基于改进GRU-CNN的风光水一体化超短期功率预测方法

doi:10.11930/j.issn.1004-9649.202209120

中国电力 ›› 2023, Vol. 56 ›› Issue (9): 178-186,205.DOI: 10.11930/j.issn.1004-9649.202209120

基于改进GRU-CNN的风光水一体化超短期功率预测方法

吴晓刚¹, 阎洁², 葛畅², 唐雅洁³, 倪筹帷³, 季青锋¹

1. 国网浙江省电力有限公司丽水供电公司，浙江丽水 323000;
2. 新能源电力系统国家重点实验室（华北电力大学），北京 102206;
3. 国网浙江省电力有限公司电力科学研究院，浙江杭州 310014

收稿日期:2022-09-29 修回日期:2023-08-18 发布日期:2023-09-20
作者简介:吴晓刚(1972-),男,高级工程师,从事电力系统调度运行、保护自动化技术研究,E-mail:wuxiaogang@zj.sgcc.com.cn;阎洁(1987-),女,通信作者,博士,教授,从事风功率预测、风电场运行控制技术研究,E-mail:yanjie@ncepu.edu.cn
基金资助:
国网浙江省电力公司科技项目（浙江丽水公司分布式清洁能源系统多尺度功率预测技术研究，5211LS21N003）。

Ultra-Short-Term Power Forecasting Method for Wind-Solar-Hydro Integration Based on Improved GRU-CNN

WU Xiaogang¹, YAN Jie², GE Chang², TANG Yajie³, NI Chouwei³, JI Qingfeng¹

1. Lishui Power Supply Company of State Grid Zhejiang Power Co., Ltd., Lishui 323000, China;
2. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;
3. Electric Power Research Institute of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310014, China

Received:2022-09-29 Revised:2023-08-18 Published:2023-09-20
Supported by:
This work is supported by Science and Technology Project of State Grid Zhejiang Electric Power Co., Ltd. (No.5211LS21N003).

摘要/Abstract

摘要： 风、光、水能源系统模型差异性大，相互存在多重不确定性，高精度的风、光、水功率预测是充分发挥风、光、水互补特性的重要前提。为此，基于门控循环单元（GRU）及卷积神经网络（CNN），提出了一种能够考虑异质能源时序特性及空间关联特性的一体化超短期功率预测方法。先分析了区域内不同场站不同数据的关联特性，再通过引入时序注意力机制，基于改进的GRU-CNN网络，建立了历史气象、功率数据与未来功率数据的映射关系，实现了多场站联合超短期预测。算例结果表明：所提预测方法能够实现区域风光水电站的一体化高精度超短期功率预测，效果优于单场预测及常规联合预测方法，且有着更高的建模效率。

关键词: 风光水一体化预测, 超短期功率预测, 联合预测, 注意力机制

Abstract: The models of wind, solar, and hydro energy systems are very different, and there are multiple uncertainties among them. High-precision power forecasting technology for wind, solar, and hydro is an important prerequisite for giving full play to the complementary characteristics of wind, solar, and hydro. To this end, an integrated ultra-short-term power forecasting method is proposed based on gated recurrent units (GRUs) and convolutional neural networks (CNNs), which can consider the temporal and spatial correlation characteristics of heterogeneous energy sources. Firstly, the correlation characteristics of different data of different stations in the area are analyzed, and then, by introducing a temporal attention mechanism, the mapping relationship between historical meteorological/power data and future power data is established based on the improved GRU-CNN network, which realizes the multi-station integrated ultra-short-term forecasting. The calculation example results show that the forecasting method proposed in this paper can realize the integrated high-precision ultra-short-term power forecasting of regional wind, solar, and hydro power stations, and the model effect is better than the single-field forecasting method and general integrated forecasting method, with higher modeling efficiency.

Key words: wind-solar-hydro integration forecasting, ultra-short-term power forecasting, integrated forecasting, attention mechanism

吴晓刚, 阎洁, 葛畅, 唐雅洁, 倪筹帷, 季青锋. 基于改进GRU-CNN的风光水一体化超短期功率预测方法[J]. 中国电力, 2023, 56(9): 178-186,205.

WU Xiaogang, YAN Jie, GE Chang, TANG Yajie, NI Chouwei, JI Qingfeng. Ultra-Short-Term Power Forecasting Method for Wind-Solar-Hydro Integration Based on Improved GRU-CNN[J]. Electric Power, 2023, 56(9): 178-186,205.

