Forecast of Photovoltaic Power Based on IWPA-LSSVM Considering Weather Types and Similar Days

doi:10.11930/j.issn.1004-9649.202108059

Abstract

Abstract: In order to improve the prediction accuracy of photovoltaic power, the input of the photovoltaic power prediction model is determined according to the characteristics of photovoltaic output power under different weather types. Aiming at the defects of the wolf pack algorithm (WPA), an improved wolf pack algorithm (IWPA) was obtained by improving the walking position and running step of the wolf pack. The least squares support vector machine (lSSVM) was optimized by IWPA, and an IWPA-LSSVM based photovoltaic power prediction model was established considering weather types and similar days. The photovoltaic power generation data under different weather types were used for simulation, and the simulation results show that the proposed method has a higher prediction accuracy and the error fluctuation of regression fitting is smaller whether the weather is sunny, cloudy or rainy.

Key words: weather type, similar day, photovoltaic power, least squares support vector machine

XU Yilun, ZHANG Binqiao, HUANG Jing, XIE Xiao, WANG Ruoxin, SHEN Danqing, HE Lina, YANG Kaifan. Forecast of Photovoltaic Power Based on IWPA-LSSVM Considering Weather Types and Similar Days[J]. Electric Power, 2023, 56(2): 143-149.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202108059

