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Recent Advances in Electrical & Electronic Engineering

Author(s): Hao Hu*, Wen Jie Li, Yan Shi, Chao Zhou and DeHua Guo

DOI: 10.2174/2352096516666230418104618

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TCN Multi-time-scale Transformation and Temporal-Attention Neural Network for Monthly Electricity Consumption Forecasting

Page: [872 - 883] Pages: 12

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Abstract

Background: For the efficient and secure running of the power industry, accurate monthly electricity projections are crucial. Due to coupling variations and a variety of data resolutions, current approaches are still unable to accurately extract multidimensional time-series data.

Objective: For monthly electricity consumption forecasting, a multi-time-scale transformation and temporal attention neural network for a temporal convolutional network is proposed.

Methods: First, a multi-time-scale compression model of temporal convolutional network is proposed, which compresses data on several time scales from different resolutions, such as the economy, weather, and historical load. Second, a multi-source temporal attention module is built to further dynamically extract crucial information. Finally, the decoding-encoding and residual connections' structure contributes to the prediction's improved resilience.

Results: The proposed method was compared with the state-of-the-art monthly load forecasting based on two years of historical data in a certain region, demonstrating its effectiveness.

Conclusion: Through the verification of local historical data, the proposed model was contrasted with cutting-edge monthly load forecasting techniques. The obtained results demonstrate the effectiveness.

Keywords: Monthly electricity consumption forecasting, multi-time-scale transformation, temporal attention, decodingencoding, TCN, multidimensional time-series data.

