Medium and Long Term Power Load Forecasting Based on Stacked-GRU

  • Zheng Yang Electric Power Research Institute of Yunnan Power Grid, Kunming 650217, China https://orcid.org/0000-0003-0592-8660
  • Jing Cui Electric Power Research Institute of Yunnan Power Grid, Kunming 650217, China
  • Qiangjian Zhang Key Laboratory in Software Engineering of Yunnan Province, School of Software, Yunnan University, Kunming 650091, China
  • Chunlin Yin Electric Power Research Institute of Yunnan Power Grid, Kunming 650217, China
  • Li Yang Electric Power Research Institute of Yunnan Power Grid, Kunming 650217, China
  • Pengfeng Qiu Electric Power Research Institute of Yunnan Power Grid, Kunming 650217, China
  • Kai Hu Yunnan Power Grid Co., Ltd, Kunming 650011, China
  • Junwen Yang Electric Power Research Institute of Yunnan Power Grid, Kunming 650217, China
Keywords: Medium and long term power load forecasting, time series forecasting, stacked gated RNN

Abstract

Power load forecasting plays a critical role in energy economy development and distribution of power systems. Predicting medium and long term power loads have facilitated the development of power grids. In this paper, a stacked-gated recurrent unit (Stacked-GRU) is applied to establish a power load forecasting model by integrating economic factors. Meanwhile, it also conducts medium and long term power load (MLTPL) forecasting based on the power load data of Yunnan Province from 2009 to 2020. By comparing different optimizers, it is found that the Adam optimizer works the best on the Stacked-GRU architecture. In the experiment of medium and long term power load forecasting for Yunnan Province, the values of MAPE, RMSE, and MAE of the model are 9.76%, 1.412, and 1.14, respectively, all of which outperform other deep learning comparison algorithms.

Downloads

Download data is not yet available.

Author Biographies

Zheng Yang, Electric Power Research Institute of Yunnan Power Grid, Kunming 650217, China

Zheng Yang received his M. S. degree from Beijing Jiao Tong University in 2012. He is currently work for the Electric Power Research Institute of Yunnan Power Grid as an engineer. His main research interests are computer networking and security.

Jing Cui, Electric Power Research Institute of Yunnan Power Grid, Kunming 650217, China

Jing Cui received his M. S. degree from Kunming University of Science and Technology in 2021. Her main research fields are computer analysis and deep learning.

Qiangjian Zhang, Key Laboratory in Software Engineering of Yunnan Province, School of Software, Yunnan University, Kunming 650091, China

Qiangjian Zhang received his B.S. degree in industrial engineering from Kunming University in 2019. He is currently a master’s student of Yunnan University. His main research fields are Machine Learning and Computer Vision.

Chunlin Yin, Electric Power Research Institute of Yunnan Power Grid, Kunming 650217, China

Chunlin Yin received his M. S. degree from Yun Nan University in 2017. He is currently work for the Electric Power Research Institute of Yunnan Power Grid as an engineer. His main research interests are transfer learning.

Li Yang, Electric Power Research Institute of Yunnan Power Grid, Kunming 650217, China

Li Yang received her MA.Eng degree from Kunming University of Science and Technology in 2011. She is currently work for the Electric Power Research Institute of Yunnan Power Grid as an senior engineer. Her main research interests are digital transformation.

Pengfeng Qiu, Electric Power Research Institute of Yunnan Power Grid, Kunming 650217, China

Pengfeng Qiu received his M. S. degree from Chongqing University in 2017. He is currently work for the Electric Power Research Institute of Yunnan Power Grid as an engineer. His main research interests are High Voltage Engineering and Power Electronics.

Kai Hu, Yunnan Power Grid Co., Ltd, Kunming 650011, China

Kai Hu is currently work for Yunnan Power Grid Co., Ltd. as an engineer. His main research interests are Power System Analysis and Planning.

Junwen Yang, Electric Power Research Institute of Yunnan Power Grid, Kunming 650217, China

Junwen Yang, master, senior engineer, working in China Southern Power Grid. His research interest is power system analysis and planning.

References

Yunnan Provincial Bureau of Statistics. (2021, February 1). Report on Energy Production in Yunnan in 2020. Retrieved April 26, 2022, from http://stats.yn.gov.cn/tjsj/jjxx/202102/t20210201_1044039.html

Pingfei Wang. (2021). Research on Power load Prediction based on Time Seriesconvolution Lstm. Master’s Thesis, Sichuan University.

Shuxin Luo, Minhua Ma, Lin Jiang, Bingjie Jin, Yong Lin, Xuhao Diao, Canbing Li and Bo Yang. (2020). Medium and Long-term Load Forecasting Method Considering Multi-time Scale Data. Proceedings of the CSEE, (S1), 11–19.

Qing Zhong, Wen Sun, Nanhua Yu, Chunfang Liu, Fang Wang and Xin Zhang. (2014). Load and Power Forecasting in Active Distribution Network Planning. Proceedings of the CSEE, 34, (19), 3050–3056.

