Nonparametric probabilistic load forecasting based on quantile combination in electrical power systems
Authors:
- Yaoyao HeYHYaoyao He
- Chaojin CaoCCChaojin Cao
- Shuo WangSWShuo Wang
- Hong FuHFHong Fu
- 1 July 2022
- journal article
- research article
- Published by Elsevier in Applied Energy
- Vol. 322
Abstract
No abstract availableKeywords
Funding Information
- Anhui Provincial Natural Science Foundation (2108085J36)
- Science Fund for Distinguished Young Scholars of Anhui Province
- National Natural Science Foundation of China (71771073, 72171068)
This publication has 44 references indexed in Scilit:
- A comparison of AdaBoost algorithms for time series forecast combinationInternational Journal of Forecasting, 2016
- GEFCom2014: Probabilistic solar and wind power forecasting using a generalized additive tree ensemble approachInternational Journal of Forecasting, 2016
- Improving short term load forecast accuracy via combining sister forecastsEnergy, 2016
- Day-ahead load forecast using random forest and expert input selectionEnergy Conversion and Management, 2015
- Ensemble approaches for regressionACM Computing Surveys, 2012
- An Improved IAMB Algorithm for Markov Blanket DiscoveryJournal of Computers, 2010
- Strictly Proper Scoring Rules, Prediction, and EstimationJournal of the American Statistical Association, 2007
- Combining density forecastsInternational Journal of Forecasting, 2007
- A new test for multivariate normalityJournal of Multivariate Analysis, 2005
- Long Short-Term MemoryNeural Computation, 1997