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

Authors

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

DOI:

https://doi.org/10.13052/spee1048-5236.4141

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.

Medium and Long Term Power Load Forecasting Based on Stacked-GRU 373

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),

–75+86.

G ̈ob, 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 Sys-

tem Short-term Load Forecasting Based on Improved Random Forest

with Grey Relation Projection. Automation of Electric Power Systems,

(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.

Z. Yang et al.

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 P ́erez-Garc ́ıa, 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 fore-

casting 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 com-

parison for forecasting Iran’s annual electricity load. In 2009 Interna-

tional conference on power engineering, energy and electrical drives

(pp. 675–679). IEEE.

Mikolov, T., Karafi ́at, M., Burget, L., Cernock ́y, 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 Merri ̈enboer, 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.c

n/tjsj/tjnj/

Medium and Long Term Power Load Forecasting Based on Stacked-GRU 375

Willmott, C. J., and Matsuura, K. (2005). Advantages of the mean abso-

lute 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 symmet-

ric 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.

Downloads

Published

2022-09-30

How to Cite

Yang , Z. ., Cui , J. ., Zhang , Q. ., Yin , C. ., Yang , L. ., Qiu, P. ., Hu, . K. ., & Yang, J. . (2022). Medium and Long Term Power Load Forecasting Based on Stacked-GRU . Strategic Planning for Energy and the Environment, 41(4), 363–378. https://doi.org/10.13052/spee1048-5236.4141

Issue

Section

Articles

Most read articles by the same author(s)