دانلود مقاله ISI انگلیسی شماره 6340
عنوان فارسی مقاله

مدل های رگرسیونی برای پیش بینی مصرف انرژی ساختمان اداری انگلستان از تقاضاهای گرمایش و سرمایش

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
6340 2013 14 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
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عنوان انگلیسی
Regression models for predicting UK office building energy consumption from heating and cooling demands
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Energy and Buildings, Volume 59, April 2013, Pages 214–227

کلمات کلیدی
- ساختمان های اداری انگلستان - سیستم های تهویه مطبوع - مدل های رگرسیونی - پارامترها - عملکرد انرژی
پیش نمایش مقاله
پیش نمایش مقاله مدل های رگرسیونی برای پیش بینی مصرف انرژی ساختمان اداری انگلستان از تقاضاهای گرمایش و سرمایش

چکیده انگلیسی

This paper described the development of regression models which are able to predict office building annual heating, cooling and auxiliary energy requirements for different HVAC systems as a function of office building heating and cooling demands. In order to represent the office building stock, a large number of building parameters were explored such as built forms, fabrics, glazing levels and orientation. Selected parameters were combined into a large set of office building models (3840 in total). As different HVAC systems have different energy requirements when responding to same building demands, each of the 3840 models were further coupled with five HVAC systems: VAV, CAV, fan-coil system with dedicated air (FC), and two chilled ceiling systems with dedicated air, radiator heating and either embedded pipes (EMB) or exposed aluminium panels (ALU). In total 23,040 possible scenarios were created and simulated using EnergyPlus software. The annual heating and cooling demands and their HVAC system's heating, cooling and auxiliary energy requirements were normalised per floor area and fitted to two groups of statistical models. Outputs from the regression analysis were evaluated by inspecting models best fit parameter values and goodness of fit. Based on the described analysis, the specific regression models were recommended.

مقدمه انگلیسی

HVAC system is usually divided into two parts, primary HVAC system and secondary HVAC system. Primary HVAC system is composed of equipment such as boilers and chillers, which generates heating/cooling energy (Qh, Qc) from primary fuels and electricity. Heating/cooling energy is then distributed in a building by a secondary HVAC system in respond to the building's heating/cooling demand. During this process, secondary HVAC system requires additional energy input, i.e. auxiliary energy (Qa), to operate mechanical components of the system such as pumps, fans and control gears. Building heating/cooling demand is the amount of energy required to maintain desired indoor conditions. It is calculated by taking into account its heat gains and heat losses such as transmission heat gains/losses through building envelope elements, solar heat gains through fenestration areas, internal heat gains from occupant, artificial lighting and electrical equipment, infiltration air heat gains/losses, and fresh air ventilation heat gains/losses. Building heating/cooling demand depends on various building parameters such as building fabrics, glazing percentage and glazing properties, occupancy pattern, level of internal gains, etc. Although heating/cooling demand calculation is often used in practice for building's energy performance evaluation, it unnecessarily reflects the actual energy consumption of the building in response to heating/cooling demand. This is because different HVAC systems have different energy requirements when responding to the same building heating/cooling demand. Such behaviour is predominantly affected by the way a particular HVAC system is designed and operated to match the characteristics of the building. In theory, an ideal HVAC system must meet the following criteria [1] in addition to the usual requirement for minimising circulation cost of the heating/cooling media:

نتیجه گیری انگلیسی

This paper described the development of regression models which are able to predict, with a high level of accuracy, office building annual heating, cooling and auxiliary energy requirements for different HVAC systems as a function of office building heating and cooling demands. Building heating and cooling demands were chosen as input parameters as they are relatively easy to calculate at various stages of building design or refurbishment project. In order to represent the office building stock as accurately as possible, a large number of building parameters were explored in this study. Four building built forms were coupled with five building fabrics and three levels of glazing. Building orientation was also varied in 45° intervals. In addition, two measures of reducing solar gains, overhangs and reflective coating, were considered as well as implementation of daylight control. Selected built forms, insulation levels, glazing percentages, etc. were combined into a large set of office building models (3840 in total). As different HVAC systems have different energy requirements when responding to same building heating and cooling demands, each of the 3840 office building models were further coupled with five HVAC systems: variable air volume system (VAV), constant air volume system (CAV), fan-coil system with dedicated air (FC), chilled ceiling system with embedded pipes, dedicated air and radiator heating (EMB), and chilled ceiling system with exposed aluminium panels, dedicated air and radiator heating (ALU). In total 23,040 possible scenarios were created and the annual office building heating and cooling demands and their HVAC system's heating, cooling and auxiliary energy requirements were calculated using EnergyPlus building simulation software. These results were normalised per meter square and fitted to two groups of statistical models. The first group included models based on the single independent variable, which was either building cooling demand or building heating demand. The second group was composed of models with two independent variables: heating and cooling demands. Outputs from the regression analysis were evaluated by inspecting models best fit parameter values and goodness of fit. Based on the described analysis, the specific regression models were recommended.

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