Photovoltaic power forecasting based on harmony search and Gaussian process algorithms

被引：0
|
作者：
Li, Yuancheng [1 ]
Wang, Bei [1 ]
Wang, Xufeng [1 ]
机构：
[1] State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2014年 / 34卷 / 08期
关键词：
Electric power generation; Forecasting; Gaussian process; Grid-connection; Harmony search; Models; Optimization; Output power; Photovoltaic cells;
D O I：
10.3969/j.issn.1006-6047.2014.08.003
中图分类号：
学科分类号：
摘要：
The integration of photovoltaic power generation into power grid has impact on its stability. The power characteristics of photovoltaic power generation are analyzed and the principle of Gaussian process algorithm is researched, based on which, a photovoltaic power forecasting model is built. Instead of the conjugate gradient method, the harmony search algorithm is applied in the built model to optimize the hyper-parameter. Simulative results show that, the Gaussian process algorithm optimized by the harmony search has higher accuracy than the traditional one.
引用
收藏
页码：13 / 18
页数：5
相关论文
共 19 条
  • [1] Liu T., Xu G., Cai P., Development forecast of renewable energy power generation in China and its influence on the GHG control strategy of the country, Renewable Energy, 36, 2, pp. 1284-1292, (2011)
  • [2] Liu L.Q., Wang Z.X., Zhang H.Q., Solar energy development in China-a review, Renewable and Sustainable Energy Reviews, 14, 1, pp. 301-311, (2010)
  • [3] Reikard G., Predicting solar radiation at high resolutions: a comparison of time series forecasts, Solar Energy, 83, 3, pp. 342-349, (2009)
  • [4] Wang X., Ge P., PV array output power fore-casting based on similar day and RBFNN, Electric Power Automation Equipment, 33, 1, pp. 100-103, (2013)
  • [5] Chen C., Duan S., Yin J., Design of photo-voltaic array power forecasting model based on neutral network, Transactions of China Electrotechnical Society, 24, 9, pp. 153-158, (2009)
  • [6] Ding M., Wang L., Bi R., A short-term prediction model to forecast output power of photovoltaic system based on improved BP neural network, Power System Protection and Control, 40, 11, pp. 93-99, (2012)
  • [7] Shi J., Li W.J., Liu Y.Q., Et al., Forecasting power output of photovoltaic systems based on weather classification and support vector machines, 48, 3, pp. 1064-1069, (2012)
  • [8] Li R., Li G., Photovoltaic power generation output fore-casting based on support vector machine regression technique, Electric Power, 41, 2, pp. 74-78, (2008)
  • [9] Li Y.L., Wolfs P.J., A hybrid model for residential loads in a distribution system with high PV penetration, 28, 3, pp. 3372-3379, (2013)
  • [10] Liu Y.Q., Shi J., Yang Y.P., Et al., Short-term wind-power pre-diction based on wavelet transform-support vector machine and statistic-characteristics analysis, 48, 4, pp. 1136-1141, (2012)
12