Ultra-short-term Load Forecasting Using Hybrid Neural Network Based on Parallel Multi-model Combination

doi:10.12204/j.issn.1000-7229.2020.10.001

Abstract

Abstract:

For the purpose of addressing the difficulty of improving load forecasting accuracy brought by enormous input data features, a method based on hybrid neural network using parallel multi-model combination is proposed. In order to respectively extract local features and time-series features, this paper places the convolutional neural network (CNN) in parallel with the gated recurrent unit (GRU) structure, then concatenates the output of two network structures and inputs to a deep neural network, uses deep neural network to perform load forecasting. Through a prediction experiment of load and temperature data by using the proposed method, the experiment results show that, compared with GRU-NN model, long short term memory (LSTM) model, serial CNN-LSTM network model and serial CNN-GRU network model, the proposed method shows better prediction performance.

Key words: ultra-short-term load forecasting, convolutional neural network (CNN), gated recurrent unit (GRU), deep neural network (DNN), feature extraction

CLC Number:

TM715

ZHUANG Jiayi, YANG Guohua, ZHENG Haofeng, WANG Yudong, HU Ruikun, DING Xu. Ultra-short-term Load Forecasting Using Hybrid Neural Network Based on Parallel Multi-model Combination[J]. ELECTRIC POWER CONSTRUCTION, 2020, 41(10): 1-8.

Figures/Tables 12

Fig.1 Comparison of load and temperature on January 1st

Fig.2 Comparison of load and temperature on July 5th

Fig.3 Basic structure of GRU

Fig.4 Structure of hybrid neural network using parallel multi-model combination

Fig.5 Sliding window structure of input and output

Table 1 Prediction metrics comparison of hybrid neural network under different GRU structure

Table 2 Prediction metrics comparison of hybrid neural network under different CNN structure

