电力市场中电价预测模型方法及应用研究
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摘要
在电力市场中,电价不仅和市场参与者的利益息息相关,也影响着社会和经济的众多方面,因此是各方关注的焦点。准确的电价预测,无论对于政府监管机构、电力企业还是公众都具有深远的意义。电力市场中的电价波动大,不同市场中的电价特性差异也很大,很难采用同一方法建模预测。
本文在对电力市场中电价影响因素分析的基础上,选择合适的模型进行建模。在建模之前,首先对电价的最重要的影响因素-负荷进行了预测:采用遗传算法(Genetic Algorithm, GA)优化的支持向量机(Support Vector Machine, SVM)对短期负荷进行预测;采用综合考虑季节影响和节假日影响的灰色优化模型对中长期负荷进行预测,为短期电价预测和中长期电价预测做好准备。
针对平稳日前市场中的电价,采用考虑外生变量的广义自回归条件异方差(General Autoregressive Conditional Heteroskedasticity,GARCH)进行预测。GARCH模型很好地考虑了电价的群集性波动,而外生变量则增强了模型对外界因素的响应。同时考虑到电价波动剧烈,在GARCH建模之前,先对原始的电价序列先采用小波(Wavelet)进行预处理,将电价分为概貌序列和细节序列,对概貌序列采用GARCH建模,并将预测结果直接作为电价预测值。算例研究表明,该模型预测效果要好于普通的时间序列建模效果。
对波动较大的日前市场电价(尤其是夏季高峰电价),采用神经网络和SVM等智能算法进行预测。针对神经网络对输入变量较为敏感的特点,在采用神经网络建模之前先采用主成分分析(Principle Component Analysis,PCA)对输入信息进行筛选,剔除其中的冗余信息。采用自组织映射(Self-Organizing Mapping,SOM)对电价进行分类,对相似的电价采用同一SVM模型进行预测,避免了夏季高峰电价预测建模时训练样本不足的缺陷,有效地提高了模型预测精度。
对于波动更大的实时电价,综合智能算法和时间序列建模的优点建立SVM-GA-GARCH模型,对电价进行预测。利用SVM强大的非线型映射能力拟合电价和相关影响因素之间的关系;利用GARCH充分挖掘时间序列信息,对SVM建模的预测误差进行进一步处理,剥离前者不能解释的误差影响,提高预测精度。中长期电价既受用电需求等周期性因素的影响,还受到众多不确定性的因素影响,变化规律不明显,预测难度大。采用经验模式分解(Empirical Mode Decomposition, EMD)将中长期电价序列分解成多个反映电价不同变化规律和周期性的分量,并分别建模预测。实例研究表明该方法能够结合不同算法的优点,对变化规律性复杂的中长期电价预测效果较好。
Electricity price in power market is closely linked to the interests of the participants. It is the focus that their concerns. It has profound effects to forecast price precisely for all the related sides, not only the power industry, the regulator, but also the publics. It is difficult to forecast with one model for its high volatility and obvious difference in different markets. It should be modeled respectively with special inputs selected for each kind market. The characters of electricity price and its influence factors were discussed in detail for following modeling. Then power load, the most important factor, was forecasted. Support Vector Machine (SVM) with parameters optimized by Genetic Algorithm(GA) were applied to forecast short term load; and enhanced gray methodology considering seasonal factors to forecast mid-term load.
General Autoregressive Conditional Heteroskedasticity(GARCH) was applied for stationary day-ahead price forecasting. GARCH considered more volatility clustering than other time series modeling. Exogenous variable, the daily price ratios of different weekday types were led into the GARCH model to intensify its response to external influences. Wavelet was used to reduce the fluctuation of price series before modeling.
For other more volatility day-ahead price, Intelligence Algorithms (IA), Artificial Neural Network(ANN) and SVM, were applied for forecasting model. Principle Component Analysis (PCA) was employed to mine main information of the inputs in ANN modeling. For the peculiar price during the time of summer peak load and holiday, Self-Organizing Mapping (SOM) was used to cluster the price and its influential factors automatically, then each cluster was modeled by a SVM model. Cases study shows that the PCA helps to improve the modeling accuracy, also the SOM cluster works well in forecasting modling of summer peak price.
An integrated methodology of IA and time series modeling was applied for real time price forecasting. Time series, GARCH models were applied to adjust the error series of price forecasted by IA models, SVM-GA, eliminating their autocorrelations and heteroscedasticity effects.
