基于分布式存儲(chǔ)與計(jì)算平臺(tái)的用電量預(yù)測(cè)研究
發(fā)布時(shí)間：2019-06-29 14:13
【摘要】：隨著SG-ERP和智能電網(wǎng)建設(shè)的開展和深入,電網(wǎng)業(yè)務(wù)數(shù)據(jù),以幾何級(jí)增長(zhǎng)的速度在增長(zhǎng),數(shù)據(jù)來源更加復(fù)雜和多樣。如何充分利用應(yīng)用這些巨量的多樣化數(shù)據(jù),對(duì)其進(jìn)行深入分析以便提供大量的高附加值服務(wù)迫在眉睫。因此,本文以《國(guó)網(wǎng)信通部關(guān)于開展2014年大數(shù)據(jù)應(yīng)用試點(diǎn)研究工作的通知》為指導(dǎo),在湖南省電力公司開展大數(shù)據(jù)工作,基于公司售電量、全社會(huì)用電量、各產(chǎn)業(yè)用電量、行業(yè)用電量等關(guān)鍵指標(biāo)數(shù)據(jù),結(jié)合季節(jié)變化、自然增長(zhǎng)等外部因素,利用大數(shù)據(jù)相關(guān)技術(shù),建立用電預(yù)測(cè)分析模型,開展未來用電走勢(shì)分析,提高統(tǒng)計(jì)分析的及時(shí)性和準(zhǔn)確性,為公司運(yùn)營(yíng)管理提供決策支撐。本文首先介紹了課題的研究背景、意義,梳理了國(guó)內(nèi)外關(guān)于分布式存儲(chǔ)與計(jì)算和用電量分析預(yù)測(cè)的現(xiàn)狀,然后結(jié)合湖南省實(shí)際情況,提出了論文需解決的主要問題和組織架構(gòu)。其次研究學(xué)習(xí)了分布式存儲(chǔ)與計(jì)算平臺(tái)的相關(guān)技術(shù),比如Hadoop、HDFS、HBase、Hive、Ganglia、Sqoop,為課題的進(jìn)一步研究提供了理論基礎(chǔ)。接著對(duì)分布式存儲(chǔ)與計(jì)算平臺(tái)進(jìn)行設(shè)計(jì)與實(shí)現(xiàn),包括平臺(tái)技術(shù)架構(gòu)設(shè)計(jì)、物理部署以及管理模塊設(shè)計(jì)與實(shí)現(xiàn)。之后分析了當(dāng)前用電量預(yù)測(cè)問題,并在分布式存儲(chǔ)與計(jì)算平臺(tái)的基礎(chǔ)上,提出了基于無模型自適應(yīng)迭代學(xué)習(xí)控制的年、月用電量預(yù)測(cè)模型,基于MapReduce的遺傳算法優(yōu)化神經(jīng)網(wǎng)絡(luò)的短期用電量預(yù)測(cè)模型,并進(jìn)行實(shí)驗(yàn)驗(yàn)證,結(jié)果表明本文提出的兩種預(yù)測(cè)模型均能夠更快、更精確的對(duì)未來用電量走勢(shì)進(jìn)行預(yù)測(cè)。最后在分布式存儲(chǔ)與計(jì)算平臺(tái)的基礎(chǔ)上,進(jìn)行了用電量預(yù)測(cè)系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn),包括系統(tǒng)功能設(shè)計(jì)、架構(gòu)設(shè)計(jì)、核心界面實(shí)現(xiàn)以及系統(tǒng)在湖南省電力公司應(yīng)用后的效果。
[Abstract]:With the development and deepening of SG-ERP and smart grid construction, the business data of power grid is growing at the rate of geometric growth, and the data sources are more complex and diverse. It is urgent to make full use of these huge amounts of diversified data and analyze them in order to provide a large number of high value-added services. Therefore, under the guidance of the Circular of the Ministry of Information and Communications of the National Network on the Development of big data's Application pilot Research work in 2014, this paper carries out big data work in Hunan Electric Power Company. Based on the key index data such as electricity sales, electricity consumption in the whole society, electricity consumption in each industry, electricity consumption in the industry, combined with seasonal changes, natural growth and other external factors, this paper uses big data related technology to establish a forecast and analysis model of electricity consumption. Carry out the analysis of future power consumption trend, improve the timeliness and accuracy of statistical analysis, and provide decision-making support for the operation and management of the company. This paper first introduces the research background and significance of the subject, combs the present situation of distributed storage and calculation and electricity consumption analysis and prediction at home and abroad, and then puts forward the main problems and organizational structure to be solved according to the actual situation in Hunan Province. Secondly, the related technologies of distributed storage and computing platform are studied, such as Hadoop,HDFS,HBase,Hive,Ganglia,Sqoop, which provides a theoretical basis for the further research of the subject. Then the distributed storage and computing platform is designed and implemented, including the platform technical architecture design, physical deployment and management module design and implementation. Then the current power consumption prediction problem is analyzed, and on the basis of distributed storage and computing platform, the annual and monthly power consumption prediction model based on model-free adaptive iterative learning control and the genetic algorithm