本文選題：建筑環(huán)境優(yōu)化　切入點：建筑能量預(yù)測　出處：《浙江大學(xué)》2013年博士論文　論文類型：學(xué)位論文

【摘要】：當(dāng)今,能源危機和環(huán)境污染是世界各國面臨的共同挑戰(zhàn)。我國是能源消耗大國,隨著經(jīng)濟的飛速發(fā)展,建筑能耗已占社會總能耗的四分之一以上,且這一比例還在逐年升高。在此背景下,建筑節(jié)能技術(shù)越來越受到重視。從控制學(xué)角度看,建筑能量系統(tǒng)可看作多變量、非線性的復(fù)雜系統(tǒng),建筑節(jié)能目標(biāo)的實現(xiàn)涉及到建筑環(huán)境的優(yōu)化控制、建筑能量的預(yù)測與管理等諸多方面內(nèi)容。本文立足控制學(xué)科,在前人的基礎(chǔ)上,從建筑環(huán)境控制與能量管理等相關(guān)領(lǐng)域入手,進(jìn)行了如下研究工作。 ·出于節(jié)能和室內(nèi)環(huán)境優(yōu)化的目的,暖通空調(diào)系統(tǒng)對熱環(huán)境的控制與優(yōu)化需要室內(nèi)溫度分布的動態(tài)信息幫助決策。通常,計算流體力學(xué)(CFD)模型能提供這樣的精確信息,但是由于其迭代計算繁雜、耗時長,難以滿足實時性要求。本文引入基于本征正交分解(POD)的模型降階技術(shù),與CFD仿真結(jié)合提出一種新的建筑熱環(huán)境建模方法,可同時滿足熱環(huán)境建模的精度和實時性要求。POD模型降階是 種映射方法,配合離散化技術(shù),該方法可將無限維的非線性復(fù)雜系統(tǒng)變?yōu)閮H與POD模式系數(shù)相關(guān)的低階線性系統(tǒng)。具體建模方法為：首先,利用CFD工具對建筑室內(nèi)熱環(huán)境進(jìn)行動態(tài)仿真,在此期間用快照的方式采集動態(tài)溫度場信息；其次,運用有限體積法對能量平衡方程進(jìn)行空間和時間的離散化,并建立離散能量平衡方程的狀態(tài)空間表達(dá)式：然后,運用POD方法對室內(nèi)動態(tài)溫度場進(jìn)行降階,并運用Galerkin映射將高階能量方程投射到降階子空間上,從而得到階數(shù)十以內(nèi)的低階線性系統(tǒng)。在一個二維房間的仿真實驗中,室內(nèi)溫度場的POD降階模型得到與CFD仿真基本一致的瞬、穩(wěn)態(tài)精度,而其階數(shù)低至六階,證明了該方法的有效性。 ·設(shè)計一個基于POD降階模型的室內(nèi)溫度精確控制系統(tǒng)。該溫控系統(tǒng)的特點在于,運用“離線-在線”策略建立了室內(nèi)溫度場的動態(tài)降階模型,以實時反饋熱環(huán)境信息提高溫控精度。降階溫度場的初始狀態(tài)通過一個溫度傳感器和卡爾曼濾波器估計得到。本文分別設(shè)計了單神經(jīng)元自適應(yīng)PID控制器和模型預(yù)測控制器對該方法進(jìn)行仿真驗證。結(jié)果表明,在速度場不變等假設(shè)條件下,該溫控系統(tǒng)可利用室內(nèi)溫度場信息精確控制各區(qū)域溫度,具有提高熱舒適度和降低能耗的潛力。 ·針對目前建筑室內(nèi)環(huán)境的優(yōu)化策略大都忽視環(huán)境參數(shù)空間分布的問題,本文利用POD降階技術(shù)在環(huán)境建模方面的快速性和準(zhǔn)確性,運用多維插值和遺傳算法,設(shè)計一種綜合考慮室內(nèi)熱舒適度、室內(nèi)空氣質(zhì)量(IAQ)及空調(diào)能耗的優(yōu)化控制策略。其中,建筑室內(nèi)環(huán)境參數(shù),包括溫度場、氣流場、CO2濃度分布、及熱舒適度分布等,先通過CFD仿真得到；然后利用POD方法重構(gòu)上述參數(shù)分布的低階變化空間。優(yōu)化方法采用遺傳算法,控制變量包括置換通風(fēng)系統(tǒng)的送風(fēng)溫度和速度。優(yōu)化目標(biāo)涵蓋系統(tǒng)能耗、室內(nèi)熱舒適度、IAQ、以及垂直溫差等。在遺傳算法的每次優(yōu)化迭代中,通過POD參數(shù)空間內(nèi)的多維插值快速求解候選控制變量對應(yīng)的系統(tǒng)響應(yīng),確保了優(yōu)化算法的實時性。一個辦公室環(huán)境的優(yōu)化仿真證實該方法的有效性。 ·作為典型的數(shù)據(jù)驅(qū)動建模方法,在過去20年間,人工神經(jīng)網(wǎng)絡(luò)在建筑能耗預(yù)測領(lǐng)域應(yīng)用廣泛。本文結(jié)合自適應(yīng)模糊推理系統(tǒng)(ANFIS)和遺傳算法的各自特點提出一種新的建筑能耗預(yù)測方法,即GA-ANFIS方法。其中,ANFIS通過訓(xùn)練輸入/輸出數(shù)據(jù)自適應(yīng)調(diào)整T-S模糊系統(tǒng)的隸屬度函數(shù)參數(shù)和結(jié)論參數(shù),遺傳算法則對ANFIS中的模糊規(guī)則參數(shù)進(jìn)行優(yōu)化以幫助構(gòu)造最優(yōu)規(guī)則基。設(shè)計ANFIS的分級結(jié)構(gòu)用于應(yīng)對輸入變量過多造成的維數(shù)災(zāi)難問題。利用美國采暖、制冷與空調(diào)工程師學(xué)會(ASHRAE)提供的建筑能耗數(shù)據(jù)對該方法進(jìn)行驗證。結(jié)果表明,該方法與神經(jīng)網(wǎng)絡(luò)方法相比其建模時間在同一尺度內(nèi),而預(yù)測精度最多可提高20%。 ·本文利用GA-ANFIS方法分別對玉泉圖書館和杭州某酒店的電力能耗作預(yù)測實驗。能耗數(shù)據(jù)均由浙大中控能耗監(jiān)控系統(tǒng)實時采集,氣象數(shù)據(jù)來自浙江省氣象局官方資料。實驗結(jié)果驗證了仿真結(jié)論,GA-ANFIS方法可結(jié)合目前的建筑能量采集系統(tǒng),應(yīng)用于建筑未來能耗的預(yù)測與分析。
[Abstract]:Nowadays, energy crisis and environmental pollution are common challenges facing the world. China is a big energy consuming country, with the rapid development of economy, the building energy consumption has accounted for 1/4 of the total social energy consumption, and the proportion is increasing year by year. Under this background, building energy-saving technology has attracted more and more attention from the control point of view. That building energy system can be seen as a multi variable, nonlinear complex system, to achieve the goal of building energy-saving control relates to the optimization of building environment, many aspects of building energy prediction and management. Based on the control subjects, on the basis of previous studies, starting from the construction of environmental control and energy management and other related fields, are as follows the research work.
And for the purpose of saving energy and optimizing indoor environment, HVAC system control and optimization of thermal environment dynamic information of indoor temperature distribution decision-making. Usually, the computational fluid dynamics (CFD) model can provide accurate information to this, but because of the iterative calculation of complicated, time-consuming, difficult to meet the real-time requirements. This paper introduced based on the proper orthogonal decomposition (POD) reduction technique combined with CFD simulation model, put forward a new modeling method for building thermal environment, thermal environment modeling can meet the accuracy and real-time requirements of model reduction is.POD
