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Open AccessArticle

Intelligent Systems for Power Load Forecasting: A Study Review

1
ENET Centre, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic
2
Computer Science Department, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic
*
Author to whom correspondence should be addressed.
Energies 2020, 13(22), 6105; https://doi.org/10.3390/en13226105
Received: 22 October 2020 / Revised: 17 November 2020 / Accepted: 18 November 2020 / Published: 21 November 2020
(This article belongs to the Section Smart Grids and Microgrids)
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. View Full-Text
Keywords: renewable energy sources; load forecasting; smart system; weather data; off-grid system renewable energy sources; load forecasting; smart system; weather data; off-grid system
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Jahan, I.S.; Snasel, V.; Misak, S. Intelligent Systems for Power Load Forecasting: A Study Review. Energies 2020, 13, 6105.

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