基于ARMA-GARCH模型的電價預(yù)測與研究
發(fā)布時間：2018-08-04 08:17
【摘要】：電價在電力市場中的重要性不言而喻,它既能反映出電力市場中的供求關(guān)系,又能調(diào)節(jié)和控制電力市場的交易,所以電價作為電力市場競爭效率的核心部分,電價的確定在電力市場中對市場各參與方來說都是最重要的部分。隨著電力市場的改革浪潮席卷全球和電力市場打破壟斷、相互競爭局面的形成,電力市場各參與方便十分注重電價的預(yù)測。因為準確的電價預(yù)測對市場各參與方來說具有十分重要的意義,他們在電力市場競爭中做出相關(guān)決策時,可以將預(yù)測的電價作為參考依據(jù),以便在電力市場交易中處于有利地位,因此,如何根據(jù)電力市場中的歷史電價數(shù)據(jù)和電價的相關(guān)特點準確預(yù)測出未來電價,已然成為國內(nèi)外學(xué)者研究的熱點,故準確的電價預(yù)測也變得越來越重要。電價具有與其他商品不同的特征,由于電價受眾多因素的影響,使得電價具有均值回復(fù)、較強的波動性、較強的跳躍性、以及價格尖峰和杠桿效應(yīng)等特征,電價的這些特征加大了對電價預(yù)測的難度。目前,國內(nèi)外學(xué)者已相繼提出多種預(yù)測方法,主要有時間序列法、人工神經(jīng)網(wǎng)絡(luò)法、基于小波理論分析法和組合模型預(yù)測法等,本文主要分析基于時間序列方法建模的模型。本文針對系統(tǒng)電價的特征和不同市場的特點,運用GARCH模型、TGARCH模型、EGARCH模型和PARCH模型對PJM電力市場、MISO電力市場(包括MISO中的三個節(jié)點市場)以及New England電力市場一共6個市場的電價序列分別構(gòu)建了預(yù)測模型。在模型估計時假設(shè)殘差分別服從正態(tài)分布、學(xué)生t分布和廣義誤差分布,從而比較不同電力市場下不同模型的預(yù)測精度,通過比較分析得出,由于不同市場的電價數(shù)據(jù)特征不同,GARCH族模型的預(yù)測精度也會有所不同。很難單一的從某方面來說GARCH族模型中哪個模型的預(yù)測效果更佳,根據(jù)不同電力市場的特點以及假設(shè)殘差的不同分布,GARCH模型、TGARCH模型、EGARCH模型和PARCH模型分別適合不同的電力市場電價預(yù)測。
[Abstract]:The importance of electricity price in electricity market is self-evident. It can not only reflect the relationship between supply and demand in electricity market, but also regulate and control the transaction of electricity market, so electricity price is the core part of the competitive efficiency of electricity market. The determination of electricity price is the most important part for all participants in the electricity market. With the reform of the electricity market sweeping the world and the electricity market breaking the monopoly and the formation of mutual competition, the electricity market is very convenient to participate in the prediction of electricity price. Because accurate electricity price prediction is of great significance to all participants in the market, when they make relevant decisions in the competition of the electricity market, they can use the electricity price forecast as a reference basis. In order to be in a favorable position in electricity market transaction, therefore, how to accurately predict the future electricity price according to the historical electricity price data and the relevant characteristics of electricity price in the electricity market has become a hot spot of domestic and foreign scholars. Therefore, accurate electricity price prediction is becoming more and more important. Electricity price has different characteristics from other commodities. Because electricity price is influenced by many factors, electricity price has the characteristics of average return, strong volatility, strong jump, price spike and leverage effect, etc. These characteristics of electricity price increase the difficulty of forecasting electricity price. At present, many prediction methods have been put forward by domestic and foreign scholars, such as time series method, artificial neural network method, wavelet theory analysis method and combination model prediction method, etc. In this paper, the modeling model based on time series method is mainly analyzed. This paper aims at the characteristics of system electricity price and the characteristics of different markets. Using the GARCH model, the EGARCH model and the PARCH model are used to predict the price sequence of the miso electricity market (including the three node markets in MISO) and the six markets in the New England electricity market. In the estimation of the model, it is assumed that the residuals are divided into normal distribution, student t distribution and generalized error distribution, so that the prediction accuracy of different models in different power markets is compared. The prediction accuracy of GARCH model will be different because of the different characteristics of electricity price data in different markets. It's hard to single out, in some ways, which model of the GARCH family has the best prediction effect. According to the characteristics of different power markets and the different distributions of the assumed residuals, the TGARCH model and the PARCH model are respectively suitable for different electricity market price forecasting.
【學(xué)位授予單位】：重慶師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F426.61;F726
[Abstract]:The importance of electricity price in electricity market is self-evident. It can not only reflect the relationship between supply and demand in electricity market, but also regulate and control the transaction of electricity market, so electricity price is the core part of the competitive efficiency of electricity market. The determination of electricity price is the most important part for all participants in the electricity market. With the reform of the electricity market sweeping the world and the electricity market breaking the monopoly and the formation of mutual competition, the electricity market is very convenient to participate in the prediction of electricity price. Because accurate electricity price prediction is of great significance to all participants in the market, when they make relevant decisions in the competition of the electricity market, they can use the electricity price forecast as a reference basis. In order to be in a favorable position in electricity market transaction, therefore, how to accurately predict the future electricity price according to the historical electricity price data and the relevant characteristics of electricity price in the electricity market has become a hot spot of domestic and foreign scholars. Therefore, accurate electricity price prediction is becoming more and more important. Electricity price has different characteristics from other commodities. Because electricity price is influenced by many factors, electricity price has the characteristics of average return, strong volatility, strong jump, price spike and leverage effect, etc. These characteristics of electricity price increase the difficulty of forecasting electricity price. At present, many prediction methods have been put forward by domestic and foreign scholars, such as time series method, artificial neural network method, wavelet theory analysis method and combination model prediction method, etc. In this paper, the modeling model based on time series method is mainly analyzed. This paper aims at the characteristics of system electricity price and the characteristics of different markets. Using the GARCH model, the EGARCH model and the PARCH model are used to predict the price sequence of the miso electricity market (including the three node markets in MISO) and the six markets in the New England electricity market. In the estimation of the model, it is assumed that the residuals are divided into normal distribution, student t distribution and generalized error distribution, so that the prediction accuracy of different models in different power markets is compared. The prediction accuracy of GARCH model will be different because of the different characteristics of electricity price data in different markets. It's hard to single out, in some ways, which model of the GARCH family has the best prediction effect. According to the characteristics of different power markets and the different distributions of the assumed residuals, the TGARCH model and the PARCH model are respectively suitable for different electricity market price forecasting.
【學(xué)位授予單位】：重慶師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F426.61;F726
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1 李娜;李郁俠;王麗霞;楊亞剛;;電價與負荷的相關(guān)性分析及其在電價預(yù)測中的應(yīng)用[J];武漢大學(xué)學(xué)報(工學(xué)版);2009年04期
2 張建,馬光文,楊東方,過夏明,王立明;邊際電價預(yù)測的三時點模型[J];水電能源科學(xué);2003年02期
3 潘e，
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