短期电力负荷的智能化预测方法研究
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摘要
当电力系统进行在线控制时，应当用短期负荷预测来实现发、供电的合理调度。短期负荷预测是电力系统安全调度、经济运行的重要依据，负荷预测精度的高低直接影响到电力系统运行的可靠性、经济性和供电质量。本文在短期负荷预测方面所做的主要研究工作如下：
1．通过对历史负荷数据进行垂直和水平预处理，使其更能体现电力负荷的变化趋势，为短期负荷预测模型利用这些历史数据奠定了基础：将自相关系数的概念应用于短期负荷预测模型的输入变量选择，提出采用近大远小原则和自相关理论相结合的方法进行输入变量的选取，找到一组合适的输入变量来有效地解释负荷的变化关系。该方法有明确的理论依据，并具有很强的可操作性，是一种比较科学系统的输入变量选择方法。通过该方法可以得到更小的输入变量集，采用的输入变量与预测点负荷相关性最强，预测结果更准确。
2．将神经网络与模糊逻辑相结合建立了组合负荷预测模型，神经网络只处理历史负荷信息，既缩短了神经网络的学习时间，又避免了由于神经网络对其它信息的不敏感而造成的错误学习；而模糊逻辑则处理对负荷变化影响较大的气温、节假日等因素。根据负荷变化的具体特点，构造出这些因素的隶属函数和模糊规则库，从而用模糊逻辑实现对基本负荷分量的修正。与传统的神经网络预测模型相比，该组合模型充分利用了神经网络的学习能力，以及模糊逻辑对主观经验的吸收，能够充分考虑气温和节假日等因素对系统负荷造成的影响，在一定程度上可以提高负荷预测结果的准确性，特别是可以明显提高对周末和节假日负荷预测结果的准确性。
3．结合电力负荷非线性变化的特点，提出了一个具有混沌机制的模糊神经网络预测模型。为了拓宽权的运动范围，引入权的混沌学习算法，将一个权的非线性自反馈项引入到模糊神经网络互联权空间学习算法的动力系统方程中，使模糊神经网络的学习动力学过程成为混沌动力学，使系统在学习过程中能够很快找到系统的全局最小或它的近似值；模糊推理和解模糊均通过神经网络来实现，选取的隶属函数使权值具有一定的知识表示，便于分析和理解；对该模型提出两种不同的模糊推理算法进行模糊推理，并确定出模糊相乘推理算法更优。在模糊推理运算中用简单的加权计算取代了传统的重心法，在不降低预测精度的情况下，大大减少了学习时间，提高了运行效率。该模型较好地解决了常规BP算法收敛性差，预测精度低的缺陷，对非线性负荷变化具有较好的预测性能。
4．为了克服当预测日天气出现快速变化，预测误差也随之增加的问题，提出了一种新的具有反馈递归结构的在线实时负荷预测模型。在该预测模型中，预测
短期电力负荷的智能化预测方法研究
负荷是通过给预测日的类似日数据平均值加一个矫正值获得，神经网络只产生一
个非常小的数据作矫正值，而不必学习所有的类似日数据，因此可以大大减少神
经网络的节点数和训练时间，简化网络结构。由于该模型采用在线实时学习的算
法，神经网络可以捕捉快速的天气变化和预测误差之间的关系，从而输出一个适
当的负荷矫正值，这个矫正值与快速的温度变化相一致。在该模型中首次提出了
动态类似日的概念，对于同一预测日的不同时刻，选出的类似日是随着时间的变
化而动态变化的。这样较好地解决了特殊天气和节假日历史数据少，不易组成训
练样本集的问题。该模型在天气条件发生较大变化时进行短时负荷预测有明显优
势，并可用于超短时负荷预测。
本文还指出在未来电力市场的环境下，电价因素也是一个必须在负荷预测模
型中加以考虑的因素，对电价因素和电力市场对短期负荷预测的影响进行了分析，
并讨论了负荷预测与电价预测的关系。
最后对全文进行了总结，并对短期负荷预测的发展趋势进行了展望。
关键词:神经网络模糊推理混沌非线性鲁棒性负荷预测
When the power systems are controlled on line, short-term load forecasting should be used for realizing reasonable distribution of generating and supplying electricity. Short-term load forecasting is important basis of safely assigning and economically running. The forecasting precision will directly affect the reliability, economy running and supplying power quality of power system. The primary work done in short-term load forecasting in the paper is as follows.Firstly, via vertical and horizontal pretreatment, the historical load data can furthermore show the load changing trend, which establishes the basis for short-term load forecasting model using these historical data. By using the autocorrelation function on input variables selection for short-term load forecasting model, the principle which effect is big at near place and is small at far place and autocorrelation function theory are combined to select input variables. Then a group of input variables selected can effectively explain load varying relation. This approach has clear theory foundation and very strong maneuverability, and it is a systemic and scientific method for input variables selection. By use of this method, we can get small input variables sets, the correlation of adopted input variables and forecasting hour load is most strong, and the forecasting effect is more exact.Secondly, a combined load forecasting model is presented by integrating neural networks and fuzzy logic. In the model, neural networks only settle historical load information. This not only shortens the learning time of neural networks, but also avoids improper study produced by slowness of neural networks to other information. Moreover, fuzzy logic deals with the factors which have great effect to load varying, such as air temperature and holidays, etc. According to the own characteristic of load varying, the memberships and fuzzy rules base are constructed, and the modifying of basic load heft is realized by fuzzy logic. Comparing with conventional neural networks forecasting models, the combined model adequately makes use of the neural network learning ability and absorbing