https://www.electricpower.com.cn/EN/Y2023/V56/I2/143

References

[1] 刘斌, 谈竹奎, 唐赛秋, 等. 基于数据预测启发式算法的光伏电池参数识别[J]. 电力系统保护与控制, 2021, 49(23): 72–79
LIU Bin, TAN Zhukui, TANG Saiqiu, et al. Photovoltaic cell parameter extraction using data prediction based on a meta-heuristic algorithm[J]. Power System Protection and Control, 2021, 49(23): 72–79
[2] 管飞, 卫思明, 付文启, 等. 光伏电源经新能源同步机并网的仿真研究[J]. 智慧电力, 2021, 49(7): 23–30
GUAN Fei, WEI Siming, FU Wenqi, et al. Simulation of photovoltaic power connected to grid by motor-generator pair[J]. Smart Power, 2021, 49(7): 23–30
[3] LIU H F, GAO Q, MA P C. Photovoltaic generation power prediction research based on high quality context ontology and gated recurrent neural network[J]. Sustainable Energy Technologies and Assessments, 2021, 45: 101191.
[4] 刘倩, 胡强, 杨凌帆, 等. 基于时间序列的深度学习光伏发电模型研究[J]. 电力系统保护与控制, 2021, 49(19): 87–98
LIU Qian, HU Qiang, YANG Lingfan, et al. Deep learning photovoltaic power generation model based on time series[J]. Power System Protection and Control, 2021, 49(19): 87–98
[5] 解振学, 林帆, 王若谷, 等. 基于时序动态回归的超短期光伏发电功率预测方法[J]. 智慧电力, 2022, 50(7): 45–51
XIE Zhenxue, LIN Fan, WANG Ruogu, et al. Very short-term photovoltaic power forecasting method based on time series dynamic regression[J]. Smart Power, 2022, 50(7): 45–51
[6] MA H R, LIU Z, LI M, et al. A two-stage optimal scheduling method for active distribution networks considering uncertainty risk[J]. Energy Reports, 2021, 7: 4633–4641.
[7] BERMUDEZ-GARCIA A, VOARINO P, RACCURT O. Environments, needs and opportunities for future space photovoltaic power generation: a review[J]. Applied Energy, 2021, 290: 116757.
[8] MA H R, WANG B, GAO W Z, et al. Optimal scheduling of an regional integrated energy system with energy storage systems for service regulation[J]. Energies, 2018, 11(1): 195.
[9] 苏向敬, 刘一航, 张知宇, 等. 计及源荷不确定影响的不平衡配电网两阶段优化[J]. 电力系统保护与控制, 2022, 50(23): 94–103
SU Xiangjing, LIU Yihang, ZHANG Zhiyu, et al. Two-stage optimization of unbalanced distribution networks considering impacts of DG and load uncertainties[J]. Power System Protection and Control, 2022, 50(23): 94–103
[10] WEI D Q, WANG B, LIN G, et al. Research on unstructured text data mining and fault classification based on RNN-LSTM with malfunction inspection report[J]. Energies, 2017, 10(3): 406.
[11] 白斌, 韩明亮, 林江, 等. 含风电和光伏的可再生能源场景削减方法[J]. 电力系统保护与控制, 2021, 49(15): 141–149
BAI Bin, HAN Mingliang, LIN Jiang, et al. Scenario reduction method of renewable energy including wind power and photovoltaic[J]. Power System Protection and Control, 2021, 49(15): 141–149
[12] WANG B, FANG B W, WANG Y J, et al. Power system transient stability assessment based on big data and the core vector machine[J]. IEEE Transactions on Smart Grid, 2016, 7(5): 2561–2570.
[13] 万天虎, 李华, 唐浩, 等. 基于多主站协调控制的光伏电站一次调频应用研究[J]. 智慧电力, 2021, 49(4): 37–43
WAN Tianhu, LI Hua, TANG Hao, et al. Application & research on primary frequency modulation of photovoltaic power station based on multi-master coordinated control[J]. Smart Power, 2021, 49(4): 37–43
[14] 陈伟, 李旭斌, 纪青春, 等. 一种基于关联集和可用度的光伏发电系统维护策略[J]. 电力系统保护与控制, 2022, 50(14): 94–104
CHEN Wei, LI Xubin, JI Qingchun, et al. A maintenance strategy for a photovoltaic power generation system based on an associative set and availability[J]. Power System Protection and Control, 2022, 50(14): 94–104
[15] 李秉晨, 于惠钧, 刘靖宇. 基于Kmeans和CEEMD-PE-LSTM的短期光伏发电功率预测[J]. 水电能源科学, 2021, 39(4): 204–208
LI Bingchen, YU Huijun, LIU Jingyu. Prediction of short-term photovoltaic power generation based on kmeans and CEEMD-PE-LSTM[J]. Water Resources and Power, 2021, 39(4): 204–208
[16] 黄雨薇, 彭道刚, 姚峻, 等. 基于SSA和K均值的TD-BP神经网络超短期光伏功率预测[J]. 太阳能学报, 2021, 42(4): 229–238
HUANG Yuwei, PENG Daogang, YAO Jun, et al. Ultra-short-term photovoltaic power forecast of td-bp neural network based on ssa and k-means[J]. Acta Energiae Solaris Sinica, 2021, 42(4): 229–238
[17] 张洁, 郝倩男. 基于烟花算法优化BP神经网络的光伏功率预测[J]. 计算机技术与发展, 2021, 31(10): 146–153
