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Ultra-Short Term Power Load Prediction Based on Gated Cycle Neural Network and XGBoost Models

  • Published:2021-09-01 Issue:1 Volume:2026 Page:012022
  • ISSN:1742-6588
  • Container-title:Journal of Physics: Conference Series
  • language:
  • Short-container-title:J. Phys.: Conf. Ser.

Author:

Li Xiaojin,Huang Yueyang,Shi Yuanbo

Publisher

IOP Publishing

Subject

General Physics and Astronomy

Reference16 articles.

1. Long term electricity forecast: A systematic review;Esteves;Procedia Computer Science,2015

2. The time series approach to short term load forecasting;Hagan;IEEE Transactions on Power Systems,1987

3. Quantitative analysis model of power load influencing factors based on improved grey correlation degree;Wang;Powergrid Technology,2017

4. Data-driven baseline estimation of residential buildings for demand response;Park;Energies,2015

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1. An Ultra-short-term Power Load Forecasting Method Based on XGBoost and Fusion Feature Optimization;2024 6th International Conference on Energy Systems and Electrical Power (ICESEP);2024-06-21

2. PM2.5 Concentration Prediction Model Based on Random Forest and SHAP;International Journal of Pattern Recognition and Artificial Intelligence;2024-04

3. Enterprise Application Outage Prediction Using XGBoost and LSTM;2023 19th International Conference on Network and Service Management (CNSM);2023-10-30

4. Mid- to Long-Term Electric Load Forecasting Based on the EMD–Isomap–Adaboost Model;Sustainability;2022-06-22
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