|Author:||Cheung, Chin To|
|Title:||A hybrid simulation approach to evaluate cooling energy consumption for public housings of subtropics|
|Advisors:||Wong, Ling-tim (BSE)|
Mui, Kwok-wai (BSE)
|Subject:||Dwellings -- Energy consumption -- China -- Hong Kong|
Public housing -- China -- Hong Kong
Dwellings -- Energy conservation -- China -- Hong Kong
Hong Kong Polytechnic University -- Dissertations
|Department:||Department of Building Services Engineering|
|Pages:||xxiv, 240 pages : illustrations|
|Abstract:||Cooling energy use in building is marked especially in the subtropical climate region. Cooling demand in residential sector, different from office or commercial buildings, is significantly subject to occupant decisions where indepth investigations are found limited. Electricity consumption assessment is conducted in apartments and communal areas for both public and private housing in Hong Kong. It is found to be associated with the occupant load per apartment (tenant) and the gross floor area per building (communal). Residential electricity demand forecast shows that energy consumptions could be associated with housing types. It can be efficiently lessened by increasing the public housing stock and reducing communal energy use. The Hong Kong public housing sector is being targeted in this study. Surveys have been conducted in existing public housings to understand the housing characteristics, apartment electricity demands, occupant's thermal expectations and airconditioning usage patterns for further cooling energy saving assessments. Despite several simulation programs and mathematical expressions are available for cooling energy prediction, these tools are too sophisticated for layman use and limited to relative small scale simulation which are unsupportive or time consuming for city scale housings energy forecast. A hybrid model, integrated by EnergyPlus (EP) and artificial neural network (ANN), is proposed to simulate the cooling energy demands for public apartments. Advantage of this new hybrid model attributes to its quick response time in predicting cooling electricity use available from individual apartment to entire housing sector. Good agreement on energy prediction of the proposed model is confirmed via peer literatures, government statistics and surveyed public housings.|
The model provides a foundation on cooling energy prediction for apartments in public housings that helps prioritize energy conservation in terms of building material use, construction design, climate change and occupant behaviour in airconditioning needs. Impacts on public residential cooling energy consumption are evaluated regarding to sensitivity of external wall and window material selection, window size reduction, shading extension, building orientation and apartment size control. Significant energy reduction is recorded with material thermal insulation enhancement and a larger stock of medium size flats (3050m2) in the public housing sector. Occupants’ thermal comfort conditions in their living environment are revealed and corresponding thermal comfort zones are established to identify the thermally neutral conditions perceived by occupants for potential cooling energy saving. The cooling energy consumption is specified by optimal comfort temperature setpoint and outdoor temperature variation due to climate change. Besides, home airconditioner operation criteria are studied with respect to occupants’ thermal expectation, socioeconomic group and occupancy schedule. More precise cooling demand, validated by energy use in surveyed households, is confirmed by implementing the updated occupant behavioural airconditioning operation schedule instead of fixed occupancy patterns for simulation in residential buildings. The above findings are integrated into strategies, considering both housing design arrangements and occupants’ thermal comfort behaviours, of cooling energy reduction in public housings. Since cooling demand in public apartments is occupant behavioural dependent, incentive with an example of energy pricing strategy is proposed to reduce cooling electricity use specifically in the summer months from May to October. The achievements are summarized into a cooling energy calculator for layman use to enhance cooling energy saving awareness in their own living. Findings present in this study can be a directory framework for future cooling energy evaluation in residential buildings, especially focus on the occupant behavioural airconditioning operation and criteria of energy saving incentives.
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