ZHANG Jie, HAO Qiannan. Forecast of photovoltaic power generation based on firework algorithm optimized BP neural network[J]. Computer Technology and Development, 2021, 31(10): 146–153
[18] 李丰君, 王磊, 赵健, 等. 基于天气融合和LSTM网络的分布式光伏短期功率预测方法[J]. 中国电力, 2022, 55(11): 149–154
LI Fengjun, WANG Lei, ZHAO Jian, et al. Research on distributed photovoltaic short-term power prediction method based on weather fusion and LSTM-net[J]. Electric Power, 2022, 55(11): 149–154
[19] 刘海涛, 叶筱怡, 吕干云, 等. 基于BAS-SVM的配电网电压暂降源识别[J]. 中国电力, 2022, 55(5): 128–133
LIU Haitao, YE Xiaoyi, LÜ Ganyun, et al. Identification of voltage sag source in distribution network based on BAS-SVM[J]. Electric Power, 2022, 55(5): 128–133
[20] 杨茂, 杨宇. 基于小波包与LSSVM的短期光伏输出功率预测研究[J]. 可再生能源, 2019, 37(11): 1595–1602
YANG Mao, YANG Yu. Short-term photovoltaic output power prediction based on wavelet packet and LSSVM[J]. Renewable Energy Resources, 2019, 37(11): 1595–1602
[21] 汪友明, 穆恒星, 徐国宁. 基于改进粒子群优化支持向量机的光伏电池输出功率预测[J]. 电气自动化, 2019, 41(3): 63–65, 91
WANG Youming, MU Hengxing, XU Guoning. Prediction of output power of photovoltaic cells based on the improved particle swarm optimization support vector machine[J]. Electrical Automation, 2019, 41(3): 63–65, 91
[22] 时珉, 许可, 王珏, 等. 基于灰色关联分析和GeoMAN模型的光伏发电功率短期预测[J]. 电工技术学报, 2021, 36(11): 2298–2305
SHI Min, XU Ke, WANG Jue, et al. Short-term photovoltaic power forecast based on grey relational analysis and GeoMAN model[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2298–2305
[23] 孟安波, 陈嘉铭, 黎湛联, 等. 基于相似日理论和CSO-WGPR的短期光伏发电功率预测[J]. 高电压技术, 2021, 47(4): 1176–1184
MENG Anbo, CHEN Jiaming, LI Zhanlian, et al. Short-term photovoltaic power generation prediction based on similar day theory and CSO-WGPR[J]. High Voltage Engineering, 2021, 47(4): 1176–1184
[24] 余向阳, 赵怡茗, 杨宁宁, 等. 基于VMD-SE-LSSVM和迭代误差修正的光伏发电功率预测[J]. 太阳能学报, 2020, 41(2): 310–318
YU Xiangyang, ZHAO Yiming, YANG Ningning, et al. Photovoltaic power generation forecasting based on vmd-se-lssvm and iterative error correction[J]. Acta Energiae Solaris Sinica, 2020, 41(2): 310–318
[25] 方权, 刘闯, 宋敏, 等. 模糊评价与PSO优化的LSSVM架空输电线路故障率预测[J]. 水电能源科学, 2021, 39(1): 171–175
FANG Quan, LIU Chuang, SONG Min, et al. Failure rate prediction of overhead transmission line based on fuzzy evaluation and PSO-optimized LSSVM[J]. Water Resources and Power, 2021, 39(1): 171–175
[26] 刘闯, 何沁鸿, 卢银均, 等. 输电线路PSOEM-LSSVM覆冰预测模型[J]. 电力科学与技术学报, 2020, 35(6): 131–137
LIU Chuang, HE Qinhong, LU Yinjun, et al. PSOEM-LSSVM forecasting model for the transmission lines icing[J]. Journal of Electric Power Science and Technology, 2020, 35(6): 131–137
[27] 张志浩, 熊文洁, 钟文, 等. 基于改进PSO-DE融合算法优化LSSVM的短期风功率预测[J]. 山东电力技术, 2022, 49(10): 9–15
ZHANG Zhihao, XIONG Wenjie, ZHONG Wen, et al. Short term wind power forecast based on LSSVM optimized by improved PSO-DE fusion algorithm[J]. Shandong Electric Power, 2022, 49(10): 9–15
[28] 刘聪, 费炜, 胡胜. 狼群算法的研究与应用综述[J]. 科学技术与工程, 2020, 20(9): 3378–3386
LIU Cong, FEI Wei, HU Sheng. Review on research and application of wolf pack algorithm[J]. Science Technology and Engineering, 2020, 20(9): 3378–3386
[29] 吴虎胜, 张凤鸣, 吴庐山. 一种新的群体智能算法: 狼群算法[J]. 系统工程与电子技术, 2013, 35(11): 2430–2438
WU Husheng, ZHANG Fengming, WU Lushan. New swarm intelligence algorithm-wolf pack algorithm[J]. Systems Engineering and Electronics, 2013, 35(11): 2430–2438
[30] 魏鹏飞, 樊小朝, 史瑞静, 等. 基于改进麻雀搜索算法优化支持向量机的短期光伏发电功率预测[J]. 热力发电, 2021, 50(12): 74–79
WEI Pengfei, FAN Xiaochao, SHI Ruijing, et al. Short-term photovoltaic power generation forecast based on improved sparrow search algorithm optimized support vector machine[J]. Thermal Power Generation, 2021, 50(12): 74–79
[31] 李刚, 刘佳林, 王腾飞, 等. 基于相似日理论和IPSO-Elman模型的短期光伏发电功率预测[J]. 测控技术, 2020, 39(2): 91–97, 131
LI Gang, LIU Jialin, WANG Tengfei, et al. Short-term photovoltaic power forecast based on similar day theory and IPSO-Elman model[J]. Measurement & Control Technology, 2020, 39(2): 91–97, 131