A mapping method, with the discretization technique, this method can get nonlinear infinite dimensional complex system is only related to POD mode coefficients of low order linear system. The modeling method is as follows: firstly, the simulation of indoor thermal environment by using CFD tools, with a snapshot of the way of collecting information of dynamic temperature field during this period; secondly, the energy balance of discrete space and time equation using the finite volume method, and the discrete energy balance equation of state space expression. Then, using POD method to reduce the order of dynamic indoor temperature field, and using the Galerkin mapping of high order equation of energy reduction is projected onto the subspace, resulting in a low order linear system. In order for within a two-dimensional simulation room, indoor temperature field of POD reduced order model consistent with the CFD simulation of transient and steady-state accuracy, and its order The validity of the method is proved by the low to six order.
Design a reduced order model based on POD indoor temperature control system. The precise features of temperature control system, the use of "offline - online" strategy to establish a dynamic indoor temperature field of the reduced order model, real-time feedback information to improve the accuracy of temperature. The thermal environment reduced the initial state order temperature by a temperature sensor and Calman filter estimated. This paper designs a single neuron adaptive PID controller and model predictive controller to verify the method. The results show that the velocity field is invariant under assumed conditions, the control system can use the information of indoor temperature field accurately control the temperature of the area, can improve thermal comfort and reduce energy consumption potential.
For most of the optimization strategy of the construction of the indoor environment neglect environment parameters of spatial distribution, rapidity and accuracy of the POD reduction technique in environmental modeling, using a multidimensional interpolation algorithm and genetic algorithm, design a comprehensive consideration of the indoor thermal comfort, indoor air quality (IAQ) and the energy optimal control strategy. Among them, the indoor environment parameters, including temperature field, flow field, concentration distribution of CO2, and the thermal comfort distribution, first obtained by CFD simulation; and then use the POD method to reconstruct the distribution parameters of the low order change space. Using genetic algorithm optimization method, the control variables include wind speed and temperature to displacement ventilation system the optimization goal of covering the system energy consumption, indoor thermal comfort, IAQ, and the vertical temperature difference. In each iterative optimization of genetic algorithm, the POD parameter space multidimensional interpolation fast The response of the candidate control variable is solved to ensure the real-time performance of the optimization algorithm. The optimization simulation of an office environment proves the effectiveness of the method.
As a typical data-driven modeling method, in the past 20 years, artificial neural network is widely used in the prediction of the energy consumption of the building. This paper combines adaptive fuzzy inference system (ANFIS) characteristics of genetic algorithm and proposes a new building energy consumption prediction method, namely GA-ANFIS method. Among them, ANFIS through membership functions and conclusion the training parameters of input / output data adaptive T-S fuzzy system, genetic algorithm for the parameters of the fuzzy rules in ANFIS are optimized to help construct the optimal rule base. The dimension disaster response caused by excessive input variables for a hierarchical structure design of ANFIS. Using the heating, refrigeration and Air Conditioning Engineers (ASHRAE) to verify the the method of building energy consumption data. The results show that this method and neural network method compared with the modeling time in the same scale, and the prediction accuracy The maximum can be improved by 20%.
In this paper, using GA-ANFIS method of power consumption of the Jade Spring respectively the library and a hotel in Hangzhou for the prediction experiments. The energy consumption data by SUPCON real-time energy consumption monitoring system, meteorological data from Zhejiang Provincial Meteorological Bureau official data. Experimental results verify the simulation results, the GA-ANFIS method can be combined with the building energy acquisition system at present, forecast and analysis the future application in building energy consumption.

【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2013
【分類號】：TU111.195

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相關(guān)期刊論文 前1條

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本文編號：1644887