发电技术 ›› 2019, Vol. 40 ›› Issue (1): 78-82.DOI: 10.12096/j.2096-4528.pgt.18141

• 新能源 • 上一篇    下一篇

新能源功率预测算法优化研究

史洁1(),刘晓飞2   

  1. 1 济南大学物理科学与技术学院, 山东省 济南市 250022
    2 济南市城市规划咨询服务中心, 山东省 济南市 250099
  • 收稿日期:2018-08-05 出版日期:2019-02-28 发布日期:2019-02-26
  • 作者简介:史洁(1984),女,博士,讲师,研究方向为风电、光伏出力预测及优化, jeccie0921@163.com
  • 基金资助:
    国家自然科学基金项目(51606085)

The Optimization Research Approaches for Renewable Energy Output Forecasting

Jie SHI1(),Xiaofei LIU2   

  1. 1 School of Physics and Technology, University of Jinan, Jinan 250022, Shandong Province, China
    2 Jinan Urban Planning Advisory Service Center, Jinan 250099, Shandong Province, China
  • Received:2018-08-05 Published:2019-02-28 Online:2019-02-26
  • Supported by:
    National Natural Science Foundation of China(51606085)

摘要:

以风能和太阳能为代表的新能源具有随机性、间歇性和波动性,对新能源发电功率进行预测是有效解决以上问题的途径。在确定性预测中充分考虑风电出力和预测模型特性,提出分段支持向量机(piecewise support vector machine,PSVM)和神经网络(neural network,NN)预测算法;充分考虑天气特征对光伏出力的影响,提出基于气象特性分析的光伏出力预测算法。通过若干风电场的算例分析,证明了上述几种预测模型的实用性,为功率预测的可靠性分析提供支持。

关键词: 风电, 光伏, 功率预测, 支持向量机, 神经网络, 小波分析

Abstract:

Randomness, intermittence and fluctuation are features of new energy, which includes wind energy and solar energy, and power forecasting is an effective solution. The characteristics of wind power output and forecasting model are fully considered to propose piecewise support vector machine (PSVM) and neural network (NN) model; the effort of weather condition on photovoltaic is analyzed to optimize the forecasting model. The case studies from several wind farms and photovoltaic power stations prove that the proposed models have higher precision, which offer support for reliability analysis of power output forecasting.

Key words: wind power, photovoltaic, power forecasting, support vector machine, neural network, wavelet analysis