Building-mix optimization in district cooling system implementation

Chow, Tommy W. S.; Chan, Alfred; Song, Chuankai

doi:10.1016/s0306-2619(03)00102-8

Cited by 64 publications

(21 citation statements)

References 4 publications

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“…To simplify the answers, this study first individually summarized the typical AC on/off modes ( Figure 4). Note that because the respondents 13 could choose multiple modes, the total number of on/off modes can be greater than the number of respondents. Figure 4 shows that the behavior mode "on when feeling hot" was the most common and received 298 votes.…”

Section: Typical Modes Of Occupant Behavior Typesmentioning

confidence: 99%

“…They recommended that typical uniform schedules should be simple and limited to one or a few sets of schedules for practical engineering purposes. In addition, Chow [13] and Gang [14] considered different schedules of serveral building types (e.g., office, school, hotel) for cooling load prediction in their studies, and analyzed the system performance based on predicted loads.…”

Section: Introductionmentioning

confidence: 99%

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A novel stochastic modeling method to simulate cooling loads in residential districts

Yan

Hong

et al. 2017

Applied Energy

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District cooling systems are widely used in urban residential communities in China. Most district cooling systems are oversized; this leads to wasted investment and low operational efficiency and thus energy wastage. The accurate prediction of district cooling loads that supports rightsizing cooling plant equipment remains a challenge. This study developed a new stochastic modeling method that includes (1) six prototype house models representing a majority of apartments in the district, (2) occupant behavior models in residential buildings reflecting the temporal and spatial diversity and complexity based on a large-scale residential survey in China, and (3) a stochastic sampling process to represent all apartments and occupants in the district. The stochastic method was employed in a case study using the DeST simulation engine to simulate the cooling loads of a real residential district in Wuhan, China. The simulation results agree well with the actual measurement data based on five performance metrics representing the aggregated cooling loads, the peak cooling loads as well as the spatial load distribution, and the load profiles. Two currently used simulation methods were also employed to simulate the district cooling loads. The simulation results showed that oversimplified occupant behavior assumptions lead to significant overestimations of the peak cooling load and total district cooling loads. Future work will aim to simplify the workflow and data requirements of the stochastic method to enable its practical application as well as explore its application in predicting district heating loads and in commercial or mixed-use districts.

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Section: Typical Modes Of Occupant Behavior Typesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel stochastic modeling method to simulate cooling loads in residential districts

Yan

Hong

et al. 2017

Applied Energy

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“…A TRNSYS model is built to investigate a similar question [10] for a case study in Hong Kong; storage is not found to be an economically attractive option there. A genetic algorithm is used [11] to assess the optimum shares of five building types to form a cooling load time series that is most suitable for district cooling. This approach was later embedded in an optimization scheme for a development project in South East Kowloon, Hong Kong.…”

Section: District Coolingmentioning

confidence: 99%

District cooling network optimization with redundancy constraints in Singapore

Dorfner

Krystallas

Durst

et al. 2017

Future Cities and Environment

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This study presents a mathematical optimization model for planning topology and capacity of a district cooling network. The model relies on a mixed-integer linear programming formulation to find the most economic network layout while satisfying redundancy criteria against unavailability of cooling stations. The simplicity of the formulation makes it easy to embed in other models or to extend it to other redundancy cases. The model is applied to a case study in the central business district of Singapore. Results show that district cooling is a profitable option for Singapore, especially due to its constant high cooling demand that is currently satisfied mainly through decentralized cooling units. This result generalizes to tropical cities world-wide with high cooling demand density.

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“…They were an office building, a restaurant and a shopping center. Figure-4 shows the cooling load profiles collected from three real buildings in Hong Kong [36][37]. It is observed that these buildings had different cooling load features.…”

Section: Cooling Water Supply Temperature Controlmentioning

confidence: 99%