An Efficient Hybrid Intelligent Method for Electricity Price Forecasting
An Efficient Hybrid Intelligent Method for Electricity Price Forecasting
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一种高效的混合智能电价预测方法
DOI：
10.1016/j.procs.2016.09.337
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
H. Mori and K. Nakano
中科院分区：
文献类型：
--
作者：
王;板子;H. Mori and S. Itaba;H. Mori and K. Nakano
本文提出了一种有效的电价预测方法。本文研究了通过缓解输电网阻塞来有效维持电力市场运行的负荷边际电价（LMP）。电力市场中的不确定性因素使得电力市场中的时间序列具有复杂的行为。从市场参与者的角度来看，需要一种复杂的方法来有效地预测LMP。该方法利用了层次贝叶斯估计的高斯过程（GP）、进化计算的进化粒子群优化（EPSO）和允许数据属于两个或多个聚类的模糊c均值算法的混合。EPSO算法用于提高GP最大后验概率（MAP）估计中参数的精度。模糊c均值的使用对于增加GP处理尖峰的学习数据的数量是有用的。通过对真实的LMP数据的处理，验证了该方法的有效性.
In this paper an efficient method is proposed for electricity price forecasting. This paper focuses on Locational Marginal Price (LMP) that efficiently maintains power markets by alleviating transmission network congestion. There are complicated behaviors of the time series due to uncertain factors in the power markets. From a standpoint of market players, a sophisticated method is required to forecast LMP effectively. The proposed method makes use of the hybridization of GP (Gaussian Process) of hierarchical Bayesian estimation, EPSO (Evolutionary Particle Swarm Optimization) of evolutionary computation and fuzzy c-means of allowing data to belong to two or more clusters. EPSO is used to improve the accuracy of parameters in MAP (Maximum a Posteriori) estimation for GP. The use of fuzzy c-mean is useful for increasing the number of learning data for GP to deal with spikes. The effectiveness of the proposed method is demonstrated for real LMP data.