of fuzzy logic to subjective experience. It fully considers the effect of factors such as air temperature and holidays to power load, which can enhance the load forecasting results veracity to a certain extent, especially weekends and holidays.Thirdly, in order to describe the non-linear relation of power load varying, a fuzzy neural networks short-term load forecasting model based on chaos mechanism is presented. For the sake of extending the scope of weight acting, the chaotic learning algorithm of weight values is introduced. The non-liner feedback item of weight value is employed in dynamic system equation of fuzzy neural networks weights space learning algorithm, which make the neural networks learning dynamics turn into chaos dynamics.
So the system can find the global minimal point or its approximation quickly. The fuzzy inference and defuzzification of the model are both realized by neural networks. The selected membership function made neural network weight values have definite knowledge meaning, and it can be analyzed and understood. In the model, two different fuzzy inference algorithms are put forward to finish fuzzy inference, and it is confirmed that the fuzzy multiplication inference algorithm can get better forecasting effect. The conventional centroid method is replaced by sample adding authority calculation in fuzzy inference. Under not reducing forecasting precision, the learning time is shortened, and the running efficiency is increased. The model preferably settles the slow convergence speed and low forecasting precision limitation of general BP algorithm, and has preferable forecasting capability for non-linear load varying.Fourthly, in order to overcome the shortcomings that forecasting error will increase evidently while weather varies rapidly on the forecasting day, a new on line real time load forecasting model with feedback and recurrent structure is proposed. In the model, the fo
1．通过对历史负荷数据进行垂直和水平预处理，使其更能体现电力负荷的变化趋势，为短期负荷预测模型利用这些历史数据奠定了基础：将自相关系数的概念应用于短期负荷预测模型的输入变量选择，提出采用近大远小原则和自相关理论相结合的方法进行输入变量的选取，找到一组合适的输入变量来有效地解释负荷的变化关系。该方法有明确的理论依据，并具有很强的可操作性，是一种比较科学系统的输入变量选择方法。通过该方法可以得到更小的输入变量集，采用的输入变量与预测点负荷相关性最强，预测结果更准确。
2．将神经网络与模糊逻辑相结合建立了组合负荷预测模型，神经网络只处理历史负荷信息，既缩短了神经网络的学习时间，又避免了由于神经网络对其它信息的不敏感而造成的错误学习；而模糊逻辑则处理对负荷变化影响较大的气温、节假日等因素。根据负荷变化的具体特点，构造出这些因素的隶属函数和模糊规则库，从而用模糊逻辑实现对基本负荷分量的修正。与传统的神经网络预测模型相比，该组合模型充分利用了神经网络的学习能力，以及模糊逻辑对主观经验的吸收，能够充分考虑气温和节假日等因素对系统负荷造成的影响，在一定程度上可以提高负荷预测结果的准确性，特别是可以明显提高对周末和节假日负荷预测结果的准确性。
3．结合电力负荷非线性变化的特点，提出了一个具有混沌机制的模糊神经网络预测模型。为了拓宽权的运动范围，引入权的混沌学习算法，将一个权的非线性自反馈项引入到模糊神经网络互联权空间学习算法的动力系统方程中，使模糊神经网络的学习动力学过程成为混沌动力学，使系统在学习过程中能够很快找到系统的全局最小或它的近似值；模糊推理和解模糊均通过神经网络来实现，选取的隶属函数使权值具有一定的知识表示，便于分析和理解；对该模型提出两种不同的模糊推理算法进行模糊推理，并确定出模糊相乘推理算法更优。在模糊推理运算中用简单的加权计算取代了传统的重心法，在不降低预测精度的情况下，大大减少了学习时间，提高了运行效率。该模型较好地解决了常规BP算法收敛性差，预测精度低的缺陷，对非线性负荷变化具有较好的预测性能。
4．为了克服当预测日天气出现快速变化，预测误差也随之增加的问题，提出了一种新的具有反馈递归结构的在线实时负荷预测模型。在该预测模型中，预测
短期电力负荷的智能化预测方法研究
负荷是通过给预测日的类似日数据平均值加一个矫正值获得，神经网络只产生一
个非常小的数据作矫正值，而不必学习所有的类似日数据，因此可以大大减少神
经网络的节点数和训练时间，简化网络结构。由于该模型采用在线实时学习的算
法，神经网络可以捕捉快速的天气变化和预测误差之间的关系，从而输出一个适
当的负荷矫正值，这个矫正值与快速的温度变化相一致。在该模型中首次提出了
动态类似日的概念，对于同一预测日的不同时刻，选出的类似日是随着时间的变
化而动态变化的。这样较好地解决了特殊天气和节假日历史数据少，不易组成训
练样本集的问题。该模型在天气条件发生较大变化时进行短时负荷预测有明显优
势，并可用于超短时负荷预测。
本文还指出在未来电力市场的环境下，电价因素也是一个必须在负荷预测模
型中加以考虑的因素，对电价因素和电力市场对短期负荷预测的影响进行了分析，
并讨论了负荷预测与电价预测的关系。
最后对全文进行了总结，并对短期负荷预测的发展趋势进行了展望。
关键词:神经网络模糊推理混沌非线性鲁棒性负荷预测
