Intelligent System for Power Load Forecasting in Off-grid Platform

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Intelligent System for Power Load Forecasting in Off-grid Platform

Popis výsledku v původním jazyce

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%.

Název v anglickém jazyce

Intelligent System for Power Load Forecasting in Off-grid Platform

Popis výsledku anglicky

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%.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

Počet stran výsledku

5

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Brno

Datum konání akce

16. 5. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000439649500086