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Abstract: It is not feasible to apply the traditional prediction method to predict hourly building cooling load at the urban energy planning stage because of the limited building information and complexity of energy prediction. This paper presents a simplified prediction model: Hourly Cooling Load Factor Method (HCLFM) that can provide quick and fair estimate of building cooling load for large-scale urban energy planning. The paper introduces the assumptions and principles of the proposed method, as well as discussing the implication and limitation of the approach. As a verification and demonstration, the method is applied to an office building in Beijing. The predicted results show that the dynamical trend of the cooling load is reasonable. The study further analyzes the potential causes of prediction errors and the significance of various cooling load influence factors. [Copyright &y& Elsevier]

The paper briefly describes energy-efficiency standards that have been issued for residential buildings in Northern and Central China as well as energy-efficiency inspection standards and specifications for renovation of existing residential heating systems in Northern China, and development of energy-efficiency standards for Southern China. The paper also describes barriers to improving the energy efficiency of residential buildings in China and strategies for overcoming these barriers, including reform of the method of payment for heating in Northern China. Finally, future prospects for further developing energy-efficiency standards for residential buildings in China are addressed. [Copyright &y& Elsevier]

Abstract: As a regular data-driven method, Artificial Neural Networks (ANNs) are popular in building energy prediction. In this paper, an alternative approach, namely, hybrid genetic algorithm-adaptive network-based fuzzy inference system (GA-ANFIS) is presented. In this model, GA optimizes the subtractive clustering’s radiuses which help form the rule base, and ANFIS adjusts the premise and consequent parameters to optimize the forecasting performance. a hierarchical structure of ANFIS is also suggested to solve the probably curse-of-dimensionality problem. The performance of the proposed model is compared with ANN using two different data sets, which are collected from the Energy Prediction Shootout I contest and a library building located in Zhejiang University, China. Results show that the hybrid GA-ANFIS model has better performance than ANN in term of prediction accuracy. The proposed model also has the same scale of modeling time as ANN if parameters in GA procedure are carefully selected. It can be regarded as an alternative method in building energy prediction. [Copyright &y& Elsevier]

Abstract: The concern on climate change leads to growing demand for minimization of energy use. As building is one of the largest energy consuming sectors, it is essential to study the impact of climate change on building energy performance. In this regard, building energy simulation software is a useful tool. A set of appropriate typical weather files is one of the key factors towards successful building energy simulation. This paper reports the work of developing a set of weather data files for subtropical Hong Kong, taking into the effect of future climate change. Projected monthly mean climate changes from a selected General Circulation Model for three future periods under two emission scenarios were integrated into an existing typical meteorological year weather file by a morphing method. Through this work, six sets of future weather files for subtropical Hong Kong were produced. A typical office building and a residential flat were modeled using building simulation program EnergyPlus. Hourly building energy simulations were carried out. The simulated results indicate that there will be substantial increase in A/C energy consumption under the impact of future climate change, ranging from 2.6% to 14.3% and from 3.7% to 24% for office building and residential flat, respectively. [Copyright &y& Elsevier]