The Optimization Research Approaches for Renewable Energy Output Forecasting

Jie SHI; Xiaofei LIU

doi:10.12096/j.2096-4528.pgt.18141

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The Optimization Research Approaches for Renewable Energy Output Forecasting

New and Renewable Energy | 更新时间：2025-06-06

- The Optimization Research Approaches for Renewable Energy Output Forecasting
- Power Generation Technology Vol. 40, Issue 1, Pages: 78-82(2019)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(51606085)
- DOI：10.12096/j.2096-4528.pgt.18141
  CLC：
- Received：05 August 2018，
  
  Published：26 February 2019
- 稿件说明：
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Jie SHI, Xiaofei LIU. The Optimization Research Approaches for Renewable Energy Output Forecasting[J]. Power Generation Technology, 2019, 40(1): 78-82.
DOI：

Jie SHI, Xiaofei LIU. The Optimization Research Approaches for Renewable Energy Output Forecasting[J]. Power Generation Technology, 2019, 40(1): 78-82. DOI： 10.12096/j.2096-4528.pgt.18141.

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。

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.

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references

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