| Citation: | LIU Qianqian, LIU Yushan, WEN Yeting, et al. Short-term load forecasting method based on PCC-LSTM model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(12): 2529-2536. doi: 10.13700/j.bh.1001-5965.2021.0145(in Chinese)
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Short-term load forecasting is the basis for reasonable dispatch and smooth operation of the power grid. To improve the accuracy of short-term load forecasting, a method based on Pearson correlation coefficient (PCC) and long and short term memory (LSTM) neural network is proposed. This method uses Pearson correlation analysis to analyze the correlation of the time series composed of original multi-dimensional input variables, and selects the influencing factors with greater correlation with the power load data as the input to achieve the dimensionality reduction of the original data. Then, through the combination of LSTM neural network and Adam optimization algorithm, a network model is established for the nonlinear relationship between the influencing factors that have a greater correlation with the power load and the actual output sequence of the load. Taking the load data of Jiajie BOX and Chongqing International Grand Metropark Liyuan Hotel as calculation examples, the proposed model is compared with Prophet, LSTNet, and gated recurrent unit (GRU) models. Results show that the prediction accuracy of the proposed model is above 91%, with the highest up to 95.44%, thus effectively improving the accuracy of load forecasting.
| [1] |
康重庆, 夏清, 刘梅. 电力系统负荷预测[M]. 北京: 中国电力出版社, 2007.
KANG C Q, XIA Q, LIU M. Power system load forecasting[M]. Beijing: China Electric Power Press, 2007(in Chinese).
|
| [2] |
杨英, 胡函武, 魏晗, 等. 电力系统负荷预测分类研究[J]. 电子世界, 2018(21): 109. https://www.cnki.com.cn/Article/CJFDTOTAL-ELEW201821072.htm
YANG Y, HU H W, WEI H, et al. Research on classification of power system load forecast[J]. Electronics World, 2018(21): 109(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ELEW201821072.htm
|
| [3] |
康重庆, 夏清, 张伯明. 电力系统负荷预测研究综述与发展方向的探讨[J]. 电力系统自动化, 2004, 28(17): 1-11. doi: 10.3321/j.issn:1000-1026.2004.17.001
KANG C Q, XIA Q, ZHANG B M. Review of power system load forecasting and its development[J]. Automation of Electric Power Systems, 2004, 28(17): 1-11(in Chinese). doi: 10.3321/j.issn:1000-1026.2004.17.001
|
| [4] |
程宇也. 基于人工神经网络的短期电力负荷预测研究[D]. 杭州: 浙江大学, 2017: 31-40.
CHENG Y Y. Short-term electricity demand forecasting based on artificial neural network[D]. Hangzhou: Zhejiang University, 2017: 31-40(in Chinese).
|
| [5] |
YILDIZ B, BILBAO J I, SPROUL A B. A review and analysis of regression and machine learning models on commercial building electricity load forecasting[J]. Renewable and Sustainable Energy Reviews, 2017, 73: 1104-1122. doi: 10.1016/j.rser.2017.02.023
|
| [6] |
BOX G G, BOX G, JENKINS G. Time series analysis: Forecasting and control[J]. Journal of Time, 1976, 31(4): 238-242.
