發(fā)布時間：2018-04-27 03:15

  本文選題：極限學(xué)習(xí)機 + 人工神經(jīng)網(wǎng)絡(luò)　； 參考：《北京交通大學(xué)》2017年碩士論文

【摘要】：基于通信、控制、IT技術(shù)的智能電網(wǎng)系統(tǒng)現(xiàn)在已成為全球趨勢。通過客戶行為預(yù)測未來電網(wǎng)負(fù)荷(電力使用)是向智能電網(wǎng)的重要任務(wù),精確的預(yù)測可以幫助公用事業(yè)公司制定合理的資源分配計劃,采取控制措施來平衡供電和電力需求。在競爭激烈的電力市場中,電力負(fù)荷預(yù)測對消費者和電力生產(chǎn)商來說是至關(guān)重要的,既可以使消費者了解自己的用電習(xí)慣,又可以幫助生產(chǎn)商根據(jù)客戶的消費習(xí)慣制定特定的產(chǎn)品,從而規(guī)劃運營和防止電力風(fēng)險。另外,預(yù)測在電力經(jīng)濟優(yōu)化中也起了非常重要的作用。在本文中,我們提出了一個新的數(shù)據(jù)挖掘框架來分析客戶行為,以預(yù)測未來時間智能電網(wǎng)中特定消費者實體的負(fù)載。然后,利用極限學(xué)習(xí)機(ELM)分析集群用戶電力行為的相似度,收集用戶電力負(fù)荷,將具有相似行為的用戶分類到相同的模型中預(yù)測,這樣可以增加模型的適應(yīng)性。為了證明所提方法的有效性·我們分別從理論和實驗去分析。極限學(xué)習(xí)機是一種新型的機器學(xué)習(xí)算法,其隨機初始化網(wǎng)絡(luò)節(jié)點權(quán)值和偏置的策略可以解決單層前饋神經(jīng)網(wǎng)絡(luò)訓(xùn)練和優(yōu)化慢的問題,并可取得全局最優(yōu)解。最后,我們使用山東省電力公司的運監(jiān)系統(tǒng)數(shù)據(jù)(包括設(shè)備信息、線路信息、用戶信息、負(fù)載信息等)和可能影響負(fù)荷變化的外部系統(tǒng)數(shù)據(jù)(如天氣信息),在MATLAB平臺進行了仿真實驗。實驗結(jié)果表明,該方法能夠深入挖掘用戶電力行為,通過合理的用戶聚類提高負(fù)荷預(yù)測的準(zhǔn)確性,揭示預(yù)測精度與集群數(shù)量之間的關(guān)系。隨著智能電網(wǎng)技術(shù)的發(fā)展,先進的計量基礎(chǔ)設(shè)施(AMI)和各種監(jiān)控系統(tǒng)的大量部署生成并積累了大量的數(shù)據(jù)。智能電表是AMI的重要組成部分,可以在一定時間內(nèi)(如每15分鐘或者每60分鐘)獲得精確的用戶消耗的電力負(fù)荷。與傳統(tǒng)電網(wǎng)系統(tǒng)相比,智能電表收集數(shù)據(jù)頻率較高,能夠生成更多的數(shù)據(jù)。但是,累積的大數(shù)據(jù)一直處于擱置狀態(tài)。隨著機器學(xué)習(xí)算法和大數(shù)據(jù)的發(fā)展,我們可以對電力大數(shù)據(jù)進行分析,充分挖掘這些隱藏在這些數(shù)據(jù)的背后的價值。例如,基于運監(jiān)系統(tǒng)中的設(shè)備和客戶數(shù)據(jù),結(jié)合聚類算法挖掘用戶用電行為,基于智能電表數(shù)據(jù)和分類回歸算法,預(yù)測未來負(fù)載的變化。[37]負(fù)載預(yù)測一直是電力系統(tǒng)安全發(fā)展的關(guān)鍵,因為它可以影響了許多有關(guān)電力系統(tǒng)的決策,如經(jīng)濟調(diào)度,自動發(fā)電控制·安全評估,維護調(diào)度和能源商業(yè)化。精確的負(fù)載預(yù)測可以在經(jīng)濟合理的情況下啟動和停止電力系統(tǒng)發(fā)電機組,在維護安全穩(wěn)定方面發(fā)揮重要作用,保持社會正常生產(chǎn)和生活,有效降低發(fā)電成本。通常情況下,按照負(fù)荷預(yù)測時間的長短,負(fù)載預(yù)測可分為三類:短期負(fù)載預(yù)測,中期負(fù)載預(yù)測,長期負(fù)載預(yù)測。其中,短期負(fù)荷預(yù)測的預(yù)測時間范圍是未來1小時,一天或一周。中期負(fù)載預(yù)測的預(yù)測時間范圍大概是未來一個月。長期負(fù)載預(yù)測的時間范圍則是未來一年,甚至三至五年。本文主要對用戶電力負(fù)荷進行短期負(fù)荷預(yù)測。負(fù)載預(yù)測對能源管理系統(tǒng)的實時性和安全性中起著主要作用,準(zhǔn)確的預(yù)測有利于電力系統(tǒng)的規(guī)劃者完成各種任務(wù),如發(fā)電量的經(jīng)濟調(diào)度·燃料采購的調(diào)度等。難題是·負(fù)載預(yù)測是一項艱巨的任務(wù),因為其變化受到許多因素的影響·如天氣條件,是否是節(jié)假日,人口流動,經(jīng)濟狀況和客戶的用電習(xí)慣。不準(zhǔn)確或錯誤地負(fù)載預(yù)測可能會增加運營成本。據(jù)觀察,電力需求預(yù)測誤差僅增長百分之一,導(dǎo)致英國電力系統(tǒng)運營成本增加了 1000萬英鎊。這是負(fù)載預(yù)測效用類型的嚴(yán)重失誤。而且,糟糕的負(fù)載預(yù)測會誤導(dǎo)了規(guī)劃者,導(dǎo)致錯誤和昂貴的擴張計劃。高估未來的電力負(fù)荷可能導(dǎo)致多余的儲備的電力,對負(fù)載的低估導(dǎo)致提供足夠電力的故障。相反,準(zhǔn)確的預(yù)測可使公用事業(yè)提供商提前計劃燃料等資源·并采取控制措施·如開啟/關(guān)閉需求響應(yīng)裝置和修訂電價等。同樣地,高估未來的電力負(fù)荷可能導(dǎo)致多余的儲備的電力。相反,對負(fù)載的低估導(dǎo)致提供足夠電力的故障。無論計劃者低估還是誤判負(fù)荷,高精度的負(fù)載預(yù)測技術(shù)需要先進的技術(shù)、自適應(yīng)的預(yù)測模型。雖然不同的模型在動態(tài)系統(tǒng)中有一些優(yōu)勢·但改善相關(guān)缺點的可能性是不能排除的。因此,需要開發(fā)最佳和準(zhǔn)確的負(fù)載預(yù)測模型來改善(最小化)預(yù)測誤差。通過對多種數(shù)據(jù)挖掘算法、機器學(xué)習(xí)算法的分析,我們致力于提出高精度的負(fù)載預(yù)測模型。極限學(xué)習(xí)機是新提出的機器學(xué)習(xí)算法,不僅效率高而且可以防止過擬合。因此,該項目的主要研究問題是:融合跨系統(tǒng)的數(shù)據(jù),進行數(shù)據(jù)預(yù)處理;對數(shù)據(jù)進行分析,挖掘影響負(fù)載變化的強關(guān)聯(lián)特征;利用極限學(xué)習(xí)機構(gòu)建負(fù)載預(yù)測模型,調(diào)整參數(shù)獲得精度最高的負(fù)荷預(yù)測結(jié)果。在大數(shù)據(jù)的背景下·影響負(fù)載變化的因子眾多,電力負(fù)荷預(yù)測是一項復(fù)雜的工作,其呈現(xiàn)復(fù)雜的非線性變換。