基于多元理论融合的电力系统短期负荷预测的研究
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摘要
电力系统短期负荷预测是电力系统正常、安全调度操作、经济运行的重要
依据。引入电力市场竞争机制以来,各电力公司都要制定出合理经济的实时电
价模型,它对负荷预测的准确性和快速性提出了更高的要求。
本文在基于多元理论的电力系统短期负荷预测研究中开展了如下几方面的
工作。
通过对目前国内外短期负荷预测研究动态作了仔细分析,对各种原理和方
法的特点及所存在的问题从本质上作了深入分析,说明了本文研究工作的必要
性和重要意义。
以电力系统负荷序列的性质是属于复杂非线性、混沌性的时间序列为依据,
从基于多元理论的不同角度建立了多个 STLF 模型,并通过实际负荷预测系统
的仿真,验证了模型的有效性。本文创造性地提出了最优近邻点法,该判定法
直接定量运用轨道演化特性指数,动力学行为概念清晰;能有效剔除相空间伪
近邻点及其对局域动力学估计的不利影响。本文首次提出了基于多元理论、整
体动力学行为机理的 STLF 模型。不仅从模型的结构方面保证,而且要赋予其
内在动力学行为性能,即构造动力学的预测模型必须内、外统一;局部和整体
统一。
本文首次提出了改进混沌神经网络(ICNN)预测模型,所提出的 ICNN,
是用修正 Aihara 混沌神经元构造。ICNN 对初始值和混沌轨迹的游动性有强的
敏感性,它能刻画复杂的动力学行为,并具全局寻优性能,预测性能明显优于
其它动态 NN。首次提出基于 ONP+ICNN 融合的预测模型,它是本文几个主要创
新之处的融合,它可使预测性能实现高精度并具有高的稳定性。
本文创造性地提出了基于数据挖掘改进算法的电力系统负荷序列聚类分析
方法,首次提出负荷序列间差分序列方差的概念。在基于 DM 聚类改进算法对
夏季高温负荷进行二次“细化”聚类的基础上,首次提出了两种外部随机因素
负荷预测模型——基于分布式模糊 NN 群模型和基于分布式 DGA-NN 群模型,
通过负荷系统实例仿真证实,所提出模型能有效、稳定地提高预测精度,为所
提出模型用于实际工程取得了有效的理论探索。
The Short-term Load Forecasting (STLF) plays an important role in power
industry. Economic and reliable operation of power systems depends significantly
on the accuracy of the load forecasting. In a deregulated and competitive power
market, it requires that the precision of STLF improved effectively and steadily.
The main work and achievement about the research of the STLF model based
on the multi-theory in this paper is generalized as follows.
Through the particular analysis of the study trend of STLF at home and abroad,
the existent problems of all kinds of principle and method are analyzed in essence,
and the necessity and significance of study work in this paper is explained.
The load series of electric power systems is a complicated nonlinear time series
and it possess chaotic characteristic, and several STLF models are proposed based
on the multi-theory. The testing results show that the proposed models can improve
effectively and steadily the precision of STLF. The Optimal Neighbor Points (ONP)
Approach is creatively presented, and it can eliminate some false neighbor points
through identifying exponential separating rate of time evolutional trajectory and
can improve the precision of load forecasting. The STLF model based on the
multi-theory and the mechanism of the global dynamical behavior is first proposed.
It can be ensured not only by the structure of the model, but also by the inherent
performance of dynamical behavior.
The STLF model based on the Improved Chaotic Neural Networks (ICNN) is
firstly proposed in this paper, and the ICNN model is built based on modified Aihara
chaotic neuron. The ICNN model possesses the sensitivity to the initial load value
and to the walking of the whole chaotic track. And it can characterize complicated
dynamics behavior and has global searching optimal ability, and its forecasting
performance is preferable to other dynamic neural networks. The STLF approach is
further proposed based on the fusion of ONPA and ICNN model, and it is the fusion
of several innovative points, and the testing results show that the proposed model
and its algorithm can improve effectively and steadily the precision of STLF.
The clustering analysis approach of electric load series based on improved data
mining arithmetic is creatively proposed in the paper, and it proposed the notion of
the variance of difference sequence. In the basis of clustering result of load series in
the summer, the correction model based on the distributed fuzzy neural network and
the correction model based on the distributed DGA-neural network are proposed.