|
| [7] |
贺静, 韦钢, 熊玲玲. 负荷预测线性回归分析法的模糊改进[J]. 华东电力, 2003, 31(11): 21-23. https://www.cnki.com.cn/Article/CJFDTOTAL-HDDL200311007.htm
HE J, WEI G, XIONG L L. Fuzzy improvement of linear regression analysis for load forecasting[J]. East China Electric Power, 2003, 31(11): 21-23(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HDDL200311007.htm
|
| [8] |
袁诚斌, 张涛, 朱建良. 基于MRA与回归分析法的短期电力负荷预测[J]. 信息技术, 2007, 31(10): 88-90. https://www.cnki.com.cn/Article/CJFDTOTAL-HDZJ200710030.htm
YUAN C B, ZHANG T, ZHU J L. Short-term power load forecasting based on MRA and regression analysis[J]. Information Technology, 2007, 31(10): 88-90(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HDZJ200710030.htm
|
| [9] |
李强, 王中宇, 王卓然. 压力传感器动态校准不确定度评定[J]. 北京航空航天大学学报, 2015, 41(5): 847-856. doi: 10.13700/j.bh.1001-5965.2014.0356
LI Q, WANG Z Y, WANG Z R. Uncertainty evaluation for the dynamic calibration of pressure transducer[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(5): 847-856(in Chinese). doi: 10.13700/j.bh.1001-5965.2014.0356
|
| [10] |
郭松林, 水泉龙, 顾翔瑜. 灰色系统理论在负荷预测中运用综述[J]. 工业仪表与自动化装置, 2017(3): 24-27. https://www.cnki.com.cn/Article/CJFDTOTAL-GYZD201703006.htm
GUO S L, SHUI Q L, GU X Y. Summary of application of gray system theory in load forecasting[J]. Industrial Instrumentation & Automation, 2017(3): 24-27(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GYZD201703006.htm
|
| [11] |
张国江, 邱家驹, 李继红. 基于模糊推理系统的多因素电力负荷预测[J]. 电力系统自动化, 2002, 26(5): 49-53. https://www.cnki.com.cn/Article/CJFDTOTAL-DLXT200205011.htm
ZHANG G J, QIU J J, LI J H. Multi-factor short-term load forecasting based on fuzzy inference system[J]. Automation of Electric Power Systems, 2002, 26(5): 49-53(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DLXT200205011.htm
|
| [12] |
艾欣, 周志宇, 魏妍萍, 等. 基于自回归积分滑动平均模型的可转移负荷竞价策略[J]. 电力系统自动化, 2017, 41(20): 26-31. https://www.cnki.com.cn/Article/CJFDTOTAL-DLXT201720004.htm
AI X, ZHOU Z Y, WEI Y P, et al. Bidding strategy for time-shiftable loads based on autoregressive integrated moving average model[J]. Automation of Electric Power Systems, 2017, 41(20): 26-31(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DLXT201720004.htm
|
| [13] |
刘龙龙. 基于SARIMA与SVR的短期电力负荷预测[D]. 抚州: 东华理工大学, 2018: 7-14.
LIU L L. Short-term power load forecasting based on SARIMA and SVR[D]. Fuzhou: East China Institute of Technology, 2018: 7-14(in Chinese).
|
| [14] |
吴潇雨, 和敬涵, 张沛, 等. 基于灰色投影改进随机森林算法的电力系统短期负荷预测[J]. 电力系统自动化, 2015, 39(12): 50-55. https://www.cnki.com.cn/Article/CJFDTOTAL-DLXT201512009.htm
WU X Y, HE J H, ZHANG P, et al. Power system short-term load forecasting based on improved random forest with grey relation projection[J]. Automation of Electric Power Systems, 2015, 39(12): 50-55(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DLXT201512009.htm
|
| [15] |
李元诚, 方廷健, 于尔铿. 短期负荷预测的支持向量机方法研究[J]. 中国电机工程学报, 2003, 23(6): 55-59. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC200306010.htm
LI Y C, FANG T J, YU E K. Study of support vector machines for short-term load forecasting[J]. Proceedings of the Chinese Society for Electrical Engineering, 2003, 23(6): 55-59(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC200306010.htm
|
| [16] |
张涛, 赵登福, 周琳, 等. 基于RBF神经网络和专家系统的短期负荷预测方法[J]. 西安交通大学学报, 2001, 35(4): 331-334. https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT200104000.htm
ZHANG T, ZHAO D F, ZHOU L, et al. Short-term load forecasting using radial basis function networks and expert system[J]. Journal of Xi'an Jiaotong University, 2001, 35(4): 331-334(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT200104000.htm
|
| [17] |
李龙, 魏靖, 黎灿兵, 等. 基于人工神经网络的负荷模型预测[J]. 电工技术学报, 2015, 30(8): 225-230. https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS201508028.htm
