| 短期太阳能光伏发电预测方法研究进展 |
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| 引用本文： | 崔洋,孙银川,常俾林.短期太阳能光伏发电预测方法研究进展[J].资源科学,2013,35(7):1474-1481. |
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| 作者姓名： | 崔洋 孙银川 常俾林 |
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| 作者单位： | 1. 宁夏气象防灾减灾重点实验室,银川750002;宁夏气候中心,银川750002
2. 宁夏气象防灾减灾重点实验室,银川,750002 |
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| 基金项目： | 科技部公益性行业(气象)科研专项:“太阳能预报技术研究”(编号:GYHY201006036);宁夏气象防灾减灾重点实验室项目:“宁夏太阳能发电预测方法及系统研究”。 |
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| 摘 要： | 提高短期光伏发电预测水平是太阳能光伏发电站并入现有电网系统和太阳能光伏开发利用的关键问题,对提高太阳能光伏发电开发利用、保证并网安全也具有重要意义.本文对国内外短期太阳能光伏发电预测方法进行了分类归纳总结,对各类方法的发展趋势、优缺点等进行了分析.结果表明,统计智能类预测方法是国内外小型光伏电站短期光伏发电量预测技术发展的重点,总体平均预测误差在3.0％～ 11.0％之间.简单物理模型类预测方法是目前国内外大中型并网光伏电站业务运行采用最多的短期光伏发电量预测方法,总体平均预测误差在5.0％～20.0％之间.复杂物理模型类预测方法是未来大型光伏电站短期发电量预测技术研究和发展应用的主要方向.文章结论对我国短期太阳能光伏发电预测技术的发展具有促进作用.
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| 关 键 词： | 太阳能光伏发电 短期功率预测 直接预测法 间接预测法 预测模型 |
| 收稿时间： | 1/9/2013 12:00:00 AM |
A Review of Short-term Solar Photovoltaic Power Generation Prediction Methods |
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| CUI Yang,SUN Yinchuan and CHANG Zhuolin.A Review of Short-term Solar Photovoltaic Power Generation Prediction Methods[J].Resources Science,2013,35(7):1474-1481. |
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| Authors: | CUI Yang SUN Yinchuan and CHANG Zhuolin |
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| Institution: | Key Laboratory of Meteorological Disaster Preventing and Reducing in Ningxia, Yinchuan 750002, China;Ningxia Climate Center, Yinchuan 750002, China;Key Laboratory of Meteorological Disaster Preventing and Reducing in Ningxia, Yinchuan 750002, China;Ningxia Climate Center, Yinchuan 750002, China;Key Laboratory of Meteorological Disaster Preventing and Reducing in Ningxia, Yinchuan 750002, China |
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| Abstract: | Enhancing the prediction accuracy of short-term photovoltaic power generation is not only crucial for the PV grid-connected, but also for solar photovoltaic utilization. In this paper, the present situation and trend of short-term solar photovoltaic power generation forecasting techniques are comprehensively discussed and analyzed. First, all kinds of short-term solar photovoltaic power generation forecasting methods are introduced. Second, short-term solar photovoltaic power generation forecasting methods are divided into two classes. One class is statistical forecast methods, and the other is physical forecast methods. Last, we discuss the prospects of short-term solar photovoltaic power generation forecasting techniques in the future. Our review shows that artificial intelligence predicting statistical methods will be the key tool for small-scale short-term power generation prediction, as prediction errors are generally in the range of 3%~11% at present. Complex physical model methods are new short-term solar photovoltaic power generation forecasting techniques, and take full account of weather conditions such as precipitation, snow and cloud. Although these methods are in the early phase of theoretical research, some results show that these methods will be the main development and application direction for large-scale photovoltaic power plant short-term power generation prediction. |
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| Keywords: | Solar photovoltaic generation Short-term power prediction Direct predictive method Indirect predictive method Prediction model |
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