Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications-Reference-Cited by-同舟云学术

Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications

Published:2023-03-26 Issue:7 Volume:16 Page:3024
ISSN:1996-1073
Container-title:Energies
language:en
Short-container-title:Energies

Author:

Dudek Grzegorz¹^ORCID,Piotrowski Paweł²^ORCID,Baczyński Dariusz²^ORCID

Affiliation:

1. Faculty of Electrical Engineering, Czestochowa University of Technology, 42-200 Czestochowa, Poland

2. Electrical Power Engineering Institute, Warsaw University of Technology, 00-662 Warsaw, Poland

Abstract

A modern power system is a complex network of interconnected components, such as generators, transmission lines, and distribution subsystems, that are designed to provide electricity to consumers in an efficient and reliable manner [...]

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

Link

https://www.mdpi.com/1996-1073/16/7/3024/pdf

Reference18 articles.

1. Piotrowski, P., Baczyński, D., and Kopyt, M. (2022). Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development. Energies, 15.

2. Sulandari, W., Yudhanto, Y., and Rodrigues, P.C. (2022). The Use of Singular Spectrum Analysis and K-Means Clustering-Based Bootstrap to Improve Multistep Ahead Load Forecasting. Energies, 15.

3. Czapaj, R., Kamiński, J., and Sołtysik, M. (2022). A Review of Auto-Regressive Methods Applications to Short-Term Demand Forecasting in Power Systems. Energies, 15.

4. Dudek, G. (2022). A Comprehensive Study of Random Forest for Short-Term Load Forecasting. Energies, 15.

5. Pełka, P. (2023). Analysis and Forecasting of Monthly Electricity Demand Time Series Using Pattern-Based Statistical Methods. Energies, 16.

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