引用本文: | 贾德香,吕干云,林 芬,等.基于SAPSO-BP和分位数回归的光伏功率区间预测[J].电力系统保护与控制,2021,49(10):20-26.[点击复制] |
JIA Dexiang,LÜ Ganyun,LIN Fen,et al.Photovoltaic power interval prediction based on SAPSO-BP and quantile regression[J].Power System Protection and Control,2021,49(10):20-26[点击复制] |
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摘要: |
提出了一种基于SAPSO-BP(模拟退火粒子群优化BP神经网络)和分位数回归的光伏功率区间预测方法。首先给出一种动态SAPSO-BP算法对光伏出力进行预测,该方法考虑BP的局部极小化和粒子群的早熟收敛等问题,能优化BP的参数设置,提高光伏功率的预测精度。在光伏功率确定性预测的基础上,通过分析不同天气类型下SAPSO-BP模型的预测功率误差。最后根据不确定天气因素建立分位数回归模型,实现对光伏输出功率的波动区间分析。该模型无需假设光伏预测功率误差分布,且计算方法简单,可提供在任意置信水平下,光伏预测功率的波动范围,为电力系统调度决策、运行风险评估提供更加丰富的信息。 |
关键词: 光伏功率预测 区间预测 SAPSO-BP 分位数回归 |
DOI:DOI: 10.19783/j.cnki.pspc.200906 |
投稿时间:2020-07-29修订日期:2020-11-16 |
基金项目:国家自然科学基金项目资助(51577086);江苏“六大人才高峰”资助(2016-XNY027,TD-XNY004);国家电网公司总部科技项目资助(5400-202040230A0000);江苏省高校科研重大项目资助(19KJA510012) |
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Photovoltaic power interval prediction based on SAPSO-BP and quantile regression |
JIA 1Dexiang,LÜ Ganyun2,LIN Fen3,WU Chenyuan2,ZHANG Xinyin2 |
(1. State Grid Energy Research Institute Co., Ltd., Beijing 102209, China; 2. School of Electric Power Engineering, Nanjing
Institute of Technology, Nanjing 211167, China; 3. State Grid Fujian Electric Power Co., Ltd., Fuzhou 350003, China) |
Abstract: |
A photovoltaic power interval prediction is proposed based on an optimized Back Propagation (BP) neural network by Simulated Annealing Modified Particle Swarm Optimization (SAPSO-BP) and quantile regression. First, a dynamic SAPSO algorithm is used to predict the Photovoltaic (PV) output, taking the local minimization of BP and precocious convergence of the particle swarm into account. Then it optimizes the parameter setting of the BP neural network and improves the accuracy of prediction. From a deterministic prediction of PV power, the prediction power error of the SAPSO-BP model under different weather types is analyzed. Finally, a quantile regression model is established based on uncertain weather factors to realize the interval analysis of future power fluctuation. The model does not have to predict the distribution of the PV predicted power error, and the calculation method is simple. The fluctuation range of the predicted power of the PV can be provided at any confidence level. The model provides more abundant information for power system scheduling decision and operational risk assessment.
This work is supported by the National Natural Science Foundation of China (No. 51577086). |
Key words: PV power prediction interval prediction SAPSO-BP quantile regression |