SARIMA-Based Medium- and Long-Term Load Forecasting

Authors

  • Chunlin Yin Electric Power Research Institute of Yunnan Power Grid, Kunming 650217, China
  • Kaihua Liu Key Laboratory in Software Engineering of Yunnan Province, School of Software, Yunnan University, Kunming 650091, China
  • Qiangjian Zhang Key Laboratory in Software Engineering of Yunnan Province, School of Software, Yunnan University, Kunming 650091, China
  • Kai Hu Yunnan Power Grid Co., Ltd, Kunming 650011, China
  • Zheng Yang Electric Power Research Institute of Yunnan Power Grid, Kunming 650217, China
  • Li Yang Electric Power Research Institute of Yunnan Power Grid, Kunming 650217, China
  • Na Zhao 1) Electric Power Research Institute of Yunnan Power Grid, Kunming 650217, China 2 ) Key Laboratory in Software Engineering of Yunnan Province, School of Software, Yunnan University, Kunming 650091, China 3) The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province, Kunming 650201, China

DOI:

https://doi.org/10.13052/spee1048-5236.4222

Keywords:

SARIMA, medium- and long-term load forecasting, time series models

Abstract

The operation and planning of power systems depend heavily on M-LTLF, which is complicated and nonlinear, making it challenging for conventional medium- and long-term forecasting models to produce reliable results. The SARIMA model is chosen for M-LTLF in this study, and the model’s parameters are tuned. This study takes the electricity consumption data of the whole Yunnan as the research object. Among them, the electricity consumption data from 2008 to 2018 is used as a training sample for fitting and analysis, and the electricity consumption of the whole province is predicted from 2019 to 2020. The end results demonstrate the viability and efficacy of the SARIMA model for M-LTLF.

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Author Biographies

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.

Kaihua Liu, Key Laboratory in Software Engineering of Yunnan Province, School of Software, Yunnan University, Kunming 650091, China

Kaihua Liu is currently a bachelor’s student at Yunnan University. His main research fields are Machine Learning and Computer Vision.

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.

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.

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.

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.

Na Zhao, 1) Electric Power Research Institute of Yunnan Power Grid, Kunming 650217, China 2 ) Key Laboratory in Software Engineering of Yunnan Province, School of Software, Yunnan University, Kunming 650091, China 3) The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province, Kunming 650201, China

Na Zhao received the Ph.D. degree from the Yunnan University in 2011. She is an Associate Professor with the School of Software, Yunnan University. Her research interests include software engineering, complex network, the Internet of things, smart grid, etc.

References

Zheng Lei, Shuxin Tian, Dawei Yan, Lianbin Wei, Kui Wang and Lu Liu. (2020). Mid-Long Term Load Forecasting Based on ARIMA-TARCH-BP Neural Network Model. Chinese Journal of Electron Devices, 43(01), 175–179.

Pinjing Zou, Jiangang Yao, Weihui Kong, Linbo Hu and Xueqing Pan. (2017). Mid-long Term Power Load Forecasting Based on Multivariable Time Series Inversion Self-memory Model. Proceedings of the CSU-EPSA, 29(10), 98–105.

Xincheng Sun, Jianshou Kong and Zhao Liu. (2018). Middle-term power load forecasting model based on kernel principal component analysis and improved neural network. Journal of Nanjing University of Science and Technology, 2018, 42(03), 259–265.

Zheng Zheng, Lei Tan, Nan Zhou, Junwei Han, Jing Gao and Liguo Weng. Power load prediction based on multi-headed attentional convolutional network. Journal of Nanjing University of Information Science and Technology(Natural Science Edition).

Yongzhi Wang, Bo Liu and Yu Li. (2020). A Power Load Data Prediction Method Based on LSTM Neural Network Model. Research and Exploration in Laboratory, 39(05), 41–45.

Zhaoying Tu, Yi Mao, Xiaoxiao Song and Junliang Yuan. (2019). Research on Medium and Long Term Power Load Forecasting in Boluo County. Guangdong Province, 42(01), 76–81.