傳統(tǒng)的電力負(fù)載預(yù)測模型,大都是線性模型,缺乏非線性映射能力。因此,以前的預(yù)測方法根本不適應(yīng)大數(shù)據(jù)時代的發(fā)展。另一方面,智能電網(wǎng)缺乏通用的訓(xùn)練框架,為電力系統(tǒng)的其他任務(wù)提供支撐,以提高智能電網(wǎng)分析的效率。自1990年以來,研究人員就開始關(guān)注電力負(fù)載預(yù)測問題并提出了很多預(yù)測模型[1]。如提出的時間序列分析模型ARIMA,該模型重點在于分析負(fù)載根據(jù)時間的變化曲線,從而預(yù)測未來負(fù)荷的變化,其優(yōu)點是簡單·缺點是沒有考慮影響負(fù)載變化的因子。使用模糊邏輯方法Fuzzy Logic分析負(fù)載的變化,該方法考慮了影響負(fù)荷變化的各種影響因子,如溫度、濕度等,但其沒有考慮設(shè)備、線路信息。使用人工神經(jīng)網(wǎng)絡(luò)方法構(gòu)建負(fù)荷預(yù)測模型,神經(jīng)網(wǎng)絡(luò)的優(yōu)化和訓(xùn)練較慢,而且對復(fù)雜的負(fù)載變化,較易陷入局部最優(yōu)值。使用支持向量回歸SVR,該方法將特征映射到核空間·可以取得全局最優(yōu)·但訓(xùn)練效率交低。通過對已有負(fù)載預(yù)測方法的調(diào)研·本文提出基于極限機器學(xué)習(xí)ELM的負(fù)荷預(yù)測模型·并通過實際的智能電網(wǎng)數(shù)據(jù)去驗證模型的有效性。在研究中·所涉及的消費者實體可以具有各種粒度級別。例如,它可以是一個智能電表(一個家庭),一組智能電表(一個區(qū)),一個變電站(城鎮(zhèn)或城市)或電站(通常覆蓋一個很大的地理位置區(qū))。類似地,所討論的電力負(fù)載的時間單位也具有不同的長度。它可以是5分鐘,15分鐘,1小時,1天,1周,1個月,1年等。在這項工作中,我們創(chuàng)建一個系統(tǒng)來預(yù)測每個智能電表的每日最高負(fù)載。另外·需要指出的是,我們在負(fù)載預(yù)測的研究中使用的框架和技術(shù),也同樣適用于不同消費者實體的行為預(yù)測。已有的負(fù)載預(yù)測模型,都有些許的不足,無法滿足當(dāng)前智能電網(wǎng)的需求。通過研究相關(guān)的數(shù)據(jù)挖掘和機器學(xué)習(xí)算法,我們采用極限學(xué)習(xí)機來構(gòu)建負(fù)載預(yù)測模型。ELM是一種新型的極限學(xué)習(xí)機作為一類機器學(xué)習(xí)方法,以簡單易用、有效的單隱層前饋神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)算法,受到越來越多的研究者關(guān)注。傳統(tǒng)的神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)算法(如BP算法)需要人為設(shè)置大量的網(wǎng)絡(luò)訓(xùn)練參數(shù)·并且容易產(chǎn)生局部最優(yōu)解。極限學(xué)習(xí)機只需要設(shè)置網(wǎng)絡(luò)的隱層節(jié)點個數(shù)·在算法執(zhí)行過程中不需要調(diào)整網(wǎng)絡(luò)的輸入權(quán)值以及隱元的偏置,可以產(chǎn)生唯一的最優(yōu)解,因此它具有學(xué)習(xí)速度快且泛化性能好的優(yōu)點。由于我們預(yù)測未來一段時間的最高負(fù)載P,我們首先需要對采集到歷史電力負(fù)載數(shù)據(jù)進行處理,提取每天負(fù)載最大值,作為目標(biāo)屬性,設(shè)搜集的天數(shù)為N。另外,從外部數(shù)據(jù)庫中爬取相應(yīng)日期的天氣信息,主要包括日最高溫、日最低溫、月最高溫、月最低溫、是否是節(jié)假日、星期幾。因此,對于歷史記錄中的每個目標(biāo)峰值負(fù)載值·我們構(gòu)建與目標(biāo)相關(guān)聯(lián)的特征向量。在特征分析后,我們訓(xùn)練開始訓(xùn)練極限學(xué)習(xí)機ELM模型,使用N天的特征向量,目標(biāo)對來完成。在訓(xùn)練完ELM模型后,我們將使用得到的回歸模型來預(yù)測給定日期的峰值負(fù)載值P。為此,我們需要使用相同的方式處理待預(yù)測數(shù)據(jù)·并提取特征相同的特征向量。經(jīng)過ELM模型,可以得出未來第d天的負(fù)載。在訓(xùn)練測試完成后,我們將構(gòu)建的ELM模型應(yīng)用到實際的電力系統(tǒng)中,該模型取得了很好的效果。該模型具有重要的意義·如發(fā)電機公司·可以根據(jù)未來電力負(fù)荷的變化來合理的分配能源;另外,電力公司也可以根據(jù)負(fù)載的變化做出合理的決策。綜上所述,在這項工作中·我們提出了一個準(zhǔn)確的負(fù)載預(yù)測模型·可以為智能電網(wǎng)的管理者提供決策支持。在我們的方法中·我們?nèi)诤峡缦到y(tǒng)的電力數(shù)據(jù),分析影響負(fù)荷變化的強關(guān)聯(lián)特征,采用極限學(xué)習(xí)機(ELM)回歸算法構(gòu)建高精度的預(yù)測模型。實驗結(jié)果表明·我們的方法能夠比現(xiàn)有的其他預(yù)測方法提供更準(zhǔn)確的結(jié)果·并且在計算復(fù)雜度較低。在將來的工作紅·我們打算研究基于自動特征選擇的負(fù)荷回歸模型,以進一步提高其準(zhǔn)確性和自適應(yīng)性。另外,我們計劃用不同國家的多個智能電網(wǎng)負(fù)載數(shù)據(jù)測試我們的方法,并對已有的模型進行微調(diào)·以確保其通用性。最后·我們將進一步拓展我們的分析框架和預(yù)測模型,使其可適用于任何粒度級別(如個體戶·街區(qū)·城鎮(zhèn)·城市和大地理區(qū)域)的消費實體負(fù)載預(yù)測中,并調(diào)優(yōu)模型以取得更好的預(yù)測結(jié)果。未來的工作還可以繼續(xù)探索其他綜合技術(shù),結(jié)合極限學(xué)習(xí)機模型提高智能電網(wǎng)負(fù)載預(yù)測的性能。
