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Residential buildings account for nearly 60% of electrical energy consumption in hot climates as in Kuwait. As a result, this study proposes a reliable multi-objective model to obtain the optimal design for a typical residential Kuwaiti building by integrating an Artificial Neural Network (ANN) model with Genetic Algorithm (GA) optimization method. The ANN model was investigated and verified using the results of building performance simulations applying EnergyPlus software. The effects of sample size of dataset on performance of ANN were evaluated. The final optimal building design was optimized using the GA method after ensuring the convergence of the final ANN model. Several design and operation parameters were considered as decision variables, while cooling energy consumption, discomfort hours, and equivalent carbon emissions were selected as objective functions. In addition, sensitivity analysis was conducted to evaluate the impacts of decision variables on objective functions. The sensitivity results indicated that insulation highly affect energy consumption and carbon emission, while cooling setpoint played a key role in discomfort hours. Furthermore, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was applied for decision-making among Pareto optimal solutions. The results showed that the optimal solution suggested by TOPSIS methods using ANN-based model provided a substantial reduction in energy consumption, discomfort hours, and carbon emission up to 39.3%, 62.8%, and 40.5% compared with the base case, respectively. It is recommended that further research be undertaken in considering uncertainty parameters on optimization process and applying the developed framework for building in different climate. [Display omitted] • An ANN based framework developed for residential building energy performance optimization. • Energy consumption, discomfort hours, and carbon emission optimized simultaneously. • Sensitivity analysis conducted for design and operation parameters. • TOPSIS method applied to suggest optimal point among Pareto front solutions. • Energy consumption, discomfort hours, and carbon emission reduced up to 39.3%, 62.8%, and 40.5%, respectively. [ABSTRACT FROM AUTHOR]

Variable Refrigerant Flow (VRF) systems are becoming increasingly popular in commercial and residential buildings due to their flexibility and efficiency. Building design and operational parameters play an important role in the process of predicting cooling loads, and these variables contain a variety of uncertainties that must be taken into account when seeking to obtain a reliable prediction. It is common practice to oversize the Heating, Ventilation, and Air Conditioning (HVAC) system installed in a building in order to reduce uncertainties in its performance. However, oversizing is undesirable due to additional capital and operating costs, inefficient space utilization, and excessive emissions of greenhouse gases. This study provided a comprehensive literature review on previous uncertainly analysis studies from six perspectives: classification of uncertainty factors, sensitivity analysis methods, building energy model for uncertainty analysis, sampling method, the most effective parameter, and distribution of uncertainty parameters., and then developed a framework to investigate the effects of uncertainty of operational and design parameters on annual cooling load, total energy consumption, HVAC electricity use per conditioned floor area, VRF system cost, and the cooling-to-total electricity consumption ratio (R) in residential buildings equipped with VRF systems located in the Middle East (ME) region (Kuwait City, Tehran, and Istanbul). Artificial Neural Network (ANN) model was also developed to predict output parameters in residential building. Latin Hypercube Sampling (LHS) simulation was also applied to generate near-random samples of uncertainty parameter values from a multidimensional distribution. It should be mentioned that a correlation was derived in this study to predict VRF cost. The study evaluated thirteen uncertainty parameters related to building characteristics, occupants, building energy systems, VRF variables, and lighting conditions. Several methods were used to evaluate the effects of input uncertainty parameters on the output variables, namely, the Standardized Regression Coefficient (SRC), Partial Correlation Coefficient (PCC), and Spearman Rank Correlation Coefficient (SRCC) or Pearson Product-Moment Correlation Coefficient (PPMCC). The results indicated that VRF cost and HVAC electricity use per area have a greater coefficient of variation compared with other output parameters. The density distribution of output parameters indicated that uncertainty parameters had the greatest impact on output parameters in Kuwait (hyper arid climate), whereas in Istanbul (humid subtropical climate), they were the least. Among the variables examined in the Sensitivity Analysis (SA), cooling setpoint had the greatest impact on residential building energy consumption in ME climates. Finally, this paper highlights emerging trends and offers recommendations for advancing future research in the realm of building energy uncertainty analysis. • Comprehensive review of previous uncertainty analysis studies was provided. • VRF systems operational and design parameters effect on cooling load were reviewed. • Investigate an impact of uncertainties on building performance in various climates. • Energy consumption in building was most influenced by cooling setpoints. • Results show a more significant impact of uncertainty in hyper-arid climates. [ABSTRACT FROM AUTHOR]