Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/111796
Title: Medium-Term Load Forecasting in Isolated Power Systems Based on Ensemble Machine Learning Models
Authors: Matrenin, P.
Safaraliev, M.
Dmitriev, S.
Kokin, S.
Ghulomzoda, A.
Mitrofanov, S.
Issue Date: 2022
Publisher: Elsevier Ltd
Elsevier BV
Citation: Medium-Term Load Forecasting in Isolated Power Systems Based on Ensemble Machine Learning Models / P. Matrenin, M. Safaraliev, S. Dmitriev et al. // Energy Reports. — 2022. — Vol. 8. — P. 612-618.
Abstract: Over the past decades, power companies have been implementing load forecasting to determine trends in the electric power system (EPS); therefore, load forecasting is applied to solve the problems of management and development of power systems. This paper considers the issue of building a model of medium-term forecasting of load graphs for EPS with specific properties, based on the use of ensemble machine learning methods. This paper implements the approach of identification of the most significant features to apply machine learning models for medium-term load forecasting in an isolated power system. A comparative study of the following models was carried out: linear regression, support vector regression (SVR), decision tree regression, random forest (Random Forest), gradient boosting over decision trees (XGBoost), adaptive boosting over decision trees (AdaBoost), AdaBoost over linear regression. Isolation of features from a time series allows for the implementation of simpler and more overfitting-resistant models. All the above makes it possible to increase the reliability of forecasts and expand the use of information technologies in the planning, management, and operation of isolated EPSs. Calculations of the total forecast error have proved that the characteristics of the proposed models are high quality and accurate, and thus they can be used to forecast the real load of a power system. © 2021 The Author(s).
Keywords: ELECTRIC POWER SYSTEM
ENSEMBLE MODELS
ISOLATED POWER SYSTEM
MEDIUM-TERM FORECASTING
ADAPTIVE BOOSTING
ELECTRIC POWER PLANT LOADS
ELECTRIC UTILITIES
FORECASTING
INFORMATION USE
ENSEMBLE MODELS
ISOLATED POWER SYSTEM
LOAD FORECASTING
LOAD GRAPHS
MACHINE LEARNING MODELS
MEDIUM TERM
POWER
POWER COMPANY
RANDOM FORESTS
SPECIFIC PROPERTIES
DECISION TREES
URI: http://hdl.handle.net/10995/111796
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85121451920
PURE ID: 29576702
ISSN: 2352-4847
metadata.dc.description.sponsorship: The reported study was funded by RFBR, Sirius University of Science and Technology, JSC Russian Railways and Educational Fund “Talent and success”, project number 20-38-51007.
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

Files in This Item:
File Description SizeFormat 
2-s2.0-85121451920.pdf773,24 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.