Prediction of Coal Demand for Long-Term Power System Planning Based on Hybrid SSA and LSSVM Algorithms
Source abstract
Abstract
Accurate prediction of coal demand is essential for optimizing energy resources in long-term power system planning. This paper examines the coal demand in North China from 2007 to 2022 using econometric methods to identify key influencing factors as input variables. Then, the Sparrow Search Algorithm (SSA) is used to optimize the key parameters of the Least Squares Support Vector Machine (LSSVM) algorithm to enhance the prediction accuracy of coal demand. Case studies are conducted on actual data in North China, and the results show that the proposed hybrid SSA and LSSVM method outperforms traditional approaches in small-sample, multivariable forecasting, making it suitable for predictions in long-term power system planning.
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Research context
OpenAlex topics: Energy Load and Power Forecasting · Grey System Theory Applications · Smart Grid and Power Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Grid Jiangsu Electric Power Co., Ltd., Economic Research Institute, Nanjing 210008, China
The Zhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University, Haining 314400, China
Funding
China Postdoctoral Science Foundation
References (20)
Show source-provided references
1. The forecast of coal sales taking the factors influencing the demand for hard coal into account
2. ARIMA forecasting of China’s coal consumption, price and investment by 203010.1080/15567249.2017.1423413 ↗
3. The demand for coal among China’s rural households: Estimates of price and income elasticities10.1016/j.eneco.2019.03.005 ↗
4. Jia, Z., Zhou, Z., Zhang, H., Li, B., and Zhang, Y.X. (2020). Forecast of coal consumption in Gansu Province based on Grey-Markov chain model. Energy, 199.10.1016/j.energy.2020.117444 ↗
5. Liu, Y., Du, R., and Niu, D. (2022). Forecast of coal demand in shanxi province based on GA—LSSVM under Multiple Scenarios. Energies, 15.10.3390/en15176475 ↗
6. Li, S., and Li, R. (2017). Comparison of forecasting energy consumption in Shandong, China Using the ARIMA model, GM model, and ARIMA-GM model. Sustainability, 9.10.3390/su9071181 ↗
7. China’s present situation of coal consumption and future coal demand forecast10.1016/S1872-583X(09)60009-7 ↗
8. Short-term wind power prediction based on LSSVM–GSA model10.1016/j.enconman.2015.05.065 ↗
9. Coal price forecast based on ARIMA model10.18282/ff.v9i4.1530 ↗
10. Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine10.1016/j.energy.2017.04.094 ↗
11. Photovoltaic forecast based on hybrid PCA–LSSVM using dimensionality reducted data10.1016/j.neucom.2016.01.104 ↗
12. A novel multivariable grey prediction model and its application in forecasting coal consumption10.1016/j.isatra.2021.03.024 ↗
13. How much does financial development contribute to renewable energy growth and upgrading of energy structure in China?10.1016/j.enpol.2018.12.047 ↗
14. Coal production and consumption analysis, and forecasting of related carbon emission: Evidence from China
15. Least Squares Support Vector Machine Classifiers10.1023/A:1018628609742 ↗
16. Baesens, B., Viaene, S., Van Gestel, T., Suykens, J., Dedene, G., De Moor, B., and Vanthienen, J. (2000). Least Squares Support Vector Machine Classifiers: An Empirical Evaluation, Katholieke Universiteit Leuve.
17. Salp swarm algorithm: A comprehensive survey10.1007/s00521-019-04629-4 ↗
18. Evidence on structural instability in macroeconomic time series relations10.1080/07350015.1996.10524626 ↗
19. (2016). The Central Compilation Group of the Communist Party of China Outline of the 13th Five Year Plan for National Economic and Social Development of the People’s Republic of China, People’s Publishing House.
20. Li, Y. (2016). Analysis and Forecast of China’s Economic Situation in 2017, Social Sciences Literature Press.