Intelligent Systems for Power Load Forecasting: A Study Review

Ibrahim Salem Jahan, Vaclav Snasel and Stanislav Misak
Additional contact information
Ibrahim Salem Jahan: ENET Centre, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic
Vaclav Snasel: Computer Science Department, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic
Stanislav Misak: ENET Centre, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic

Energies, 2020, vol. 13, issue 22, 1-12

Abstract: The study of power load forecasting is gaining greater significance nowadays, particularly with the use and integration of renewable power sources and external power stations. Power forecasting is an important task in the planning, control, and operation of utility power systems. In addition, load forecasting (LF) aims to estimate the power or energy needed to meet the required power or energy to supply the specific load. In this article, we introduce, review and compare different power load forecasting techniques. Our goal is to help in the process of explaining the problem of power load forecasting via brief descriptions of the proposed methods applied in the last decade. The study reviews previous research that deals with the design of intelligent systems for power forecasting using various methods. The methods are organized into five groups—Artificial Neural Network (ANN), Support Vector Regression, Decision Tree (DT), Linear Regression (LR), and Fuzzy Sets (FS). This way, the review provides a clear concept of power load forecasting for the purposes of future research and study.

Keywords: renewable energy sources; load forecasting; smart system; weather data; off-grid system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/22/6105/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/22/6105/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:22:p:6105-:d:448942

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().