The testing results prove the proposed model and approach can improve effectively
and stably the precision of STLF. This research acquires the effective progression
and practical significance in the prediction engineering.
依据。引入电力市场竞争机制以来,各电力公司都要制定出合理经济的实时电
价模型,它对负荷预测的准确性和快速性提出了更高的要求。
本文在基于多元理论的电力系统短期负荷预测研究中开展了如下几方面的
工作。
通过对目前国内外短期负荷预测研究动态作了仔细分析,对各种原理和方
法的特点及所存在的问题从本质上作了深入分析,说明了本文研究工作的必要
性和重要意义。
以电力系统负荷序列的性质是属于复杂非线性、混沌性的时间序列为依据,
从基于多元理论的不同角度建立了多个 STLF 模型,并通过实际负荷预测系统
的仿真,验证了模型的有效性。本文创造性地提出了最优近邻点法,该判定法
直接定量运用轨道演化特性指数,动力学行为概念清晰;能有效剔除相空间伪
近邻点及其对局域动力学估计的不利影响。本文首次提出了基于多元理论、整
体动力学行为机理的 STLF 模型。不仅从模型的结构方面保证,而且要赋予其
内在动力学行为性能,即构造动力学的预测模型必须内、外统一;局部和整体
统一。
本文首次提出了改进混沌神经网络(ICNN)预测模型,所提出的 ICNN,
是用修正 Aihara 混沌神经元构造。ICNN 对初始值和混沌轨迹的游动性有强的
敏感性,它能刻画复杂的动力学行为,并具全局寻优性能,预测性能明显优于
其它动态 NN。首次提出基于 ONP+ICNN 融合的预测模型,它是本文几个主要创
新之处的融合,它可使预测性能实现高精度并具有高的稳定性。
本文创造性地提出了基于数据挖掘改进算法的电力系统负荷序列聚类分析
方法,首次提出负荷序列间差分序列方差的概念。在基于 DM 聚类改进算法对
夏季高温负荷进行二次“细化”聚类的基础上,首次提出了两种外部随机因素
负荷预测模型——基于分布式模糊 NN 群模型和基于分布式 DGA-NN 群模型,
通过负荷系统实例仿真证实,所提出模型能有效、稳定地提高预测精度,为所
提出模型用于实际工程取得了有效的理论探索。
The Short-term Load Forecasting (STLF) plays an important role in power
industry. Economic and reliable operation of power systems depends significantly
on the accuracy of the load forecasting. In a deregulated and competitive power
market, it requires that the precision of STLF improved effectively and steadily.
The main work and achievement about the research of the STLF model based
on the multi-theory in this paper is generalized as follows.
Through the particular analysis of the study trend of STLF at home and abroad,
the existent problems of all kinds of principle and method are analyzed in essence,
and the necessity and significance of study work in this paper is explained.
The load series of electric power systems is a complicated nonlinear time series
and it possess chaotic characteristic, and several STLF models are proposed based
on the multi-theory. The testing results show that the proposed models can improve
effectively and steadily the precision of STLF. The Optimal Neighbor Points (ONP)
Approach is creatively presented, and it can eliminate some false neighbor points
through identifying exponential separating rate of time evolutional trajectory and
can improve the precision of load forecasting. The STLF model based on the
multi-theory and the mechanism of the global dynamical behavior is first proposed.
It can be ensured not only by the structure of the model, but also by the inherent
performance of dynamical behavior.
The STLF model based on the Improved Chaotic Neural Networks (ICNN) is
firstly proposed in this paper, and the ICNN model is built based on modified Aihara
chaotic neuron. The ICNN model possesses the sensitivity to the initial load value
and to the walking of the whole chaotic track. And it can characterize complicated
dynamics behavior and has global searching optimal ability, and its forecasting
performance is preferable to other dynamic neural networks. The STLF approach is
further proposed based on the fusion of ONPA and ICNN model, and it is the fusion
of several innovative points, and the testing results show that the proposed model
and its algorithm can improve effectively and steadily the precision of STLF.
The clustering analysis approach of electric load series based on improved data
mining arithmetic is creatively proposed in the paper, and it proposed the notion of
the variance of difference sequence. In the basis of clustering result of load series in
the summer, the correction model based on the distributed fuzzy neural network and
the correction model based on the distributed DGA-neural network are proposed.
The testing results prove the proposed model and approach can improve effectively
and stably the precision of STLF. This research acquires the effective progression
and practical significance in the prediction engineering.
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