[Abstract]:The smart grid system based on communication, control and IT technology has now become a global trend. It is an important task for the smart grid to predict future power grid load through customer behavior. Accurate prediction can help utility companies to formulate reasonable resource allocation plans and take control measures to balance power supply and electricity demand. In the competitive power market, power load forecasting is very important for consumers and power producers, which can make consumers understand their own electricity use habits and help manufacturers to formulate specific products according to customer's consumption habits, so as to plan operation and prevent electricity risk. In addition, it is predicted that the power economy is superior to the power economy. In this paper, we have proposed a new data mining framework to analyze customer behavior to predict the load of specific consumer entities in the smart grid in the future. Then, we use the limit learning machine (ELM) to analyze the similarity of the power line of the cluster users and collect the power load of the user. In order to prove the validity of the proposed method, we analyze the validity of the proposed method. In order to prove the effectiveness of the proposed method, we analyze both theory and experiment. The limit learning machine is a new kind of machine learning algorithm, and its random initialization of network node weight and bias strategy can solve the single layer feedforward. In the end, we use the data (including equipment information, line information, user information, load information, etc.) and the external system data (such as weather information) that may affect load changes in the MATLAB platform. The simulation experiment on the MATLAB platform is carried out. The results show that the method can excavate the user's power behavior deeply, improve the accuracy of load forecasting by reasonable user clustering, and reveal the relationship between the prediction accuracy and the number of clusters. With the development of smart grid technology, a large number of advanced measurement infrastructure (AMI) and various kinds of monitoring systems are generated and accumulated. Data. The smart meter is an important part of AMI, which can get the power load of accurate user consumption in a certain time (such as every 15 minutes or every 60 minutes). Compared with the traditional grid system, the intelligent meter collects more data and generates more data. However, the accumulated large data has always been in a shelved state. With the development of the learning algorithm and large data, we can analyze the large data and fully excavate the value behind these data. For example, based on the equipment and customer data in the monitoring system, we use the clustering algorithm to mine the user's electricity behavior, based on the intelligence meter data and the classification regression algorithm, to predict the future load. The change of.[37] load prediction has been the key to the security development of the power system, because it can affect many decisions about power systems, such as economic scheduling, automatic generation control, security assessment, maintenance scheduling and energy commercialization. Accurate load forecasting can start and stop power system generators under economic conditions. The group plays an important role in maintaining safety and stability, maintaining the normal production and life of the society and effectively reducing the cost of generating electricity. In general, the load forecast can be divided into three categories according to the length of the load forecasting time: short-term load forecasting, mid term load forecasting, and long-term load forecasting. 