مدل های رگرسیون چندگانه برای استفاده از انرژی در ساختمان های اداری با تهویه مطبوع در اقلیم های مختلف
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|24425||2010||6 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Energy Conversion and Management, Volume 51, Issue 12, December 2010, Pages 2692–2697
An attempt was made to develop multiple regression models for office buildings in the five major climates in China – severe cold, cold, hot summer and cold winter, mild, and hot summer and warm winter. A total of 12 key building design variables were identified through parametric and sensitivity analysis, and considered as inputs in the regression models. The coefficient of determination R2 varies from 0.89 in Harbin to 0.97 in Kunming, indicating that 89–97% of the variations in annual building energy use can be explained by the changes in the 12 parameters. A pseudo-random number generator based on three simple multiplicative congruential generators was employed to generate random designs for evaluation of the regression models. The difference between regression-predicted and DOE-simulated annual building energy use are largely within 10%. It is envisaged that the regression models developed can be used to estimate the likely energy savings/penalty during the initial design stage when different building schemes and design concepts are being considered.
In China, there has been steady increase in the use of energy since the adoption of the Policy of Reforming and Opening in the 1980s, and energy conservation is of vital importance both economically and environmentally , ,  and . It was estimated that buildings stocks accounted for 24.1% of total national energy use in mainland China in 1996, rising to 27.5% in 2001, and is projected to increase to about 35% in 2020  and . Under constant energy efficiency, total annual energy consumption would be around 5000 Mtce (1 Mtce = 29.3 × 106 GJ) in 2020 . With rapid economic growth, there is a growing desire for better indoor built environment, particularly in winter space heating and summer comfort cooling, and it was estimated heating, ventilation and air-conditioning (HVAC) accounted for some 65% of the energy use in the building sector . It is envisaged that the building sector will continue to be a key energy end-user in the years ahead. Office building development is one of the fastest growing areas in the building sector especially in major cities such as Beijing and Shanghai. On a per unit floor area basis, energy use in large office building development with full air-conditioning can be 70–300 kWh/m2, 10–20 times that in residential buildings  and . Because of the climatic diversity in China, the designs of these buildings and their thermal and energy performances could vary a great deal in different climate zones across China . Computer building energy simulation is an acceptable technique for assessing the dynamic interactions between the external climates, the building envelopes and the HVAC systems, and has been playing an important role in the designs and analysis of energy-efficient buildings and the development of performance-based building energy codes , ,  and . In most architectural and engineering design practices, however, full hourly building energy simulations could be costly and time-consuming. Simple estimation models are often preferred, especially during the initial design stage when different design concepts and building schemes are being considered. There is, however, very little work on comparing hour-by-hour simulated building energy consumption with those from simple estimation models for different climates. The primary aim of the present work was, therefore, to develop simple energy estimation models for fully air-conditioned office buildings in major climate zones across China. The work involved four main aspects: (i) Generation of an energy use database through a series of building energy simulation runs for office buildings in major climate zones in China. (ii) Identifying key building design variables using sensitivity analysis technique. (iii) Develop simple energy estimation models as functions of the key design variables using regression technique. (iv) Regression models evaluation.