LI L, WEI J, LI C B, et al. Prediction of load model based on artificial neural network[J]. Transactions of China Electrotechnical Society, 2015, 30(8): 225-230(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS201508028.htm
|
| [18] |
张倩, 马愿, 李国丽, 等. 频域分解和深度学习算法在短期负荷及光伏功率预测中的应用[J]. 中国电机工程学报, 2019, 39(8): 2221-2230. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201908005.htm
ZHANG Q, MA Y, LI G L, et al. Applications of frequency domain decomposition and deep learning algorithms in short-term load and photovoltaic power forecasting[J]. Proceedings of the Chinese Society for Electrical Engineering, 2019, 39(8): 2221-2230(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201908005.htm
|
| [19] |
彭文, 王金睿, 尹山青. 电力市场中基于Attention-LSTM的短期负荷预测模型[J]. 电网技术, 2019, 43(5): 1745-1751. https://www.cnki.com.cn/Article/CJFDTOTAL-DWJS201905032.htm
PENG W, WANG J R, YIN S Q. Short-term load forecasting model based on attention-LSTM in electricity market[J]. Power System Technology, 2019, 43(5): 1745-1751(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DWJS201905032.htm
|
| [20] |
杨甲甲, 刘国龙, 赵俊华, 等. 采用长短期记忆深度学习模型的工业负荷短期预测方法[J]. 电力建设, 2018, 39(10): 20-27. https://www.cnki.com.cn/Article/CJFDTOTAL-DLJS201810006.htm
YANG J J, LIU G L, ZHAO J H, et al. A long short term memory based deep learning method for industrial load forecasting[J]. Electric Power Construction, 2018, 39(10): 20-27(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DLJS201810006.htm
|
| [21] |
陈锦鹏, 胡志坚, 陈纬楠, 等. 二次模态分解组合DBiLSTM-MLR的综合能源系统负荷预测[J]. 电力系统自动化, 2021, 45(13): 85-94. https://www.cnki.com.cn/Article/CJFDTOTAL-DLXT202113010.htm
CHEN J P, HU Z J, CHEN W N, et al. Load prediction of integrated energy system based on combination of quadratic modal decomposition and deep bidirectional long short-term memory and multiple linear regression[J]. Automation of Electric Power Systems, 2021, 45(13): 85-94(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DLXT202113010.htm
|
| [22] |
赵倩, 郑贵林. 基于WD-LSSVM-LSTM模型的短期电力负荷预测[J/OL]. 电测与仪表, 2021(2021-02-26)[2021-03-21]. http://kns.cnki.net/kcms/detail/23.1202.TH.20210226.1041.004.html.
ZHAO Q, ZHENG G L. Short-term load forecasting based on WD-LSSVM-LSTM model[J/OL]. Electrical Measurement and Instrumentation, 2021(2021-02-26)[2021-03-21]. http://kns.cnki.net/kcms/detail/23.1202.TH.20210226.1041.004.html(in Chinese).
|
| [23] |
陆继翔, 张琪培, 杨志宏, 等. 基于CNN-LSTM混合神经网络模型的短期负荷预测方法[J]. 电力系统自动化, 2019, 43(8): 131-137. https://www.cnki.com.cn/Article/CJFDTOTAL-DLXT201908018.htm
LU J X, ZHANG Q P, YANG Z H, et al. Short-term load forecasting method based on CNN-LSTM hybrid neural network model[J]. Automation of Electric Power Systems, 2019, 43(8): 131-137(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DLXT201908018.htm
|
| [24] |
王增平, 赵兵, 纪维佳, 等. 基于GRU-NN模型的短期负荷预测方法[J]. 电力系统自动化, 2019, 43(5): 53-58. https://www.cnki.com.cn/Article/CJFDTOTAL-DLXT201905008.htm
WANG Z P, ZHAO B, JI W J, et al. Short-term load forecasting method based on GRU-NN model[J]. Automation of Electric Power Systems, 2019, 43(5): 53-58(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DLXT201905008.htm
|
| [25] |
FENG M C, ZHENG J B, REN J C, et al. Big data analytics and mining for effective visualization and trends forecasting of crime data[J]. IEEE Access, 2019, 7: 106111-106123.
|
| [26] |
HUANG B, LIANG Y Y, QIU X L. Wind power forecasting using attention-based recurrent neural networks: A comparative study[J]. IEEE Access, 2021, 9: 40432-40444.
|
| [27] |
AFRASIABI M, MOHAMMADI M, RASTEGAR M, et al. Adv-anced deep learning approach for probabilistic wind speed forecasting[J]. IEEE Transactions on Industrial Informatics, 2021, 17(1): 720-727.
|
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