1 hours, one day, or a week. The forecast time range of mid-term load forecast is about the next month. The time range of long-term load forecasting is the next year, or even three to five years. This paper mainly carries out short-term load forecasting for the user's power load. Load forecasting plays a major role in the real-time and security of the energy management system. Accurate prediction is beneficial to the planners of the power system to accomplish various tasks, such as the economic dispatch of electricity generation and the scheduling of fuel procurement. The problem is that load forecasting is a difficult task because the changes are influenced by many factors, such as weather conditions, whether it is holiday, population flow, economic situation, and customer's use of electricity. Accurate or erroneous load forecasting can increase operating costs. It is observed that power demand forecast errors increase by only one percent, resulting in an increase of 10 million pounds in UK power system operation costs. This is a serious error in the type of load forecasting utility. And bad load forecasting misleads planners, causing errors and expensive expansion. Overestimating future power loads may lead to redundant reserves of electricity, and undervaluation of the load leads to sufficient power failures. On the contrary, accurate prediction can make utility providers plan fuel and other resources ahead of time, and take control measures, such as opening / closing demand response devices and revising electricity prices. In contrast, the undervaluation of the load leads to the failure to provide enough power. No matter the planners underestimate or misjudge the load, the high precision load forecasting technology requires advanced technology and adaptive prediction model. Although different models have some advantages in the dynamic system. The possibility of the shortcomings can not be excluded. Therefore, it is necessary to develop the optimal and accurate load forecasting model to improve (minimization) prediction error. By analyzing a variety of data mining algorithms and machine learning algorithms, we are committed to a high precision load forecasting model. It has high efficiency and can prevent over fitting. Therefore, the main research problem of the project is: merging the data of the cross system, preprocessing the data, analyzing the data, mining the strong correlation characteristics that affect the load change; using the limit learning mechanism to build the load forecasting model and adjusting the parameters to get the most accurate load forecasting results. According to the background, there are many factors affecting load change. Power load forecasting is a complex work, which