本文關(guān)鍵詞： 人工神經(jīng)網(wǎng)絡(luò) 建筑熱負荷 預(yù)測 節(jié)能控制　出處：《大連海事大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：目前,在我國北方城鎮(zhèn)的集中供暖大部分是供熱中心或者換熱站直接把熱水送往用戶端,易于造成供熱不均和能耗浪費。雖然部分地區(qū)按國家相關(guān)建議和要求進行了按熱收費、分戶計量的嘗試,但由于分戶計量需對散熱器端的供熱裝置進行改造且一次性投資較大,同時因用戶自主節(jié)能意識還不強,分戶調(diào)節(jié)尚不盡人意。代之以建筑分棟計量及調(diào)節(jié)的供熱控制裝置投資適中,自動化水平及工業(yè)級可靠性程度更高,是調(diào)節(jié)建筑物熱負荷實現(xiàn)供熱用戶端節(jié)能目標(biāo)的有效手段。建筑供熱系統(tǒng)是一個大時滯、大慣性的復(fù)雜系統(tǒng),建筑熱負荷與室外環(huán)境、建筑圍護結(jié)構(gòu)等存在一定程度的非線性關(guān)系,利用機理建模涉及參數(shù)眾多、難度大,預(yù)測結(jié)果也有較大誤差。通過利用人工神經(jīng)網(wǎng)絡(luò)不依賴模型本身的特點和良好的非線性逼近能力,選擇了BP (Back Propagation)與RBF (Radial Basis Function)人工神經(jīng)網(wǎng)絡(luò)的方法,根據(jù)采集到的室外干球溫度、光照、風(fēng)速、室內(nèi)溫度以及時間序列分別對建筑熱負荷進行建模和預(yù)測。對比研究表明RBF神經(jīng)網(wǎng)絡(luò)預(yù)測更穩(wěn)定,均方誤差低于BP神經(jīng)網(wǎng)絡(luò)5.3%,更適合于建筑熱負荷的預(yù)測。在對環(huán)境熱負荷預(yù)測的基礎(chǔ)上,需要為滿足建筑熱需求進行調(diào)節(jié)。通常是靠調(diào)節(jié)電動閥的開度對建筑物供熱管網(wǎng)的熱媒進行量調(diào)節(jié),但這可能因水力失衡導(dǎo)致建筑物內(nèi)的不利回路增加,造成內(nèi)部冷熱不均,嚴(yán)重時造成局部凍塞事故。因此設(shè)計了智能Bang-Bang調(diào)節(jié),即開閥就要將閥門開至設(shè)計開度且開夠一定的時長,保證管網(wǎng)中熱媒以一定的壓力和流速流經(jīng)整棟建筑；關(guān)就可以徹底關(guān)斷,實現(xiàn)供熱節(jié)能間歇；但應(yīng)注意到建筑物內(nèi)的散熱過程依然是連續(xù)的。通過計算開閥時段供給的熱量與預(yù)測的熱需求的差值,可得到滿足工藝約束的關(guān)閥時間。由此形成了獨特的開閥時段固定而關(guān)閥時段動態(tài)的變周期控制模式。將此模式與室內(nèi)舒適性、管網(wǎng)防凍、設(shè)備故障預(yù)防等因素結(jié)合,設(shè)計實現(xiàn)了具有工程可用性的智能控制器。經(jīng)實驗測定,在天氣情況穩(wěn)定及熱源熱網(wǎng)供熱充足的情況下,平均節(jié)能率可達10%,具有良好的社會價值和經(jīng)濟價值。
[Abstract]:At present, the central heating in the northern towns of our country is mostly heating center or heat exchange station to send hot water directly to the user. It is easy to cause uneven heating and waste of energy consumption. Although some regions according to the relevant suggestions and requirements of the country to charge by heat, household metering attempts. But because the household metering needs to reform the heat supply device at the end of the radiator, and the one-time investment is large, at the same time, the consciousness of energy saving of the users is not strong enough. Household regulation is not satisfactory. Instead of building building metering and regulating heating control device investment is moderate and the level of automation and industrial reliability is higher. Building heating system is a complex system with large time delay, large inertia, building heat load and outdoor environment. There is a certain degree of nonlinear relationship between the building envelope structure and so on, so it is difficult to use the mechanism to model the structure with many parameters. The prediction result also has big error, by using the artificial neural network not dependent on the characteristics of the model itself and good nonlinear approximation ability. The methods of BP back propagation and RBF Radial Basis function are selected. According to the collected outdoor dry ball temperature, light, wind speed, indoor temperature and time series, respectively, the building heat load is modeled and forecasted. The comparative study shows that the RBF neural network prediction is more stable. The mean square error is lower than that of BP neural network, which is more suitable for building heat load forecasting. It is usually by adjusting the opening of the electric valve to adjust the heat medium of the building heating network, but this may lead to the increase of the unfavorable loop in the building because of the hydraulic imbalance. Cause internal heat and cold uneven, serious local freezing plug accident. Therefore, the design of intelligent Bang-Bang regulation, that is, the valve will open to the design open to a certain length of time. Ensure that the heat medium in the pipe network flows through the whole building with a certain pressure and velocity; Close can be completely shut off, energy saving intermittent heat supply; It should be noted, however, that the heat dissipation process in the building is still continuous. The difference between the heat supplied during the open valve period and the predicted heat demand is calculated. The closing time to meet the process constraints can be obtained. Thus a unique variable period control mode with fixed opening period and closed valve period is formed. The model is combined with indoor comfort and the pipe network is frostproof. An intelligent controller with engineering availability is designed and realized by combining equipment fault prevention and other factors. The experimental results show that the average energy saving rate can reach 10% under the condition of stable weather condition and sufficient heat supply of heat source and heat network. Have good social value and economic value.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TU111;TP183

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本文編號：1489903