针对非晴空天气下光伏发电功率剧烈波动导致的预测准确度不足问题,提出一种基于饥饿游戏搜索算法（HGS）优化变分模态分解（VMD）和大脑情绪神经网络（ENN）的光伏发电功率超短期混沌预测模型。首先,为提高VMD自适应性,将HGS算法用于VMD核心参数寻优,并设计一种考虑加权排列熵和分解损失的HGS-VMD适应度函数,降低分解分量的复杂性和残差分量对预测结果的影响。其次,采用改进C-C法对VMD分解分量重构系统相空间,并将相空间重构矩阵输入ENN模型进行单步滚动预测。最后,基于实测光伏发电功率数据对所提预测模型进行仿真验证,结果表明所提预测模型能有效提高光伏发电功率在非晴空天气下的预测准确度。
Abstract
Aiming at the problem of insufficient prediction accuracy due to the drastic fluctuation of PV power under non-clear-skg weather conditions, an ultra-short-term PV power chaotic prediction model based on hunger games search （HGS） optimized variational mode decomposition （VMD） and emotional neural network （ENN） is proposed. Firstly, the HGS algorithm is used for optimization of VMD core parameters to improve the adaptivity of VMD, and an HGS-VMD fitness function considering weighted permutation entropy and decomposition loss is designed to reduce the complexity of the decomposition component and the influence of the residual component on the prediction results. Then, the phase space is reconstructed using the improved C-C method for the VMD decomposition components, and after extracting their regularity information, the phase space reconstruction matrix is input into the ENN model for prediction. Finally, the proposed prediction model is verified by simulation based on the measured PV power data, and the results show that the proposed prediction model can effectively improve the prediction accuracy of PV power under non-clear-sky weather conditions.
关键词
光伏发电 /
功率预测 /
变分模态分解 /
混沌理论 /
大脑情绪神经网络 /
功率波动
Key words
photovoltaic power generation /
power prediction /
variational mode decomposition /
chaos theory /
brain emotional neural network /
power fluctuation
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 舒印彪, 赵勇, 赵良, 等. “双碳”目标下我国能源电力低碳转型路径[J]. 中国电机工程学报, 2023, 43(5): 1663-1672.
SHU Y B, ZHAO Y, ZHAO L, et al.Study on low carbon energy transition path toward carbon peak and carbon neutrality[J]. Proceedings of the CSEE,2023, 43(5): 1663-1672.
[2] 辛保安, 单葆国, 李琼慧, 等. “双碳”目标下“能源三要素”再思考[J]. 中国电机工程学报, 2022, 42(9): 3117-3126.
XIN B A, SHAN B G, LI Q H, et al.Rethinking of the “Three Elements of Energy” toward carbon peak and carbon neutrality[J]. Proceedings of the CSEE, 2022, 42(9): 3117-3126.
[3] 王清亮,杨博,应欣峰,等.非晴空条件下光伏发电短期功率预测方法[J]. 太阳能学报, 2022, 43(3): 188-196.
WANG Q L, YANG B, YING X F, et al.Short-term photovoltaic power forecasting method under non-clear sky condition[J]. Acta energiae solaris sinica, 2022, 43(3):188-196.
[4] YANG T, YANG Z N, LI F, et al.A short-term wind power forecasting method based on multivariate signal decomposition and variable selection[J]. Applied energy,2024, 360: 122759.
[5] 朱菊萍, 魏霞, 谢丽蓉, 等. 基于VMD和改进BiLSTM的短期风电功率预测[J]. 太阳能学报, 2024, 45(6): 422-428.
ZHU J P, WEI X, XIE L R, et al.Short-term wind power peediction based on VMD and improved BiLSTM[J]. Acta energiae solaris sinica, 2024, 45(6): 422-428.
[6] LIU Y F, LIU Y, CAI H H, et al.An innovative short-term multihorizon photovoltaic power output forecasting method based on variational mode decomposition and a capsule convolutional neural network[J]. Applied energy, 2023, 343: 121-139.
[7] FU W L, WANG K, LI C S,et al.Multi-step short-term wind speed forecasting approach based on multi-scale dominant ingredient chaotic analysis, improved hybrid GWO-SCA optimization and ELM[J]. Energy conversion and management, 2019, 187: 356-377.
[8] GAO T, NIU D X, JI Z S, et al.Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm[J]. Energy, 2022, 261: 125328.
[9] 李青, 张新燕, 马天娇, 等. 基于ECBO-VMD-WKELM的风电功率超短期多步预测[J]. 电网技术, 2021, 45(8): 3070-3080.
LI Q, ZHANG X Y, MA T J, et al.Multi-step ahead ultra-short term forecasting of wind power based on ECBO-VMD-WKELM[J]. Power system technology, 2021, 45(8): 3070-3080.
[10] 张淑清, 李君, 姜安琦, 等. 基于FPA-VMD和BiLSTM神经网络的新型两阶段短期电力负荷预测[J]. 电网技术, 2022, 46(8): 3269-3279.
ZHANG S Q,LI J, JIANG A Q, et al.A novel two-stage model based on FPA-VMD and BiLSTM neural network for short-term power load forecasting[J]. Power system technolog, 2022, 46(8): 3269-3279.
[11] 王育飞, 杨启星, 薛花. 考虑混沌特征的增强型大脑情绪神经网络光伏发电功率超短期预测模型[J]. 高电压技术, 2021, 47(4): 1165-1177.
WANG Y F, YANG Q X, XUE H.Ultra-short-term prediction model of enhanced brain emotional neural network considering chaotic characteristics for photovoltaic power generation[J]. High voltage engineering, 2021, 47(4):1165-1177.
[12] XU X F, HU S T, SHI P M, et al.Natural phase space reconstruction-based broad learning system for short-term wind speed prediction: case studies of an offshore wind farm[J]. Energy, 2023, 262: 125342.
[13] FU W L, ZHANG K, WANG K, et al.A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM[J]. Renewable energy, 2021, 164: 211-229.
[14] WANG Y F, YANG Q X, XUE H, et al.Ultra-short-term PV power prediction model based on HP-OVMD and enhanced emotional neural network[J]. IET renewable power generation, 2022, 16(11): 2233-2247.
[15] 张琰妮, 史加荣, 李津, 等. 融合残差与VMD-ELM-LSTM的短期风速预测[J]. 太阳能学报, 2023, 44(9):340-347.
ZHANG Y N, SHI J Y, LI J, et al.Short-term wind speed prediction based on residual and VMD-ELM-LSTM[J]. Acta energiae solaris sinica, 2023, 44(9): 340-347.
[16] 王涛, 李薇, 许野, 等. 计及相似日的VMD-FE-LSTM光伏出力组合预测模型研究[J]. 太阳能学报, 2024, 45(5): 490-499.
WANG T, LI W, XU Y, et al.Study on photovoltaic power prediction of VMD-FE-LSTM considering similar days[J]. Acta energiae solaris sinica,2024, 45(5): 490-499.
[17] YANG Y T, CHEN H L, AMIRH G.Hunger games search: visions, conception, implementation, deep analysis, perspectives, and towards performerce shifts[J]. Expert systems with applications, 2021, 177: 114864.
基金
国家自然科学基金（61873159）; 上海市科技创新行动计划（22010501400）
PDF(1415 KB)
