大型商場建筑夏季冷負荷動態(tài)預測模型

李慧 段培永 劉鳳英

摘 要：夏季建筑冷負荷的正確預測是實現(xiàn)大型復雜中央空調(diào)優(yōu)化運行、節(jié)能降耗的關(guān)鍵。筆者探討了商場建筑冷負荷的主要影響因素，確定了建筑動態(tài)冷負荷預測模型的輸入，提出了夏季基于新風機組供電頻率的商場顧客率間接測量方法，解決了商場內(nèi)顧客量難以檢測的難題。還提出了AFCHCMAC神經(jīng)網(wǎng)絡(luò)預測模型算法，實現(xiàn)了大型商場建筑冷負荷的動態(tài)預測。仿真結(jié)果表明：顧客率在商場冷負荷預測中占有重要地位，在冷負荷預測模型中增加商場顧客率可顯著提高預測精度；AFCHCMAC神經(jīng)網(wǎng)絡(luò)預測算法與傳統(tǒng)的HCMAC神經(jīng)網(wǎng)絡(luò)算法比較，可有效降低神經(jīng)網(wǎng)絡(luò)節(jié)點數(shù)，提高預測精度。

關(guān)鍵詞：冷負荷；動態(tài)預測；模糊聚類；數(shù)據(jù)

中圖分類號：TU111.3

文獻標志碼：A 文章編號：16744764（2016）02010407

Abstract： The accurate energy consumption perdition for building is critical to improve the energy efficient of the operation of the operation of largescale central air conditioning system in summer. Firstly， the influencing factors of cooling load were identified to determine the inputs of cooling load predition model. Then， the indirect measurement method was proposed to obtain the shopper rate based on the supply frequencies of new wind8units to identify the custom number in summer. Last， an AFCHCMAC neural network algorithm is proposed to for dynamic cooling load prediction. The results show that compared with the traditional HCMAC algorithm， the proposed AFCHCMAC algorithm can effectively reduce the neural network nodes and improve the prediction accuracy. The shoppers rate plays an important role in the cooling load prediction for shopping mall. Increasing shopper rate in the inputs of prediction model can significantly improve the prediction accuracy of dynamical cooling load forecasting for shopping mall.

Keywords：cooling load； dynamical prediction； fuzzy clustering； data

隨著中國社會經(jīng)濟的快速發(fā)展，能源供需矛盾和環(huán)境壓力日益突出，目前，建筑運行能耗約占全社會總能耗的30%，單位建筑能耗面積是發(fā)達國家的2～3倍[1]，對社會造成了沉重的能源負擔和嚴重的環(huán)境污染，已成為制約中國可持續(xù)發(fā)展的主要問題。在所有建筑中，大型商場建筑對舒適性要求高，空調(diào)系統(tǒng)運行時間長，其空調(diào)系統(tǒng)單位建筑的能耗為城鎮(zhèn)建筑能耗的5倍 [2]。因此，研究大型商場建筑復雜中央空調(diào)系統(tǒng)的優(yōu)化運行，實現(xiàn)節(jié)能降耗具有重要的經(jīng)濟效益和社會意義，而正確預測商場建筑的冷負荷，根據(jù)用戶的需要提供冷量是實現(xiàn)大型復雜中央空調(diào)優(yōu)化運行、節(jié)能降耗的關(guān)鍵。……