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Among the most significant solutions suggested for estimating energy consumption and cooling load, one can refer to enhancing energy efficiency in non-residential and residential buildings. A structure's characteristics must be considered when estimating how much heating and cooling is required. To design and develop energy-efficient buildings, it can be helpful to research the characteristics of connected structures, such as the kinds of cooling and heating systems needed to ensure sui interior air quality. As an important part of energy consumption and demand of buildings, the assessment of cooling load conditions from the envelope of large buildings has not been comprehensively understood yet. In the present paper, a new conceptual system has been developed to anticipate cooling load in the sector of residential buildings. Also, the paper briefly describes the major models of the developed system to maintain continuity and concentrate on the prediction model of the cooling load. To predict cooling load, authors have modelled two methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in conjunction with teaching-learning-based optimization (TLBO). This article aims to illustrate how artificial intelligence (AI) approaches play an essential role in addressing the mentioned necessity and help estimate the optimal design parameters for various stations. The value of the multiple determination coefficient is also determined. The values of the training R² (coefficient of multiple determination) are 0.96446 and 0.97585 for TLBO-MLP and TLBO-ANFIS in the training stage and 0.95855 and 0.9721 in the testing stage, respectively, with an unknown dataset which is acceptable. The training RMSE values for TLBO-MLP and TLBO-ANFIS are 0.0685 and 0.11176 for training and 0.07074 and 0.12035 for testing, respectively, for the unknown dataset, which is acceptable. The lowest RMSE value and the higher R² value indicate the favourable accuracy of the TLBO-MLP technique. According to the high value of R² (97%) and the low value of RMSE, TLBO-MLP can predict residential buildings' cooling load. [ABSTRACT FROM AUTHOR]

Air conditioning systems are the most viable source to avoid heat waves. Unfortunately, this puts a massive burden on the total electrical load. To deal with this critical issue, the Saudi Building Code National Committee has initiated the Saudi energy conservation code (SBC-602). This paper investigated the role of SBC-602 on the reduction of energy consumption in residential buildings. Furthermore, it also proposes the most effective building element to be insulated. A case study of energy consumption was modelled using SketchUp and analysed using OpenStudio Software. Inconsequence, three scenarios of U-values as an input in this work were suggested which are; building without thermal insulation, building with thermal insulation using Saudi electricity company (SEC) requirement, and using SBC-602 requirement. The study was conducted in three different cities Riyadh, Hail, and Abha, representing three climate zones in Saudi Arabia. Design of experiment (DOE) method was utilised to analyse the significant effects of the input factors. The result indicates that the decrease of the walls U-value is the most effective way to reduce the total electrical energy consumption. Moreover, decreasing roof U-value and increasing setpoint temperature were the following effective factor. [ABSTRACT FROM AUTHOR]