Intelligent System for Power Load Forecasting in Off-grid Platform
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10241748" target="_blank" >RIV/61989100:27240/18:10241748 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8396034" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8396034</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/EPE.2018.8396034" target="_blank" >10.1109/EPE.2018.8396034</a>
Alternative languages
Result language
angličtina
Original language name
Intelligent System for Power Load Forecasting in Off-grid Platform
Original language description
Accurate and reliable load forecasting is a very important and required task conditioning the operation and management of electrical power generation systems. It is a key issue especially in planning and controlling the power grid system. The load forecasting process makes part of a smart control system. In off-grid platforms, smart control systems are needed to keep the consumed power equal to generated power as well as to maintain the power quality at standard levels of power quality parameters. Many mathematical models have been designed for load forecasting, including artificial neural network (ANN), decision tree (DT), support vector machine (SVM), fuzzy sets, etc. Still, the power load forecasting remains an open issue. In this article, we introduce an intelligent approach that predicts electrical load using data taken from an off-grid platform. The proposed approach builds on four models, namely K-means with ANN, K-means with DT, K-medoids with ANN, and K-medoids with DT. The article describes the design of these four forecasting models and compares them. The simulation results of the four models were evaluated and compared using mean absolute percentage error (MAPE) criteria. The best forecasting results were obtained using K-medoids clustering combined with ANN, where the MAPE was about 8%.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 2018 19th International Scientific Conference on Electric Power Engineering (EPE)
ISBN
978-1-5386-4612-0
ISSN
2376-5623
e-ISSN
neuvedeno
Number of pages
5
Pages from-to
—
Publisher name
IEEE
Place of publication
New York
Event location
Brno
Event date
May 16, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000439649500086