Grid-connected power forecasting of concentrating solar power plants based on clustering and long short-term memory neural network

YU Yang; CHEN Geng; YU Jialei; LI Lianping; SHI Jinwen; LI Mingtao

doi:10.19666/j.rlfd.202101048

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Grid-connected power forecasting of concentrating solar power plants based on clustering and long short-term memory neural network

更新时间：2025-09-28

- Grid-connected power forecasting of concentrating solar power plants based on clustering and long short-term memory neural network
- Thermal Power Generation Vol. 50, Issue 9, Pages: 128-136(2021)
- 作者机构：
  
  1. 中国平煤神马集团尼龙科技有限公司
  2. 西安交通大学动力工程多相流国家重点实验室国际可再生能源研究中心
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202101048
  CLC： TM615;TP183
- Published：2021
- 稿件说明：
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YU Yang, CHEN Geng, YU Jialei, et al. Grid-connected power forecasting of concentrating solar power plants based on clustering and long short-term memory neural network[J]. Thermal power generation, 2021, 50(9): 128-136.
DOI：

YU Yang, CHEN Geng, YU Jialei, et al. Grid-connected power forecasting of concentrating solar power plants based on clustering and long short-term memory neural network[J]. Thermal power generation, 2021, 50(9): 128-136. DOI： 10.19666/j.rlfd.202101048.

摘要

光热电站发电量预测技术有助于电网提前制定调度计划

有效减轻光热发电间歇性和波动性在并网运行后带来的冲击。本文以美国加州光热发电系统并网电力预测问题为例

首先研究气象参数与发电量之间的相关性

进而利用近邻传播(AP)聚类算法对比不同发电模式下气象参数对发电的影响差异;然后

建立长短期记忆(LSTM)神经网络基准模型对光热系统发电量进行预测;最后

针对基准模型对0发电量情况预测精度低的问题

根据相应聚类结果建立增量特征预测模型。预测结果表明

该改进的基于AP聚类和LSTM神经网络模型预测效果良好

可达到91.41%的日发电量预测精度。

Abstract

Power generation forecasting technology for concentrated solar power(CSP) plants can help the grid to make scheduling plans in advance and effectively mitigate the impact of intermittency and fluctuation after gridconnected operation of CSP system. Taking the grid-connected power forecasting problem of CSP system in California

USA as an example

this paper investigates the correlation between meteorological parameters and power generation at first

and then uses the affinity propagation(AP) clustering algorithm to compare the differences in the impact of meteorological parameters on power generation under different generation patterns. Based on this

a long short-term memory(LSTM) neural network benchmark model is built to predict the power generation. Finally

to address the problem of low accuracy of the benchmark model in predicting the zero generation case

an incremental feature prediction model is developed based on the corresponding clustering results. The results show that the improved model works well and can achieve 91.41% accuracy in predicting daily power generation.

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