Under many uncertain influences, medium and long term electricity price is hard to forecast with the traditional statistics methods. This paper applied Empirical Mode Decomposition (EMD) to decomposed price into several intrinsic modes intuitively. The influence on these modes of different factors was discussed in detail, and then the influence of price was discussed from the variety of time scale. At last time series modeling and SVM modeling were performed according to the characters of the modes.
本文在对电力市场中电价影响因素分析的基础上,选择合适的模型进行建模。在建模之前,首先对电价的最重要的影响因素-负荷进行了预测:采用遗传算法(Genetic Algorithm, GA)优化的支持向量机(Support Vector Machine, SVM)对短期负荷进行预测;采用综合考虑季节影响和节假日影响的灰色优化模型对中长期负荷进行预测,为短期电价预测和中长期电价预测做好准备。
针对平稳日前市场中的电价,采用考虑外生变量的广义自回归条件异方差(General Autoregressive Conditional Heteroskedasticity,GARCH)进行预测。GARCH模型很好地考虑了电价的群集性波动,而外生变量则增强了模型对外界因素的响应。同时考虑到电价波动剧烈,在GARCH建模之前,先对原始的电价序列先采用小波(Wavelet)进行预处理,将电价分为概貌序列和细节序列,对概貌序列采用GARCH建模,并将预测结果直接作为电价预测值。算例研究表明,该模型预测效果要好于普通的时间序列建模效果。
对波动较大的日前市场电价(尤其是夏季高峰电价),采用神经网络和SVM等智能算法进行预测。针对神经网络对输入变量较为敏感的特点,在采用神经网络建模之前先采用主成分分析(Principle Component Analysis,PCA)对输入信息进行筛选,剔除其中的冗余信息。采用自组织映射(Self-Organizing Mapping,SOM)对电价进行分类,对相似的电价采用同一SVM模型进行预测,避免了夏季高峰电价预测建模时训练样本不足的缺陷,有效地提高了模型预测精度。
对于波动更大的实时电价,综合智能算法和时间序列建模的优点建立SVM-GA-GARCH模型,对电价进行预测。利用SVM强大的非线型映射能力拟合电价和相关影响因素之间的关系;利用GARCH充分挖掘时间序列信息,对SVM建模的预测误差进行进一步处理,剥离前者不能解释的误差影响,提高预测精度。中长期电价既受用电需求等周期性因素的影响,还受到众多不确定性的因素影响,变化规律不明显,预测难度大。采用经验模式分解(Empirical Mode Decomposition, EMD)将中长期电价序列分解成多个反映电价不同变化规律和周期性的分量,并分别建模预测。实例研究表明该方法能够结合不同算法的优点,对变化规律性复杂的中长期电价预测效果较好。
Electricity price in power market is closely linked to the interests of the participants. It is the focus that their concerns. It has profound effects to forecast price precisely for all the related sides, not only the power industry, the regulator, but also the publics. It is difficult to forecast with one model for its high volatility and obvious difference in different markets. It should be modeled respectively with special inputs selected for each kind market. The characters of electricity price and its influence factors were discussed in detail for following modeling. Then power load, the most important factor, was forecasted. Support Vector Machine (SVM) with parameters optimized by Genetic Algorithm(GA) were applied to forecast short term load; and enhanced gray methodology considering seasonal factors to forecast mid-term load.
General Autoregressive Conditional Heteroskedasticity(GARCH) was applied for stationary day-ahead price forecasting. GARCH considered more volatility clustering than other time series modeling. Exogenous variable, the daily price ratios of different weekday types were led into the GARCH model to intensify its response to external influences. Wavelet was used to reduce the fluctuation of price series before modeling.
For other more volatility day-ahead price, Intelligence Algorithms (IA), Artificial Neural Network(ANN) and SVM, were applied for forecasting model. Principle Component Analysis (PCA) was employed to mine main information of the inputs in ANN modeling. For the peculiar price during the time of summer peak load and holiday, Self-Organizing Mapping (SOM) was used to cluster the price and its influential factors automatically, then each cluster was modeled by a SVM model. Cases study shows that the PCA helps to improve the modeling accuracy, also the SOM cluster works well in forecasting modling of summer peak price.
An integrated methodology of IA and time series modeling was applied for real time price forecasting. Time series, GARCH models were applied to adjust the error series of price forecasted by IA models, SVM-GA, eliminating their autocorrelations and heteroscedasticity effects.
Under many uncertain influences, medium and long term electricity price is hard to forecast with the traditional statistics methods. This paper applied Empirical Mode Decomposition (EMD) to decomposed price into several intrinsic modes intuitively. The influence on these modes of different factors was discussed in detail, and then the influence of price was discussed from the variety of time scale. At last time series modeling and SVM modeling were performed according to the characters of the modes.
引文
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