Skip to content
2000
  1. Home
  2. A-Z Publications
  3. The Chinese Journal of Artificial Intelligence
  4. Volume 1, Issue 1
  5. Article
Volume 1, Issue 1
  • ISSN: 2666-7827
  • E-ISSN: 2666-7835

Abstract

The aim of this study is to perform short-term load forecasting.

Short-term load forecasting plays a key role in power dispatching. It provides basic data for basic power generation planning and system safety analysis so that the power dispatching work is more practical and the power generation efficiency is higher.

The aim of this study is to ensure the safe operation of the electricity market and relieve the pressure of supply and demand.

In this paper, the SVR model is used for short-term load prediction.

The SVR model has the advantage of minimizing the structural risk and has good generalization performance for the predicted object. At the same time, the global optimization is ensured, a lot of mapping calculation is reduced, the actual risk is reduced, and the prediction performance is improved.

The target model has higher forecasting accuracy than other forecasting models and can effectively solve the problems of the power market.

© 2022 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cjai/10.2174/2666782701666210614223415
2022-04-01
2024-11-22

  • From This Site

    /content/journals/cjai/10.2174/2666782701666210614223415
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. KimY. SonH. KimS. Short term electricity load forecasting for institutional buildings.Energy Reports201851270128010.1016/j.egyr.2019.08.086
     [Google Scholar]
  2. BianH. ZhongY. SunJ. ShiF. Study on power consumption load forecast based on K-means clustering and FCM–BP model.Energy Reports2020669370010.1016/j.egyr.2020.11.148
     [Google Scholar]
  3. CaroE. JuanJ. CaraJ. Periodically correlated models for short-term electricity load forecasting.Appl. Math. Comput.202036412464210.1016/j.amc.2019.124642
     [Google Scholar]
  4. ChuY. XuP. LiM. ChenZ. ChenZ. ChenY. LiW. Short-term metropolitan-scale electric load forecasting based on load decomposition and ensemble algorithms.Energy Build.202022511034310.1016/j.enbuild.2020.110343
     [Google Scholar]
  5. ZhuX. DangY. DingS. Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China.Energy202019011641710.1016/j.energy.2019.116417
     [Google Scholar]
  6. LiuC. WuW.Z. XieW. ZhangJ. Application of a novel fractional grey prediction model with time power term to predict the electricity consumption of India and China.Chaos Solitons Fractals202014111042910.1016/j.chaos.2020.110429
     [Google Scholar]
  7. ZhangX. WangJ. A novel decomposition‐ensemble model for forecasting short‐term load‐time series with multiple seasonal patterns.Appl. Soft Comput.20186547849410.1016/j.asoc.2018.01.017
     [Google Scholar]
  8. El-HendawiM. WangZ. An ensemble method of full wavelet packet transform and neural network for short term electrical load forecasting.Electr. Power Syst. Res.202018210626510.1016/j.epsr.2020.106265
     [Google Scholar]
  9. MaX. DongY. An estimating combination method for interval forecasting of electrical load time series.Expert Syst. Appl.202015811349810.1016/j.eswa.2020.113498
     [Google Scholar]
  10. FanC. DingC. ZhengJ. XiaoL. AiZ. Empirical mode decomposition based multi-objective deep belief network for short-term power load forecasting.Neurocomputing202038811012310.1016/j.neucom.2020.01.031
     [Google Scholar]
  11. QiuX. SuganthanP.N. AmaratungaG.A.J. Short-term electricity price forecasting with empirical mode decomposition based ensemble kernel machines.Procedia Comput. Sci.20171081308131710.1016/j.procs.2017.05.055
     [Google Scholar]
  12. SadaeiH.J. GuimaraesF.G. da SilvaC.J. LeeM.H. EslamiT. Short-term load forecasting method based on fuzzy time series, seasonality and long memory process.Int. J. Approx. Reason.20178319621710.1016/j.ijar.2017.01.006
     [Google Scholar]
  13. SadaeiH.J. de Lima e Silva, P.C.; Guimaraes, F.G.; Lee, M.H. Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series.Energy201917536537710.1016/j.energy.2019.03.081
     [Google Scholar]
  14. ChenJ.F. WangW.M. HuangC.M. Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting.Electr. Power Syst. Res.19953418719610.1016/0378‑7796(95)00977‑1
     [Google Scholar]
  15. DudekG. Pattern-based local linear regression models for short-term load forecasting.Electr. Power Syst. Res.201613013914710.1016/j.epsr.2015.09.001
     [Google Scholar]
  16. WuF. CattaniC. SongW. ZioE. Fractional ARIMA with an improved cuckoo search optimization for the efficient Short-term power load forecasting.Alexandria Eng. J.2020593111311810.1016/j.aej.2020.06.049
     [Google Scholar]
  17. ChitaliaG. PipattanasompornM. Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks.Appl. Energy202027811541010.1016/j.apenergy.2020.115410
     [Google Scholar]
  18. MuzaffarS. AfshariA. Short-term load forecasts using lstm networks.Energy Procedia20191582922292710.1016/j.egypro.2019.01.952
     [Google Scholar]
  19. AhmadT. ChenH. Nonlinear autoregressive and random forest approaches to forecasting electricity load for utility energy management systems.Sustain Cities Soc20194546047310.1016/j.scs.2018.12.013
     [Google Scholar]
  20. GuoW. CheL. ShahidehpourM. WanX. Machine-learning based methods in short-term load forecasting.Electr. J.20213410688410.1016/j.tej.2020.106884
     [Google Scholar]
  21. LiY. CheJ. YangY. Subsampled support vector regression ensemble for short term electric load forecasting.Energy201816416017010.1016/j.energy.2018.08.169
     [Google Scholar]
/content/journals/cjai/10.2174/2666782701666210614223415
Loading
Application of Support Vector Regression and Time Series Method in Short-term Power Load Forecasting with Regional Difference
Chin J Arti Intel 1, E190721194078 (2022); https://doi.org/10.2174/2666782701666210614223415
/content/journals/cjai/10.2174/2666782701666210614223415
/content/journals/cjai/10.2174/2666782701666210614223415
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): ARIMA; empirical mode decomposition (EMD); intelligent algorithm; regional difference; Short-term load forecasting; support vector regression (SVR)

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test