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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 today have been developed from either forward or inverse modeling approaches. However, most of these models require extensive computer resources and involve lengthy computation. This paper discusses the use of data-driven intelligent approaches, a probabilistic entropy-based neural (PENN) model to predict the cooling load of a building. Although it is common knowledge that the presence and activity of building occupants have a significant impact on the required cooling load of buildings, practices currently adopted in modeling the presence and activity of people in buildings do not reflect the complexity of the impact occupants have on building cooling load. In contrast to previous artificial neural network (ANN) models, most of which employ a fixed schedule or historic load data to represent building occupancy in simulating building cooling load, this paper introduces two input parameters, dynamic occupancy area and rate and uses it to mimic building cooling load. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing grade A mega office buildings in Hong Kong with tenants including many multi-national financial companies that require 24-h air conditioning seven days a week. The dynamic changes that occur in the occupancy of these buildings therefore make it very difficult to forecast building cooling load by means of a fixed time schedule. The performance of simulation results demonstrate that building occupancy data play a critical role in building cooling load prediction and that their use significantly improves the predictive accuracy of cooling load models. [Copyright &y& Elsevier]

Abstract: In Hong Kong, conventional air-conditioning (AC) systems cause a high energy consumption and poor indoor thermal comfort. This paper investigated the AC load profiles of three typical local commercial buildings, i.e. office, hotel and retail, and examined the energy consumption of liquid desiccant cooling system (LDCS) for these buildings. Simulation results showed that the cooling load contributed to more than 98% of total annual load, with high latent load proportion from March to September. The high reheating ratio, i.e. from 60% to 90%, is found to be the main reason for the poor thermal comfort. Therefore, the LDCS which could handle sensible and latent loads separately was proposed. However, the electricity-driven LDCS was found not suitable for local commercial buildings. significant energy saving could be achieved by the solar-assisted LDCS system with thermal storage, when the installation area for solar collectors is larger than the minimum required one, i.e. 9.7m2/(kWm−2) for office and 12.7m2/(kWm−2) for hotel buildings. Energy saving of different system types with different installation areas was also studied. The solar-assisted LDCS shows more energy saving in winter but less in summer. Results and conclusions can be useful for researchers and local engineers. [Copyright &y& Elsevier]

Abstract: The ground-coupled heat pump (GCHP) system is becoming attractive for air-conditioning in some moderate-weather regions due to its high energy efficiency and reliable operation capability. However, when the technology is used in buildings where there is only cooling load in hot-weather areas like Hong Kong, the heat rejected into the ground by the GCHP systems will accumulate around the ground heat exchangers (GHE). This heat accumulation will result in degradation of system performance and increment of system operating costs. This problem can be resolved by using the hybrid ground-coupled heat pump (HGCHP) system, which uses supplemental heat rejecters to reject the accumulated heat. This paper presents a practical hourly simulation model of the HGCHP system by modeling the heat transfer process of the system’s main components. The computer program based on this hourly simulation model can be used to calculate the hour-by-hour operation data of the HGCHP system. As a case study, both a HGCHP system and a traditional GCHP system are designed for a hypothetic private residential building located in Hong Kong, and the economic comparisons are conducted between these two types of systems. The simulation results show that the HGCHP system can effectively solve the heat accumulation problem and reduce both the initial costs and operating costs of the air-conditioning system in the building. [Copyright &y& Elsevier]