本文選題：建筑分類　切入點(diǎn)：標(biāo)準(zhǔn)建筑　出處：《湖南大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：建筑節(jié)能是我國可持續(xù)發(fā)展戰(zhàn)略的重要組成部分。隨著我國城鎮(zhèn)化的快速發(fā)展，以小區(qū)形式進(jìn)行規(guī)劃建設(shè)并建有區(qū)域供冷供熱系統(tǒng)的建筑群越來越多，關(guān)注建筑區(qū)域的能耗已是建筑節(jié)能的重要內(nèi)容之一。在區(qū)域供冷供熱系統(tǒng)設(shè)計(jì)過程中，需要在缺乏詳細(xì)建筑信息的規(guī)劃階段，對區(qū)域建筑群總冷熱負(fù)荷進(jìn)行較為準(zhǔn)確地預(yù)測，用以指導(dǎo)能源規(guī)劃、方案設(shè)計(jì)和產(chǎn)品開發(fā)等。現(xiàn)有的建筑負(fù)荷預(yù)測方法主要是針對單體建筑，而區(qū)域建筑負(fù)荷預(yù)測方法研究相對較少，預(yù)測精確度不高。本文提出一種以計(jì)算機(jī)模擬與統(tǒng)計(jì)回歸相結(jié)合的方法，建立預(yù)測模型，用以預(yù)測區(qū)域建筑群冷熱負(fù)荷。 本文首先分析了已有區(qū)域建筑負(fù)荷預(yù)測方法及其局限性。然后，分類分析了建筑冷熱負(fù)荷影響因素，總結(jié)出各類影響因素的衡量指標(biāo)；對區(qū)域建筑進(jìn)行分類，確定了不同類型建筑的標(biāo)準(zhǔn)建筑模型類型；利用影響因素衡量指標(biāo)，建立各類標(biāo)準(zhǔn)建筑模型并提出了模型簡化原則。通過調(diào)研選取適當(dāng)因素水平，利用正交試驗(yàn)確定各類標(biāo)準(zhǔn)建筑模型樣本量。進(jìn)而，采用DesignBuilder能耗模擬軟件對標(biāo)準(zhǔn)建筑進(jìn)行負(fù)荷模擬，，獲得了各類標(biāo)準(zhǔn)建筑的動(dòng)態(tài)負(fù)荷。 以獲得的各類標(biāo)準(zhǔn)建筑動(dòng)態(tài)負(fù)荷當(dāng)作為先驗(yàn)信息，以調(diào)查的其他區(qū)域負(fù)荷統(tǒng)計(jì)值作為樣本信息，建立Bayesian回歸模型，求解得到后驗(yàn)信息，以此作為負(fù)荷預(yù)測因子。以某地區(qū)一示范區(qū)為例，利用通常的簡單面積疊加模型和Bayesian回歸模型計(jì)算夏季典型日該區(qū)域逐時(shí)冷負(fù)荷，再對兩種模型預(yù)測結(jié)果與實(shí)測值進(jìn)行對比。利用逐時(shí)相對誤差、均方根相對誤差和最大誤差比三個(gè)指標(biāo)評(píng)價(jià)了二種模型預(yù)測精度。 二種模型實(shí)例預(yù)測對比結(jié)果表明，Bayesian回歸預(yù)測模型預(yù)測結(jié)果的三類誤差指標(biāo)都小于簡單面積疊加模型預(yù)測結(jié)果，表明Bayesian回歸預(yù)測模型的有效性和具有更好的預(yù)測準(zhǔn)確度。可以認(rèn)為，在區(qū)域建筑群用能規(guī)劃階段，為了獲得更好的預(yù)測精確度，采用計(jì)算機(jī)模擬與統(tǒng)計(jì)學(xué)相結(jié)合的方法比通常的簡單面積擴(kuò)展的方法更好。
[Abstract]:Building energy saving is an important part of the sustainable development strategy of our country. With the rapid development of urbanization in our country, more and more buildings are planned and built in the form of residential area and have regional cooling and heating system. Paying attention to energy consumption of building area is one of the important contents of building energy saving. In the design process of district cooling and heating system, it is necessary to forecast the total cooling and heat load of regional building group accurately in the planning stage of lack of detailed building information. It is used to guide energy planning, project design and product development. The existing building load forecasting methods are mainly for individual buildings, while the regional building load forecasting methods are relatively few. This paper presents a method of combining computer simulation with statistical regression to establish a prediction model to predict the cooling and heat load of regional buildings. This paper first analyzes the existing regional building load forecasting methods and their limitations. Then, classifies and analyzes the factors affecting the building cooling and heat load, summarizes the measurement indicators of various factors, and classifies the regional buildings. The types of standard building models of different types of buildings are determined, and various kinds of standard building models are established and simplified principles are put forward by using the measurement index of influencing factors. The appropriate factor level is selected through investigation and research. The orthogonal test is used to determine the sample size of all kinds of standard building models. Furthermore, the load simulation of standard building is carried out by using DesignBuilder energy consumption simulation software, and the dynamic load of all kinds of standard building is obtained. Taking all kinds of standard building dynamic loads as prior information and other regional load statistics as sample information, the Bayesian regression model is established, and the posteriori information is obtained. Taking a demonstration area as an example, a simple area superposition model and Bayesian regression model are used to calculate the hourly cooling load in a typical summer day. Then the prediction results of the two models are compared with the measured values, and the prediction accuracy of the two models is evaluated by using three indexes: time-by-hour relative error, RMS relative error and maximum error ratio. The comparison of two models shows that the three kinds of error indexes of the prediction results of Bayesian regression model are all smaller than those of the simple area superposition model. The results show that the Bayesian regression prediction model is effective and has better prediction accuracy. It can be concluded that in order to obtain better prediction accuracy in the energy use planning stage of regional buildings, The combination of computer simulation and statistics is better than the usual simple area expansion method.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TU831

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