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A large part of energy consumption in homes, offices and commercial spaces is related to Heating, Ventilation and Air-conditioning (HVAC) devices. The effective parameter on the consumption of HVAC systems is internal heat gains that arise from occupants, electric equipment and lighting. In order to reduce the energy consumption of these systems, internal heat gains should be predicted accurately. Since there are few investigations performed on the prediction of internal heat gains, in this paper, three predictive models, namely multiple regression model, Levenberg–Marquardt back-propagation (LM-BP) model and similar days method based on combined weights, have been deployed. By assessing the influential factors on internal heat gains, fundamental theories, structures, equations and parameters of these models are thoroughly proposed. To examine the prediction techniques, an office building in China was considered. It was found that all the proposed models have high accuracy; however, the LM-BP neural network showed the most precision among other models with RMSE = 15.59, MAE = 10.16 and MAPE = 6.35. This model had a higher agreement with the actual internal heat gains compared to the predetermined working programs in the ASHRAE standard 90.1. The proposed models used in this study can lead to providing a theoretical base for scholars and engineers to improve the predictive control of HVAC systems, which plays an important role in enhancing thermal comfort, saving energy of residential buildings. [ABSTRACT FROM AUTHOR]

• We selected three benchmarking methods to establish building energy benchmarking models based on a sample of 45 hotel buildings in China. • We compared the three benchmarking methods in terms of consistency, robustness and explanatory ability. • The energy efficiency performance obtained from the three methods were found to be inconsistent. • We came up with suggestions based on comparative analysis for policy-makers to develop building energy benchmarking programs. A reasonable building energy efficiency benchmarking program plays an important role in energy consumption control and supervision. Previous studies have focused on the process of establishing a single benchmarking method, but few have compared the performances and outcomes of different methods. To fill this gap, this paper selects three benchmarking methods—multiple linear regression (MLR) based on Energy Star, stochastic frontier analysis (SFA) and the descriptive statistics method (DSM) based on the national energy consumption standard in China—to develop benchmarking models. We demonstrate each method using data on the energy and building characteristics of 45 four- and five-star hotel buildings located in Chongqing, China. To compare the consistency, robustness and explanatory ability of the three methods, we first utilize the Spearman rank correlation analysis to test whether these methods have consistent energy efficiency ranks and then present Sankey diagrams to further reveal the interactions of the estimated energy efficiency grades obtained from the three methods. It is found that the results of DSM and SFA are most consistent, while MLR vs. SFA and MLR vs. DSM present significant differences in evaluating building energy performance. In addition, DSM is more robust for evaluating the ranks of sampled buildings, while SFA is more robust for evaluating energy efficiency grades. Furthermore, we discuss the explanatory ability of each method. In addition to the building characteristics, the design and operational characteristics of the HVAC system have great effects on building energy consumption. Finally, we present suggestions for policy-makers regarding the development and implementation of the building energy benchmarking program in Chongqing and for the management of buildings with different energy performances to further improve the energy efficiency. [ABSTRACT FROM AUTHOR]