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https://www.electricpower.com.cn/CN/Y2023/V56/I9/178

参考文献

[1] 周原冰, 杨方, 余潇潇, 等. 中国能源电力碳中和实现路径及实施关键问题[J]. 中国电力, 2022, 55(5): 1–11
ZHOU Yuanbing, YANG Fang, YU Xiaoxiao, et al. Realization pathways and key problems of carbon neutrality in China’s energy and power system[J]. Electric Power, 2022, 55(5): 1–11
[2] 唐雅洁, 龚迪阳, 倪筹帷, 等. 基于邻域前向时序最优组合的分布式光伏超短期功率预测[J]. 浙江电力, 2021, 40(10): 95–101
TANG Yajie, GONG Diyang, NI Chouwei, et al. Ultra-short-term power prediction of distributed photovoltaic based on optimal combination of neighborhood forward time series[J]. Zhejiang Electric Power, 2021, 40(10): 95–101
[3] 刘沅昆, 张维静, 张艳, 等. 面向新型电力系统的新能源与储能联合规划方法[J]. 智慧电力, 2022, 50(10): 1–8
LIU Yuankun, ZHANG Weijing, ZHANG Yan, et al. Joint planning method of renewable energy and engergy storage for new-type power system[J]. Smart Power, 2022, 50(10): 1–8
[4] 邓韦斯, 孟子超, 王皓怀, 等. 新能源功率预测特性分析及精度提升措施[J]. 南方电网技术, 2023, 17(2): 11–23
DENG Weisi, MENG Zichao, WANG Haohuai, et al. Renewable energy power prediction characteristics analysis and accuracy improvement measures[J]. Southern Power System Technology, 2023, 17(2): 11–23
[5] 吴应双, 冯祥勇, 王寅, 等. 一种考虑新能源电站出力不确定性的采样鲁棒无功优化方法[J]. 电力科学与技术学报, 2023, 38(2): 84–95
WU Yingshuang, FENG Xiangyong, WANG Yin, et al. A sample robust reactive power optimization approach considering the power output uncertainty of renewable energy stations[J]. Journal of Electric Power Science and Technology, 2023, 38(2): 84–95
[6] 刘晨曦, 李刚, 程春田, 等. 大小水电相关分析的地区小水电发电能力预测方法[J]. 水电能源科学, 2016, 34(8): 63–66, 58
LIU Chenxi, LI Gang, CHENG Chuntian, et al. Correlation analysis method of large and small hydropower for generation capacity forecasting of small hydropower stations[J]. Water Resources and Power, 2016, 34(8): 63–66, 58
[7] 叶林, 屈晓旭, 马明顺, 等. 含风-光-水的多能源系统的同质化耦合模型[J]. 电网技术, 2020, 44(9): 3201–3210
YE Lin, QU Xiaoxu, MA Mingshun, et al. Multi-energy system homogeneous coupling model considering wind-photovoltaic-hydro power generations[J]. Power System Technology, 2020, 44(9): 3201–3210
[8] 崔杨, 陈正洪, 许沛华. 基于机器学习的集群式风光一体短期功率预测技术[J]. 中国电力, 2020, 53(3): 1–7
CUI Yang, CHEN Zhenghong, XU Peihua. Short-term power prediction for wind farm and solar plant clusters based on machine learning method[J]. Electric Power, 2020, 53(3): 1–7
[9] SEVERIANO C A, DE LIMA E SILVA P C, WEISS COHEN M, et al. Evolving fuzzy time series for spatio-temporal forecasting in renewable energy systems[J]. Renewable Energy, 2021, 171: 764–783.
[10] REIKARD G, ROBERTSON B, BIDLOT J R. Combining wave energy with wind and solar: short-term forecasting[J]. Renewable Energy, 2015, 81: 442–456.
[11] FARAJI J, KETABI A, HASHEMI-DEZAKI H, et al. Optimal day-ahead self-scheduling and operation of prosumer microgrids using hybrid machine learning-based weather and load forecasting[J]. IEEE Access, 2020, 8: 157284–157305.
[12] 张永蕊, 阎洁, 林爱美, 等. 多点数值天气预报风速和辐照度集中式修正方法研究[J]. 发电技术, 2022, 43(2): 278–286
