智能电网
Smart Grid

《智能电网》是一本开放获取、电网领域最新进展的国际中文期刊，主要刊登有关国内外智能电网框架结构和概念、智能电网发展领域内最新研究动态的相关论文。本刊支持思想创新、学术创新，倡导科学，繁荣学术，集学术性、思想性为一体，旨在给世界范围内的科学家、学者、科研人员提供一个传播、分享和讨论电网智能领域内不同方向问题与发展的交流平台。

ISSN Print: 2161-8763
ISSN Online: 2161-8771
中国教图刊号：932B0009
编辑邮箱: sg@hanspub.org 微信号：hansi-huang
Website: https://www.hanspub.org/journal/SG.html

期刊菜单

基于XGBoost算法的短期电力负荷预报
Short-Term Electricity Load Forecasting Based on the XGBoost Algorithm
李广野, 李伟, 田小蕾, 车轶锋
智能电网 Vol. 7 No. 4 (August 2017)
被下列文章引用：

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[1]	Efficient Self-attention with Relative Position Encoding for Electric Power Load Forecasting
	2022

[2]	Short-term load forecasting method with variational mode decomposition and stacking model fusion
	2022

[3]	Forecasting China's Steam Coal Prices Using Dynamic Factors and Mixed-Frequency Data.
	2021

[4]	Aboveground mangrove biomass estimation in Beibu Gulf using machine learning and UAV remote sensing
	2021

[5]	Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings
	2021

[6]	Research on Optimization and Adjustment of Gas Turbine Combustion Based on XGBoost and NSGA-II
	2021

[7]	Electrical Load Forecasting Based on Multi-model Combination by Stacking Ensemble Learning Algorithm
	2021

[8]	GANs-LSTM Model for Soil Temperature Estimation From Meteorological: A New Approach
	2020

[9]	基于优化聚类的 IXGBoost 短期电力负荷预测
	2020

[10]	A Dynamic Multi-output Prediction Model of the Hydrogen Network in a Real-World Refinery Based on XGBoost Model
	2020

[11]	基于 XGBoost 模型的炼油厂氢气网络动态多输出预测模型
	2020

[12]	Application of residual self-fitting ensemble neural network based on LSTM in short-term power load forecasting
	2019

[13]	The Impact of External Features on Prediction Accuracy in Short-Term Energy Forecasting
	2019

[14]	A hybrid predictive model for high-frequency and multi-periodic data in call center of online travel agency
	2018

[15]	A new hybrid method for China's energy supply security forecasting based on arima and xgboost
	2018

[16]	Short-Term Power Load Forecasting Based on Clustering and XGBoost Method
	2018

[1]	Application of residual self-fitting ensemble neural network based on LSTM in short-term power load forecasting Journal of Physics: Conference Series, 2019 DOI:10.1088/1742-6596/1314/1/012029

[2]	A New Hybrid Method for China’s Energy Supply Security Forecasting Based on ARIMA and XGBoost Energies, 2018 DOI:10.3390/en11071687

[3]	Aboveground mangrove biomass estimation in Beibu Gulf using machine learning and UAV remote sensing Science of The Total Environment, 2021 DOI:10.1016/j.scitotenv.2021.146816

[4]	Aboveground mangrove biomass estimation in Beibu Gulf using machine learning and UAV remote sensing Science of The Total Environment, 2021 DOI:10.1016/j.scitotenv.2021.146816

[5]	Electrical Load Forecasting Based on Multi-model Combination by Stacking Ensemble Learning Algorithm 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), 2021 DOI:10.1109/ICAICA52286.2021.9498248

[6]	Recent Developments in Mathematical, Statistical and Computational Sciences Springer Proceedings in Mathematics & Statistics, 2021 DOI:10.1007/978-3-030-63591-6_40

[7]	Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings Energies, 2021 DOI:10.3390/en14185831

[8]	Application of residual self-fitting ensemble neural network based on LSTM in short-term power load forecasting Journal of Physics: Conference Series, 2019 DOI:10.1088/1742-6596/1314/1/012029

[9]	Aboveground mangrove biomass estimation in Beibu Gulf using machine learning and UAV remote sensing Science of The Total Environment, 2021 DOI:10.1016/j.scitotenv.2021.146816

[10]	GANs-LSTM Model for Soil Temperature Estimation From Meteorological: A New Approach IEEE Access, 2020 DOI:10.1109/ACCESS.2020.2982996

[11]	A hybrid predictive model for high-frequency and multi-periodic data in call center of online travel agency 2018 IEEE International Conference on Big Data (Big Data), 2018 DOI:10.1109/BigData.2018.8621977

[12]	Research on Optimization and Adjustment of Gas Turbine Combustion Based on XGBoost and NSGA-II 2021 6th International Conference on Power and Renewable Energy (ICPRE), 2021 DOI:10.1109/ICPRE52634.2021.9635252

[13]	Efficient Self-attention with Relative Position Encoding for Electric Power Load Forecasting Journal of Physics: Conference Series, 2022 DOI:10.1088/1742-6596/2205/1/012009

[14]	A robust spatio‐temporal prediction approach for wind power generation based on spectral temporal graph neural network IET Renewable Power Generation, 2022 DOI:10.1049/rpg2.12449

[15]	Short-term load forecasting method with variational mode decomposition and stacking model fusion Sustainable Energy, Grids and Networks, 2022 DOI:10.1016/j.segan.2022.100622

[16]	Uncertainty management in electricity demand forecasting with machine learning and ensemble learning: Case studies of COVID-19 in the US metropolitans Engineering Applications of Artificial Intelligence, 2023 DOI:10.1016/j.engappai.2023.106350

[17]	Aboveground mangrove biomass estimation in Beibu Gulf using machine learning and UAV remote sensing Science of The Total Environment, 2021 DOI:10.1016/j.scitotenv.2021.146816

[18]	Short-term load forecasting method with variational mode decomposition and stacking model fusion Sustainable Energy, Grids and Networks, 2022 DOI:10.1016/j.segan.2022.100622

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