presents complex nonlinear transformation. The traditional power load forecasting model is mostly linear model, and it lacks the ability of nonlinear mapping. Therefore, the previous prediction method is not suitable for the development of the big data age. On the other hand, intelligence The power grid lacks a general training framework to support other tasks of the power system to improve the efficiency of the smart grid analysis. Since 1990, researchers have begun to pay attention to the power load forecasting problem and put forward a number of prediction models, such as the time series analysis model ARIMA proposed by [1]., which focuses on the analysis of the load basis. The change curve of the time to predict the change of the load in the future, its advantage is that the disadvantage is that it does not consider the factors that affect the load change. The fuzzy logic method Fuzzy Logic is used to analyze the change of the load, which takes into account the influence factors of the load change, such as temperature, humidity, etc., but it does not consider the equipment and line information. The artificial neural network method is used to construct the load forecasting model. The neural network is optimized and trained slowly, and it is easier to fall into the local optimal value for the complex load change. Using support vector regression SVR, this method can map the features to the nuclear space. The method can obtain the global optimum but the training efficiency is low. In this paper, a load forecasting model based on the ultimate machine learning ELM is proposed and the validity of the model is verified through actual smart grid data. In the study, the consumer entities involved can have various granularity levels. For example, it can be a smart meter (a family), a group of smart meters (a zone), one A substation (town or city) or a power station (usually covered by a large geographic area). Similarly, the time units of the power load discussed are also of different lengths. It can be 5 minutes, 15 minutes, 1 hours, 1 days, 1 weeks, 1 months, 1 years. In this work, we create a system to predict the daily most intelligent meter. In addition, it is necessary to point out that the framework and technology we use in the study of load forecasting are also applicable to the behavior prediction of different consumer entities. The existing load forecasting models are inadequate to meet the needs of the current smart grid. By studying the related data mining and machine learning algorithms, Using the limit learning machine to construct the load forecasting model.ELM is a new kind of machine learning method as a kind of machine learning method, which is easy to use and effective single hidden layer feedforward neural network learning algorithm. More and more researchers pay attention to the learning algorithm. The traditional neural network learning algorithm (such as BP algorithm) needs to set up a large number of networks. The limit learning machine only needs to set the number of hidden layer nodes in the network. In the process of executing