based on MapReduce are proposed to optimize the short-term power consumption prediction model of neural network, and the experimental results show that the two prediction models proposed in this paper can predict the future electricity consumption trend faster and more accurately. Finally, on the basis of distributed storage and computing platform, the design and implementation of power consumption prediction system are carried out, including system function design, architecture design, core interface implementation and the effect of the system in Hunan Electric Power Company.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TM73
本文編號(hào)：2507876
[Abstract]:With the development and deepening of SG-ERP and smart grid construction, the business data of power grid is growing at the rate of geometric growth, and the data sources are more complex and diverse. It is urgent to make full use of these huge amounts of diversified data and analyze them in order to provide a large number of high value-added services. Therefore, under the guidance of the Circular of the Ministry of Information and Communications of the National Network on the Development of big data's Application pilot Research work in 2014, this paper carries out big data work in Hunan Electric Power Company. Based on the key index data such as electricity sales, electricity consumption in the whole society, electricity consumption in each industry, electricity consumption in the industry, combined with seasonal changes, natural growth and other external factors, this paper uses big data related technology to establish a forecast and analysis model of electricity consumption. Carry out the analysis of future power consumption trend, improve the timeliness and accuracy of statistical analysis, and provide decision-making support for the operation and management of the company. This paper first introduces the research background and significance of the subject, combs the present situation of distributed storage and calculation and electricity consumption analysis and prediction at home and abroad, and then puts forward the main problems and organizational structure to be solved according to the actual situation in Hunan Province. Secondly, the related technologies of distributed storage and computing platform are studied, such as Hadoop,HDFS,HBase,Hive,Ganglia,Sqoop, which provides a theoretical basis for the further research of the subject. Then the distributed storage and computing platform is designed and implemented, including the platform technical architecture design, physical deployment and management module design and implementation. Then the current power consumption prediction problem is analyzed, and on the basis of distributed storage and computing platform, the annual and monthly power consumption prediction model based on model-free adaptive iterative learning control and the genetic algorithm based on MapReduce are proposed to optimize the short-term power consumption prediction model of neural network, and the experimental results show that the two prediction models proposed in this paper can predict the future electricity consumption trend faster and more accurately. Finally, on the basis of distributed storage and computing platform, the design and implementation of power consumption prediction system are carried out, including system function design, architecture design, core interface implementation and the effect of the system in Hunan Electric Power Company.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TM73
【參考文獻(xiàn)】
相關(guān)期刊論文 前1條
1 李興源;魏巍;王渝紅;穆子龍;顧威;;堅(jiān)強(qiáng)智能電網(wǎng)發(fā)展技術(shù)的研究[J];電力系統(tǒng)保護(hù)與控制;2009年17期
，本文編號(hào)：2507876
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