發(fā)布時(shí)間：2018-07-05 10:26

  本文選題：智能電表 + 能量解聚合��； 參考：《深圳大學(xué)》2017年碩士論文

【摘要】：基于智能電表的數(shù)據(jù)分析是在不增加額外設(shè)備的情況下,不接觸的對(duì)負(fù)載設(shè)備進(jìn)行監(jiān)測(cè),根據(jù)聚合總線上的電表數(shù)據(jù)解聚合出單個(gè)用電器的一種能源解聚合模型。該模型以用電器消耗的功率為特征,結(jié)合用電器的歷史使用習(xí)慣,分析出用電器的當(dāng)前狀態(tài),指導(dǎo)用戶(hù)合理的用電行為,從而節(jié)約電力能源。論文分析了國(guó)內(nèi)外各種已有能量解聚合模型的優(yōu)缺點(diǎn),結(jié)合用電設(shè)備的歷史信息,提出兩種基于智能電表的能量解聚合預(yù)測(cè)模型。1)基于用電器狀態(tài)的因子隱馬爾可夫模型。該方法引入用電器的狀態(tài)來(lái)做能量的解聚合,對(duì)單狀態(tài)用電設(shè)備來(lái)說(shuō),傳統(tǒng)基于單個(gè)用電器的模型和基于用電器狀態(tài)的模型效果相差不大,但在多狀態(tài)用電器的解聚合研究中,基于用電器狀態(tài)的模型整體上具有更高的能量分配準(zhǔn)確率。2)基于時(shí)間劃分的關(guān)聯(lián)規(guī)則學(xué)習(xí)模型。該方法尋找用電器間的相互關(guān)系,結(jié)合用電器的歷史使用習(xí)慣,生成經(jīng)常使用設(shè)備的頻繁項(xiàng)集,訓(xùn)練出具有強(qiáng)關(guān)聯(lián)規(guī)則的模型。該模型對(duì)空閑時(shí)間段的電表數(shù)據(jù)稀釋一定的倍數(shù),降低空閑時(shí)間段的先驗(yàn)信息,在不影響模型解聚合準(zhǔn)確度的條件下,降低生成模型的計(jì)算復(fù)雜度。論文將以上能量解聚合的模型在三種公開(kāi)數(shù)據(jù)集上進(jìn)行對(duì)比實(shí)驗(yàn),采用通用的評(píng)價(jià)標(biāo)準(zhǔn)。整體上,基于用電器狀態(tài)的因子隱馬爾可夫模型比傳統(tǒng)模型的能量準(zhǔn)確分配率高,尤其是在多狀態(tài)用電器的預(yù)測(cè)上。此外,提出的基于時(shí)間劃分的關(guān)聯(lián)規(guī)則學(xué)習(xí)模型具有最小的預(yù)測(cè)誤差、最高的能量分配率和準(zhǔn)確率,解聚合能力優(yōu)于傳統(tǒng)的能量解聚合模型,在小功率用電器、相同功率用電器和具有相同功率用電器組合的解聚合問(wèn)題中同樣適用。
[Abstract]:The data analysis based on intelligent ammeter is a kind of energy depolymerization model of single electric appliance, which is based on the data of ammeter on the aggregation bus, monitoring the load equipment without adding extra equipment. This model is characterized by the power consumption of electrical appliances, combined with the historical usage habits of electrical appliances, analyzes the current state of electrical appliances, instructs users to use electricity rationally, and saves power energy. This paper analyzes the advantages and disadvantages of various existing energy depolymerization models at home and abroad. Combining with the historical information of electrical equipment, two prediction models of energy depolymerization based on intelligent electric meter (1) are proposed. 1) the factor hidden Markov model based on the state of electrical appliances is proposed. This method introduces the state of electrical apparatus to depolymerize the energy. For single-state electric equipment, the effect of traditional model based on single electric appliance and the model based on state of electrical appliance is not different, but in the research of de-aggregation of multi-state electric appliance, The model based on the state of electrical appliance has higher energy allocation accuracy (.2) the learning model of association rules based on time division. Based on the historical usage habits of electrical appliances, the frequent itemsets of frequently used devices are generated, and a model with strong association rules is trained. The model dilutes a certain multiple of the ammeter data in the idle time period, reduces the prior information of the idle time period, and reduces the computational complexity of the generated model without affecting the accuracy of the model depolymerization. In this paper, the above models of energy depolymerization are compared on three kinds of open data sets, and the general evaluation criteria are adopted. On the whole, the factor hidden Markov model based on the state of electrical appliances has a higher accurate energy distribution rate than the traditional model, especially in the prediction of multi-state electrical appliances. In addition, the proposed association rule learning model based on time division has the minimum prediction error, the highest energy distribution rate and accuracy, and the ability to deaggregate is superior to the traditional energy deaggregation model. The same applies to the depolymerization problem of the same power electric appliance and the same power electric appliance combination.
【學(xué)位授予單位】：深圳大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP311.13;TM933.4

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 李增鵬;鄒巖;張磊;馬春光;;一種基于全同態(tài)加密的智能電網(wǎng)數(shù)據(jù)交換隱私保護(hù)方案[J];信息網(wǎng)絡(luò)安全;2016年03期

2 錢(qián)益輝;孫超杰;楊賽女;裘君;;基于Cortex-M0+的四通道多功能智能電表設(shè)計(jì)[J];單片機(jī)與嵌入式系統(tǒng)應(yīng)用;2015年10期

3 徐大青;欒文鵬;王鵬;張展國(guó);;智能電表數(shù)據(jù)分析方法及應(yīng)用[J];供用電;2015年08期

4 王亞?wèn)|;高巖;金鋒;;智能電表數(shù)據(jù)分析及應(yīng)用綜述研究[J];信息技術(shù);2015年02期

5 樊龍;張文愛(ài);;基于Modbus協(xié)議的智能電表數(shù)據(jù)采集傳輸系統(tǒng)的實(shí)現(xiàn)[J];制造業(yè)自動(dòng)化;2014年04期

6 李靜;楊洪耕;;一種用于家用智能電表的負(fù)荷識(shí)別方法[J];現(xiàn)代電力;2013年05期

，

本文編號(hào)：2099902