Song, K. B., Baek, Y. S., Hong, D. H., and Jang, G. (2005). Short-term load forecasting for the holidays using fuzzy linear regression method. IEEE transactions on power systems, 20(1), 96–101.

Bracale, A., Caramia, P., De Falco, P., and Hong, T. (2019). Multivariate quantile regression for short-term probabilistic load forecasting. IEEE Transactions on Power Systems, 35(1), 628–638.

Weipeng Li. (2019). Application analysis of power load forecasting based on partial least square method. Practical Electronicstor, (12), 74–75+86.

Göb, R., Lurz, K., and Pievatolo, A. (2013). Electrical load forecasting by exponential smoothing with covariates. Applied Stochastic Models in Business and Industry, 29(6), 629–645.

Abderrezak, L., Mourad, M., and Djalel, D. (2014, December). Very short-term electricity demand forecasting using adaptive exponential smoothing methods. In 2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA) (pp. 553–557). IEEE.

Liao, G. C. (2021). Fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network Application in Load Forecasting. Energies, 15(1), 130.

Chuanjun Pang, Bo Zhang and Jianming Yu. (2021). Short-term power load forecasting based on LSTM recurrent neural network. Electric Power Engineering Technology, (01), 175–180+194.

Xiaoyu Wu, Jinghan He, Pei Zhang and Jun Hu. (2015). Power System Short-term Load Forecasting Based on Improved Random Forest with Grey Relation Projection. Automation of Electric Power Systems, 39(12), 50–55.

Yang Zhao, Hanmo Wang, Li Kang, Zhaoyun Zhang. (2022). Temporal Convolution Network-Based Short-Term Electrical Load Forecasting. Transactions of China Electrotechnical Society, 37(05), 1242–1251.

Li, C., Chen, Z., Liu, J., Li, D., Gao, X., Di, F., …and Ji, X. (2019, August). Power load forecasting based on the combined model of LSTM and XGBoost. In Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence (pp. 46–51).

Wang, Z., Wang, X., Ma, C., and Song, Z. (2021). A Power Load Forecasting Model Based on FA-CSSA-ELM. Mathematical Problems in Engineering, 2021.

Alhussein, M., Aurangzeb, K., and Haider, S. I. (2020). Hybrid CNN-LSTM model for short-term individual household load forecasting. IEEE Access, 8, 180544–180557.

Wu, L., Kong, C., Hao, X., and Chen, W. (2020). A short-term load forecasting method based on GRU-CNN hybrid neural network model. Mathematical Problems in Engineering, 2020.

Chongqing Kang, Qing Xia and Boming Zhang. (2004). Review and Development of Load Forecasting in Power System. Automation of Electric Power Systems, (17), 1–11.

Han, Y., Sha, X., Grover-Silva, E., and Michiardi, P. (2014, October). On the impact of socio-economic factors on power load forecasting. In 2014 IEEE International Conference on Big Data (Big Data) (pp. 742–747). IEEE.

Moral-Carcedo, J., and Pérez-García, J. (2017). Integrating long-term economic scenarios into peak load forecasting: An application to Spain. Energy, 140, 682–695.

Liu, D., Sun, K., Huang, H., and Tang, P. (2018). Monthly load forecasting based on economic data by decomposition integration theory. Sustainability, 10(9), 3282.

Ghanbari, A., Naghavi, A., Ghaderi, S. F., and Sabaghian, M. (2009, March). Artificial Neural Networks and regression approaches comparison for forecasting Iran’s annual electricity load. In 2009 International conference on power engineering, energy and electrical drives (pp. 675–679). IEEE.

Mikolov, T., Karafiát, M., Burget, L., Cernocký, J., and Khudanpur, S. (2010, September). Recurrent neural network based language model. In Interspeech (Vol. 2, No. 3, pp. 1045–1048).

Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2015, June). Gated feedback recurrent neural networks. In International conference on machine learning (pp. 2067–2075). PMLR.

Zhewen Niu, Zeyuan Yu, Bo Li, Wenhu Tang. (2018). Short-term wind power forecasting model based on deep gated recurrent unit neural network. Electric Power Automation Equipment, (05), 36–42.

Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.

Yunnan Provincial Bureau of Statistics. (2021, November 25). Statistical Yearbook . Retrieved April 26, 2022, from http://stats.yn.gov.cn/tjsj/tjnj/

Yunnan Provincial Bureau of Statistics. (2021, November 25). Yunnan Monthly Statistics. Retrieved April 26, 2022, from http://stats.yn.gov.cn/tjsj/tjnj/

Willmott, C. J., and Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 30(1), 79–82.

Goodwin, P., and Lawton, R. (1999). On the asymmetry of the symmetric MAPE. International journal of forecasting, 15(4), 405–408.

Chai, T., and Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific model development, 7(3), 1247–1250.

Published
2022-09-30
Section
Articles