發(fā)布時間：2018-03-17 11:34

  本文選題：數(shù)據(jù)挖掘　切入點：用電優(yōu)化　出處：《華僑大學》2015年碩士論文　論文類型：學位論文

【摘要】：隨著經(jīng)濟的發(fā)展,社會用電量迅速增長,造成電網(wǎng)容量不足,能源利用率不高,環(huán)境和資源問題也面臨重大挑戰(zhàn)。因此,如何提高用電智能化水平,是提高社會用電效率,達到節(jié)能減排效果的重要措施。如今商用建筑能源消耗越來越大,對其用電進行智能化管理有重大意義。新興商業(yè)建筑安裝了電力能耗監(jiān)控系統(tǒng),所監(jiān)測的數(shù)據(jù)為研究提供了有利條件。數(shù)據(jù)挖掘技術(shù)能夠從海量數(shù)據(jù)中挖掘出有價值的內(nèi)在信息,本文利用數(shù)據(jù)挖掘技術(shù)針對商業(yè)用戶智能用電問題中的負荷預(yù)測和用電優(yōu)化問題進行了研究。首先,在深度分析商業(yè)電力負荷特性的基礎(chǔ)上,采用粗糙集對影響負荷預(yù)測的重要因素進行了約簡,并將小波理論支持向量機相結(jié)合,建立了應(yīng)用于商業(yè)電力負荷預(yù)測的小波支持向量機模型。實例表明,該模型在負荷突變處和波動較大的負荷序列中預(yù)測精度優(yōu)于單一的支持向量機和神經(jīng)網(wǎng)絡(luò)預(yù)測模型。其次,針對商業(yè)用電主要負荷的特性,將其分為可控負荷和固定負荷,對可控負荷中的空調(diào)負荷建立用電成本最小化及舒適溫度為目標的用電優(yōu)化模型,對可控負荷中的可轉(zhuǎn)移負荷建立了負荷轉(zhuǎn)移調(diào)度優(yōu)化模型,分別采用非支配遺傳算法和自適應(yīng)遺傳算法進行尋優(yōu)。結(jié)果表明,該方法在不影響人們用電舒適度的前提下降低了用電成本。最后,采用了C#與MATLAB混合編程技術(shù)開發(fā)了商業(yè)智能管理軟件,并實現(xiàn)了商業(yè)電力負荷的預(yù)測和用電優(yōu)化功能。
[Abstract]:With the development of economy, social electricity consumption is increasing rapidly, resulting in insufficient power grid capacity, low energy utilization ratio, and great challenges to environment and resources. Therefore, how to improve the intelligent level of electricity consumption is to improve the efficiency of power consumption in society. Important measures to achieve the effect of energy saving and emission reduction. Nowadays, commercial buildings are consuming more and more energy, so it is of great significance to intelligently manage their electricity consumption. The monitored data provide favourable conditions for research. Data mining techniques can extract valuable internal information from large amounts of data, In this paper, the data mining technology is used to study the load forecasting and power optimization problems in the intelligent power consumption problem of commercial users. Firstly, based on the in-depth analysis of the load characteristics of commercial electricity, The important factors affecting load forecasting are reduced by rough set, and the wavelet support vector machine is combined to establish the wavelet support vector machine model for commercial power load forecasting. The forecasting accuracy of this model is superior to that of single support vector machine and neural network model in load abrupt change and fluctuating load sequence. Secondly, it is divided into controllable load and fixed load according to the characteristics of main load of commercial electricity consumption. In this paper, the optimization model of power consumption with the aim of minimizing power cost and comfort temperature is established for the air conditioning load in controllable load, and the optimal model for load transfer scheduling is established for the transferable load in controllable load. The results show that the proposed method can reduce the power consumption cost without affecting the power comfort of people. The business intelligence management software is developed by using the mixed programming technology of C # and MATLAB, and the function of forecasting and optimizing the commercial power load is realized.
【學位授予單位】：華僑大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP311.13;TM715

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本文編號：1624563