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中国电机工程学报 2007, 27(34) 90-95 DOI:
ISSN: 0258-8013 CN: 11-2107/TM |
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| 电力系统 |
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基于聚类和Bayesian推断的市场出清电价离散概率分布预测 |
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王高琴 沈炯 李益国 |
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东南大学能源与环境学院 东南大学能源与环境学院 东南大学能源与环境学院 |
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摘要：
电力市场中，市场出清电价(market clearing price, MCP)受到众多因素的共同作用，具有较强的随机性和不确定性，常规的MCP单值预测模型未充分利用历史数据反映的不确定性信息，预测结果无法体现MCP的随机变化特性，预测精度也有限。该文提出一种基于免疫遗传机制的聚类方法，用以实现历史数据输入输出映射关系的划分，并结合贝叶斯概率法则建立MCP离散概率分布的预测模型。对美国PJM市场数据的仿真结果显示，该文的预测模型能较好地反映MCP的不确定性特点，且具有较高的预测精度。
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关键词：
市场出清电价预测
线性聚类
免疫遗传算法
贝叶斯方法
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Forecasting of MCP 's Discrete Probability Distribution Based on Clustering and Bayesian Method |
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Abstract:
As being affected by too many factors, the energy market clearing price (MCP) in power markets is stochastic and volatile. Traditional MCP forecasting models usually make no good use of the uncertain information contained in the history data, the estimation can't reflect the volatility and the precision is limited. This paper proposes a linear clustering method based on the immune-genetic algorithm (IGA) and uses the method to divide the mapping relationship between the inputs and outputs in the history data. Besides, on the base of clustering, a forecasting model of MCP's discrete probability distribution by the Bayesian method is established. Simulation experiment on history data from American power market PJM shows the forecasting method proposed in this paper can perfectly reflect the stochastic volatility of MCP and has a good precision.
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Keywords:
forecasting of market clearing price
linear clustering
immune genetic algorithm
Bayesian method
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收稿日期 2006-06-16 修回日期 1900-01-01 网络版发布日期 |
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DOI: |
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基金项目:
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通讯作者: 王高琴 |
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作者简介: |
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作者Email: yusi803@163.com |
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| 参考文献： |
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| Copyright by 中国电机工程学报 |