ZHANG Yongrui, YAN Jie, LIN Aimei, et al. Integrated correction method of multi-point numerical weather prediction wind speed and irradiance[J]. Power Generation Technology, 2022, 43(2): 278–286
[13] YAN J, ZHANG H, LIU Y Q, et al. Forecasting the high penetration of wind power on multiple scales using multi-to-multi mapping[J]. IEEE Transactions on Power Systems, 2018, 33(3): 3276–3284.
[14] ALIPOUR M, AGHAEI J, NOROUZI M, et al. A novel electrical net-load forecasting model based on deep neural networks and wavelet transform integration[J]. Energy, 2020, 205: 118106.
[15] ZHANG H, YAN J, LIU Y Q, et al. Multi-source and temporal attention network for probabilistic wind power prediction[J]. IEEE Transactions on Sustainable Energy, 2021, 12(4): 2205–2218.
[16] 刘永前, 林爱美, 阎洁, 等. 基于深度学习的风光场群功率预测方法研究[J]. 分布式能源, 2021, 6(2): 14–21
LIU Yongqian, LIN Aimei, YAN Jie, et al. Research on power forecasting method for wind farms and photovoltaic stations based on deep learning[J]. Distributed Energy, 2021, 6(2): 14–21
[17] 王函. 风光发电功率与用电负荷联合预测方法研究[D]. 北京: 华北电力大学, 2021.
WANG Han. Study on joint forecasting method of wind and solar power generation power and power load[D]. Beijing: North China Electric Power University, 2021.
[18] 章剑光, 刘理峰, 林海峰, 等. 基于空间相似度和深度学习的中长期用电量预测[J]. 浙江电力, 2021, 40(5): 45–52
ZHANG Jianguang, LIU Lifeng, LIN Haifeng, et al. Medium and long-term electricity consumption prediction based on spatial similarity and deep learning[J]. Zhejiang Electric Power, 2021, 40(5): 45–52
[19] 郑豪丰, 杨国华, 康文军, 等. 基于多负荷特征和TCN-GRU神经网络的负荷预测[J]. 中国电力, 2022, 55(11): 142–148
ZHENG Haofeng, YANG Guohua, KANG Wenjun, et al. Load forecasting based on multi-load characteristics and TCN-GRU neural network[J]. Electric Power, 2022, 55(11): 142–148
[20] 贾睿, 杨国华, 郑豪丰, 等. 基于自适应权重的CNN-LSTM&GRU组合风电功率预测方法[J]. 中国电力, 2022, 55(5): 47–56, 110
JIA Rui, YANG Guohua, ZHENG Haofeng, et al. Combined wind power prediction method based on CNN-LSTM & GRU with adaptive weights[J]. Electric Power, 2022, 55(5): 47–56, 110
[21] 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
[22] 王渝红, 史云翔, 周旭, 等. 基于时间模式注意力机制的BiLSTM多风电机组超短期功率预测[J]. 高电压技术, 2022, 48(5): 1884–1892
WANG Yuhong, SHI Yunxiang, ZHOU Xu, et al. Ultra-short-term power prediction for BiLSTM multi wind turbines based on temporal pattern attention[J]. High Voltage Engineering, 2022, 48(5): 1884–1892
[23] 曾囿钧, 肖先勇, 徐方维, 等. 基于CNN-BiGRU-NN模型的短期负荷预测方法[J]. 中国电力, 2021, 54(9): 17–23
ZENG Youjun, XIAO Xianyong, XU Fangwei, et al. A short-term load forecasting method based on CNN-BiGRU-NN model[J]. Electric Power, 2021, 54(9): 17–23

基于改进GRU-CNN的风光水一体化超短期功率预测方法

Ultra-Short-Term Power Forecasting Method for Wind-Solar-Hydro Integration Based on Improved GRU-CNN

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[8]	樊江川, 于昊正, 刘慧婷, 杨丽君, 安佳坤. 基于多分支门控残差卷积神经网络的短期电力负荷预测[J]. 中国电力, 2022, 55(11): 155-162,174.
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基于改进GRU-CNN的风光水一体化超短期功率预测方法

Ultra-Short-Term Power Forecasting Method for Wind-Solar-Hydro Integration Based on Improved GRU-CNN

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