When the power systems are controlled on line, short-term load forecasting should be used for realizing reasonable distribution of generating and supplying electricity. Short-term load forecasting is important basis of safely assigning and economically running. The forecasting precision will directly affect the reliability, economy running and supplying power quality of power system. The primary work done in short-term load forecasting in the paper is as follows.Firstly, via vertical and horizontal pretreatment, the historical load data can furthermore show the load changing trend, which establishes the basis for short-term load forecasting model using these historical data. By using the autocorrelation function on input variables selection for short-term load forecasting model, the principle which effect is big at near place and is small at far place and autocorrelation function theory are combined to select input variables. Then a group of input variables selected can effectively explain load varying relation. This approach has clear theory foundation and very strong maneuverability, and it is a systemic and scientific method for input variables selection. By use of this method, we can get small input variables sets, the correlation of adopted input variables and forecasting hour load is most strong, and the forecasting effect is more exact.Secondly, a combined load forecasting model is presented by integrating neural networks and fuzzy logic. In the model, neural networks only settle historical load information. This not only shortens the learning time of neural networks, but also avoids improper study produced by slowness of neural networks to other information. Moreover, fuzzy logic deals with the factors which have great effect to load varying, such as air temperature and holidays, etc. According to the own characteristic of load varying, the memberships and fuzzy rules base are constructed, and the modifying of basic load heft is realized by fuzzy logic. Comparing with conventional neural networks forecasting models, the combined model adequately makes use of the neural network learning ability and absorbing of fuzzy logic to subjective experience. It fully considers the effect of factors such as air temperature and holidays to power load, which can enhance the load forecasting results veracity to a certain extent, especially weekends and holidays.Thirdly, in order to describe the non-linear relation of power load varying, a fuzzy neural networks short-term load forecasting model based on chaos mechanism is presented. For the sake of extending the scope of weight acting, the chaotic learning algorithm of weight values is introduced. The non-liner feedback item of weight value is employed in dynamic system equation of fuzzy neural networks weights space learning algorithm, which make the neural networks learning dynamics turn into chaos dynamics.
So the system can find the global minimal point or its approximation quickly. The fuzzy inference and defuzzification of the model are both realized by neural networks. The selected membership function made neural network weight values have definite knowledge meaning, and it can be analyzed and understood. In the model, two different fuzzy inference algorithms are put forward to finish fuzzy inference, and it is confirmed that the fuzzy multiplication inference algorithm can get better forecasting effect. The conventional centroid method is replaced by sample adding authority calculation in fuzzy inference. Under not reducing forecasting precision, the learning time is shortened, and the running efficiency is increased. The model preferably settles the slow convergence speed and low forecasting precision limitation of general BP algorithm, and has preferable forecasting capability for non-linear load varying.Fourthly, in order to overcome the shortcomings that forecasting error will increase evidently while weather varies rapidly on the forecasting day, a new on line real time load forecasting model with feedback and recurrent structure is proposed. In the model, the fo
引文
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