Graphical Abstract

[1]
X. Lu, K. Li, H. Xu, F. Wang, Z. Zhou, and Y. Zhang, "Fundamentals and business model for resource aggregator of demand response in electricity markets", Energy, vol. 204, p. 117885, 2020.
[http://dx.doi.org/10.1016/j.energy.2020.117885]
[2]
Y. Bai, H. Zhong, L. Xie, Q. Xia, and Q. Chen, "Institutional design of chinese retail electricity market reform and related suggestions", Dianli Xitong Zidonghua, vol. 39, no. 14, pp. 1-7, 2015.
[http://dx.doi.org/10.7500/AEPS20150514003]
[3]
K-B. Song, Y-S. Baek, D.H. Hong, and G. Jang, "Short-term load forecasting for the holidays using fuzzy linear regression method", IEEE Trans. Power Syst., vol. 20, no. 1, pp. 96-101, 2005.
[http://dx.doi.org/10.1109/TPWRS.2004.835632]
[4]
C. Yuan, S. Liu, and Z. Fang, "Comparison of China’s primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model", Energy, vol. 100, pp. 384-390, 2016.
[http://dx.doi.org/10.1016/j.energy.2016.02.001]
[5]
B. Xue, C. Cheng, O.U. Shiqi, A. Liu, and S. Wang, "A linear regression model for forecasting monthly electricity sales considering comfortable temperature range and sudden variable", In: Power system protection, vol. 45. 2017, no. 1, pp. 15-20.
[http://dx.doi.org/10.7667/PSPC160072]
[6]
L. An, Y. Teng, C. Li, and Q. Dan, "Combined grey model based on entropy weight method for long-term load forecasting", In 2020 5th Asia Conference on Power and Electrical Engineering (ACPEE), Chengdu, China, 2020, pp. 149-153
[http://dx.doi.org/10.1109/ACPEE48638.2020.9136269]
[7]
X. Jiuyi, L. Ziming, Y. Jianfeng, Y. Xiaolei, T. Yiyan, and L. Jie, "Research on electricity prediction of mixed model considering economic factors", Sichuan Elect. Power Technol., vol. 44, no. 1, p. 6, 2021.
[8]
M. El-Saadawi, and A. Hatata, "A novel protection scheme for synchronous generator stator windings based on SVM", Protect. Contr. Modern Power Syst., vol. 2, no. 1, p. 24, 2017.
[http://dx.doi.org/10.1186/s41601-017-0057-x]
[9]
F. Kaytez, "A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption", Energy, vol. 197, p. 117200, 2020.
[http://dx.doi.org/10.1016/j.energy.2020.117200]
[10]
C-G. Lim, and H-J. Choi, "Deep learning-based analysis on monthly household consumption for different electricity contracts", In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), Busan, Korea (South), 2020, pp. 545-547
[http://dx.doi.org/10.1109/BigComp48618.2020.000-7]
[11]
X. Wang, F. Fang, X. Zhang, Y. Liu, L. Wei, and Y. Shi, "LSTM-based Short-term load forecasting for building electricity consumption", In IEEE 28th International Symposium on Industrial Electronics (ISIE), Vancouver, BC, Canada, 2019, pp. 1418-1423
[http://dx.doi.org/10.1109/ISIE.2019.8781349]
[12]
F. Wang, Y. Yu, Z. Zhang, J. Li, Z. Zhen, and K. Li, "Wavelet decomposition and convolutional LSTM networks based improved deep learning model for solar irradiance forecasting", Appl. Sci., vol. 8, no. 8, p. 1286, 2018.
[http://dx.doi.org/10.3390/app8081286]
[13]
L. Wenwu, Z. Pengyu, S. Qiang, F. Chenyang, and L. Dan, "Correction prediction of integrated energy system load based on aggregated mixed mode decomposition and TCN", In Power System Technology, vol. 9, Jun. 2022, no. 46, p. 3345
[http://dx.doi.org/10.13335/j.1000-3673.pst.2022.0631]
[14]
B. Liu, "Short-term Load Forecasting of Distributed Energy Supply System Based on Elman Neural Network", In 2018 china international conference on electricity distribution (CICED), Tianjin, China, 2018, pp. 2175-2178
[http://dx.doi.org/10.1109/CICED.2018.8592018]
[15]
W. Fei, L. Zhenghui, L. Yu, W. Tieqiang, Q. Gang, and G. Huaidong, "Data series resolution compression scale optimization based monthly electricity consumption forecasting", Power Syst. Protect. Contr., vol. 48, no. 11, p. 7, 2020.
[http://dx.doi.org/10.19783/j.cnki.pspc.200539]
[16]
Z. Li, K. Li, F. Wang, Z. Mi, W. Li, and P. Dehghanian, "Auto-encoder neural network-based monthly electricity consumption forecasting method using hourly data", In IEEE/IAS 56th Industrial and Commercial Power Systems Technical Conference (I&CPS), Las Vegas, NV, USA, 2020, pp. 1-8
[http://dx.doi.org/10.1109/ICPS48389.2020.9176789]
[17]
Y. Wang, Z. Li, F. Wang, Z. Zhen, P. Dehghanian, J.P.S. Catalão, K. Li, and M. Fotuhi-Firuzabad, "Greedy clustering-based monthly electricity consumption forecasting model", In IEEE industry applications society annual meeting (IAS), Vancouver, BC, Canada, 2021, pp. 1-8
[http://dx.doi.org/10.1109/IAS48185.2021.9677327]
[18]
H. Mengying, D. Jiandong, H. Zequan, W. Peng, F. Shuai, H. Peijia, and F. Chaoyuan, "Monthly electricity forecast based on electricity consumption characteristics analysis and multiple effect factors", In 2019 IEEE 8th International conference on advanced power system automation and protection (APAP), Xi'an, China, 2019, pp. 1814-1818
[http://dx.doi.org/10.1109/APAP47170.2019.9224784]
[19]
H. -F. Wang, and C-L. Lai, "Monthly electricity demand forecasting by GANN", In 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bali, Indonesia, 2016, pp. 1908-1912
[http://dx.doi.org/10.1109/IEEM.2016.7798210]
[20]
L. Jun, Z. Hongyan, L. Jiacheng, P. Liangjun, and W. Kai, "“Medium-term load forecasting based on cointegration-Granger causality test and seasonal decomposition”", Dianli Xitong Zidonghua./Autom. Electr. Power Syst., vol. 43, no. 1, p. 8, 2019.
[http://dx.doi.org/10.7500/AEPS20180629013]
[21]
Z. Jian, L. Kai, L. Zhanzhan, and L. Ruimin, "Study of monthly electricity sales forecasting based on seasonal adjustment and multi-factor correction method", In chinese control and decision conference (CCDC), shenyang, China, 2018, pp. 2086-2090
[http://dx.doi.org/10.1109/CCDC.2018.8407470]
[22]
H. Shi, L. Wang, R. Scherer, M. Wozniak, P. Zhang, and W. Wei, "Short-term load forecasting based on adabelief optimized temporal convolutional network and gated recurrent unit hybrid neural network", IEEE Access, vol. 9, pp. 66965-66981, 2021.
[http://dx.doi.org/10.1109/ACCESS.2021.3076313]
[23]
J. Shu, X. Zhang, Y. Yao, D. Yi, and B. Gu, "Graph spatio-temporal attention network-based electricity demand forecasting", In 2021 6th international conference on power and renewable energy (ICPRE), shanghai, China, 2021, pp. 792-797
[http://dx.doi.org/10.1109/ICPRE52634.2021.9635240]
[24]
J. Shi, N. Liu, Y. Huang, and L. Ma, "An edge computing-oriented net power forecasting for pv-assisted charging station: Model Complexity and Forecasting Accuracy Trade-off", Appl. Energy, vol. 310, p. 118456, 2022.
[http://dx.doi.org/10.1016/j.apenergy.2021.118456]