Hui Hou, Qing Wang, Bo Zhao, Leiqi Zhang, Xixiu Wu and Changjun Xie. (2022). Power load forecasting without key information based on phase space reconstruction and machine learning. Power System Protection and Control, 50(04), 75–82.

Zuleta-Elles, I., Bautista-Lopez, A., Cataño-Valderrama, M. J., Marín, L. G., Jiménez-Estévez, G., and Mendoza-Araya, P. (2021, December). Load Forecasting for Different Prediction Horizons using ANN and ARIMA models. In 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON) (pp. 1–7). IEEE.

Yang, Z. C. (2016). Discrete cosine transform-based predictive model extended in the least-squares sense for hourly load forecasting. IET Generation, Transmission and Distribution, 10(15), 3930–3939.

Saxena, H., Aponte, O., and McConky, K. T. (2019). A hybrid machine learning model for forecasting a billing period’s peak electric load days. International Journal of Forecasting, 35(4), 1288–1303.

Maldonado, S., Gonzalez, A., and Crone, S. (2019). Automatic time series analysis for electric load forecasting via support vector regression. Applied Soft Computing, 83, 105616.

Liu, Z., Sun, X., Wang, S., Pan, M., Zhang, Y., and Ji, Z. (2019). Midterm power load forecasting model based on kernel principal component analysis and back propagation neural network with particle swarm optimization. Big data, 7(2), 130–138.

Gao, W., Darvishan, A., Toghani, M., Mohammadi, M., Abedinia, O., and Ghadimi, N. (2019). Different states of multi-block based forecast engine for price and load prediction. International Journal of Electrical Power and Energy Systems, 104, 423–435.

Yaoyao He, Yang Qin and Shanlin Yang. (2019). Medium-term power load probability density forecasting method based on LASSO quantile regression. Systems Engineering-Theory and Practice, 39(07), 1845–1854.

Jiangyong Liu, Wenhan Liu and Lingzhi Yi. (2020). Multi-sequence Coordinated Medium-term Load Forecasting Model. Proceedings of the CSU-EPSA, 32(02), 48–53.

Jun Liu, Hongyan Zhao, Jiacheng Liu, Liangjun Pan and Kai Wang. (2019). Medium-term Load Forecasting Based on Cointegration-Granger Causality Test and Seasonal Decomposition. Automation of Electric Power Systems, 43(01), 73–80.

Shan Jiang, Qiupeng Zhou, Hongchuan Dong, Xu Ma and Zhenyu Zhao. Long-term load combination forecasting method considering the periodicity and trend of data. Electrical Measurement and Instrumentation.

Box, G. E., and Pierce, D. A. (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American statistical Association, 65(332), 1509–1526.

Jingli Guo and Bo Dong. (2019). International rice price forecast based on SARIMA model. Price:Theory and Practice, (01), 79–82.

Yanqun Sun, Shougang Zhang, Moyuan Lu, Yan Zhang, Yanyu Pan, Chong Wang, Qixin Wu, Meixue Yao and Chengguo Li. (2022). Prediction of mosquito infestation in Nanjing based on SARIMA model. Journal of Nanjing Medical University(Natural Sciences), 42(01), 108–111.

Xingqiang Pan, Rui Ma, Tianchi Yang, Yi Chen, Keqin Ding and Guozhang Xu. (2022). Establishing a seasonal ARIMA prediction model of varicella incidence in Ningbo city using Python programming language. Chinese Journal of Vaccines and Immunization, 28(01), 83–87+

Guoyun Zhang and Hui Jin. (2022). Research on the prediction of short-term passenger flow of urban rail transit based on improved ARIMA model. Computer Applications and Software, 39(01), 339–344.

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Published

2023-01-31

How to Cite

Yin, C. ., Liu, K. ., Zhang, Q. ., Hu, K. ., Yang, Z. ., Yang, L. ., & Zhao, N. . (2023). SARIMA-Based Medium- and Long-Term Load Forecasting. Strategic Planning for Energy and the Environment, 42(02), 283–306. https://doi.org/10.13052/spee1048-5236.4222

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