Table 3 Structure and parameters of hybrid neural network

Table 4 Comparison of MAPE and RMSE under different model prediction

Fig.6 Comparison of single model and multi-model predicted and practical curves

Table 5 Comparison of model prediction accuracy

Fig.7 Comparison of multi-model predicted and practical curves

References 23

[1]	HONG T, FAN S . Probabilistic electric load forecasting: A tutorial review[J]. International Journal of Forecasting, 2016,32(3):914-938. doi: 10.1016/j.ijforecast.2015.11.011 URL
[2]	苗键强, 童星, 康重庆 . 考虑相关因素统一修正的节假日负荷预测模型[J]. 电力建设, 2015,36(10):99-104. doi: 10.3969/j.issn.1000-7229.2015.10.015 URL
	MIAO Jianqiang, TONG Xing, KANG Chongqing . Holiday load forecasting model considering related factor with unified correction[J]. Electric Power Construction, 2015,36(10):99-104. doi: 10.3969/j.issn.1000-7229.2015.10.015 URL
[3]	KUSTER C, REZGUI Y, MOURSHED M . Electrical load forecasting models: A critical systematic review[J]. Sustainable Cities and Society, 2017,35:257-270. doi: 10.1016/j.scs.2017.08.009 URL
[4]	DUDEK G . Pattern-based local linear regression models for short-term load forecasting[J]. Electric Power Systems Research, 2016,130:139-147. doi: 10.1016/j.epsr.2015.09.001 URL
[5]	GOIA A, MAY C, FUSAI G . Functional clustering and linear regression for peak load forecasting[J]. International Journal of Forecasting, 2010,26(4):700-711. doi: 10.1016/j.ijforecast.2009.05.015 URL
[6]	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, 104.
	AI Xin, ZHOU Zhiyu, WEI Yanping , 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, 104.
[7]	麦鸿坤, 肖坚红, 吴熙辰 , 等. 基于R语言的负荷预测ARIMA模型并行化研究[J]. 电网技术, 2015,39(11):3216-3220. doi: 10.13335/j.1000-3673.pst.2015.11.030 URL
	MAI Hongkun, XIAO Jianhong, WU Xichen , et al. Research on ARIMA model parallelization in load prediction based on R language[J]. Power System Technology, 2015,39(11):3216-3220. doi: 10.13335/j.1000-3673.pst.2015.11.030 URL
[8]	ABBAS F, FENG D, HABIB S , et al. Short term residential load forecasting: An improved optimal nonlinear auto regressive (NARX) method with exponential weight decay function[J]. Electronics, 2018,7(12):432. doi: 10.3390/electronics7120432 URL
[9]	刘雨竹, 徐楠 . 基于混沌时间序列的IGA-WLSSVR短期负荷预测模型[J/OL]. 控制工程. ( 2019-12-20) [2020-02-07]. https://doi.org/10.14107/j.cnki.kzgc.20190495.
	LIU Yuzhu, XU Nan . Short-term load forecasting model using IGA-WLSSVR based on chaotic time series[J/OL]. Control Engineering of China. ( 2019-12-20) [2020-02-07]. https://doi.org/10.14107/j.cnki.kzgc.20190495 .
[10]	BALIYAN A, GAURAV K, MISHRA S K . A review of short term load forecasting using artificial neural network models[J]. Procedia Computer Science, 2015,48:121-125. doi: 10.1016/j.procs.2015.04.160 URL
[11]	BARBULESCU C, KILYENI S, DEACU A , et al. Artificial neural network based monthly load curves forecasting [C]// 2016 IEEE 11th International Symposium on Applied Computational Intelligence and Informatics (SACI). Timisoara, Romania: IEEE, 2016.
[12]	张刚, 刘福潮, 王维洲 , 等. 电网短期负荷预测的BP-ANN方法及应用[J]. 电力建设, 2014,35(3):54-58. doi: 10.3969/j.issn.1000－7229.2014.03.010 URL
	ZHANG Gang, LIU Fuchao, WANG Weizhou , et al. BP-ANN method for power grid short-term load forecasting and its application[J]. Electric Power Construction, 2014,35(3):54-58. doi: 10.3969/j.issn.1000－7229.2014.03.010 URL
[13]	BIANCHI F M, MAIORINO E, KAMPFFMEYER M C , et al. Recurrent neural networks for short-term load forecasting[M]. Cham: Springer International Publishing, 2017.
[14]	QING X Y, NIU Y G . Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM[J]. Energy, 2018,148:461-468. doi: 10.1016/j.energy.2018.01.177 URL
[15]	张宇帆, 艾芊, 林琳 , 等. 基于深度长短时记忆网络的区域级超短期负荷预测方法[J]. 电网技术, 2019,43(6):1884-1891.
	ZHANG Yufan, AI Qian, LIN Lin , et al. A very short-term load forecasting method based on deep LSTM RNN at zone level[J]. Power System Technology, 2019,43(6):1884-1891.
[16]	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.
	PENG Wen, WANG Jinrui, YIN Shanqing . Short-term load forecasting model based on attention-LSTM in electricity market[J]. Power System Technology, 2019,43(5):1745-1751.
[17]	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.
	WANG Zengping, ZHAO Bing, JI Weijia , et al. Short-term load forecasting method based on GRU-NN model[J]. Automation of Electric Power Systems, 2019,43(5):53-58.
[18]	HOSSEN T, PLATHOTTAM S J, ANGAMUTHU R K , et al. Short-term load forecasting using deep neural networks (DNN) [C]// 2017 North American Power Symposium (NAPS). IEEE, 2017.
[19]	MUTLU E C, OGHAZ T A , OGHAZ T A. Review on graph feature learning and feature extraction techniques for link prediction[J/OL]. ( 2019 -01-10)[2020-02-07].
[20]	XIE L, YUILLE A . Genetic CNN [C]// Proceedings of the IEEE International Conference on Computer Vision. IEEE, 2017: 1379-1388.
[21]	ABADI M, BARHAM P, CHEN J , et al. Tensorflow: A system for large-scale machine learning [C]//Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation. 2016: 265-283.
[22]	CHO K, VAN MERRIENBOER B, GULCEHRE C , et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[J/OL]. ( 2014-09-03) [2020-02-07]. https://arxiv.org/abs/1406.1078.
[23]	LIU B, FU C, BIELEFIELD A , et al. Forecasting of Chinese primary energy consumption in 2021 with GRU artificial neural network[J]. Energies, 2017,10(10):1453. doi: 10.3390/en10101453 URL