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Abstract: Building cooling load prediction is one of the key factors in the success of energy-saving measures. Many computational models available in the industry have been developed from either forward or inverse modeling approaches. However, these models usually require extensive computer resources and lengthy computation. This paper discusses the use of the multi-layer perceptron (MLP) model, one of the artificial neural network (ANN) models widely adopted in engineering applications, to estimate the cooling load of a building. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing prestigious commercial building in Hong Kong that houses a mega complex and operates 24h a day. The paper also discusses the practical difficulties encountered in acquiring building-related data. In contrast to other studies that use ANN models to predict building cooling load, this paper includes the building occupancy rate as one of the input parameters used to determine building cooling load. The results demonstrate that the building occupancy rate plays a critical role in building cooling load prediction and significantly improves predictive accuracy. [Copyright &y& Elsevier]

Abstract: In subtropical Hong Kong, buildings consume the most of the electricity use. Computer simulation technique is a useful tool for predicting building thermal and energy performance. Simulation computer programs require weather data input to drive the mathematical models within the simulation tools for comparative studies and annual energy estimation. Typical weather year data representing the long-term climatic conditions are often used. Recently, there has been an increasing interest in incorporating daylight in the architectural and building designs to reduce building energy consumption. However, daylight parameters are not always available. In 2003, the International Commission on Illumination (CIE) adopted a range of 15 standard skies covering probably the whole spectrum of the usual skies in the world. Once the skies have been identified, the basic solar irradiance and daylight illuminance at various inclined planes can be obtained. This paper presents the work on the development and evaluation of two typical weather datasets, namely Typical Meteorological Year (TMY) and Test Reference Year (TRY). Measured hourly weather data for the 30-year period from 1979 to 2008 were analysed. Building energy simulations were conducted to evaluate the two weather datasets. Daylight parameters were derived using luminous efficacy approach and the sky conditions were categorized based on common climatic variables. [Copyright &y& Elsevier]