the algorithm, it does not need to adjust the input weights of the network and the bias of the hidden element, so it can produce the unique optimal solution. Therefore, it has the advantages of fast learning speed and good generalization performance. The highest load P for the next period of time, we first need to process the collection of historical power load data, extract the maximum daily load, as the target attribute, set up the number of days to collect the N., from the external database to climb the corresponding date of weather information, mainly including the hottest day, the lowest temperature, the highest temperature of the month, the lowest temperature of the month, Whether it is holidays, weeks. So, for each target peak load value in the history record, we build the eigenvector associated with the target. After the feature analysis, we train to train the limit learning machine ELM model, use the feature vector of N days, and finish the target. After training the ELM model, we will use it. The regression model is used to predict the peak load value P. for a given date. For this purpose, we need to use the same way to deal with the expected data and extract the feature vectors with the same characteristics. After the ELM model, we can get the load of the future day D. After the training test is completed, we apply the constructed ELM model to the actual power system, which is the model. The model has great effect. The model is of great significance. For example, the generator company can allocate the energy reasonably according to the changes in the future power load; in addition, the power company can make a reasonable decision based on the change of the load. In summary, in this work, we propose an accurate load forecasting model. We provide decision support for the managers of the smart grid. In our method, we integrate the cross system power data, analyze the strong correlation characteristics that affect the load changes, and use the ELM regression algorithm to build a high precision prediction model. The experimental results show that our method can be provided than other existing forecasting methods. More accurate results and less computational complexity. In the future work red. We intend to study the load regression model based on automatic feature selection to further improve its accuracy and adaptability. In addition, we plan to test our methods with multiple smart grid load data in different countries and to advance the existing models. Fine-tuning to ensure its versatility. Finally, we will further expand our analytical framework and prediction models to apply to consumer entity load forecasting at any level of granularity, such as personal, block, town, and large geographic areas, and optimize the model for better prediction results. Future work can be followed. Further explore other integrated technologies and combine the extreme learning machine model to improve the load forecasting performance of smart grid.

【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM76

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