuracy forecasting model are the most difficult and important aspects of prediction. The goal of this research is to create an intelligent data-driven load forecast model for residential construction heating and cooling load intensities. In this paper, the shuffled shepherd red deer optimization linked self-systematized intelligent fuzzy reasoning-based neural network (SSRD-SsIF-NN) is introduced as a novel intelligent data-driven load prediction method. To test the suggested approaches, a simulated dataset based on the climate of Dhahran, Saudi Arabia will be employed, with building system parameters as input factors and heating and cooling loads as output results for each system. The simulation of this research is executed using MATLAB software. Finally, the theoretical and experimental results demonstrate the efficacy of the presented techniques. In terms of Mean Square Error (MSE), Root Mean Square Error (RMSE), Regression (R) values, Mean Absolute Error (MAE), coefficient of determination (R2), and other metrics, their prediction performance is compared to that of other conventional methods. It shows that the proposed method has achieved the finest performance of load prediction compared with the conventional methods.