Machine learning based electric load forecasting for short and long-term period

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10240148" target="_blank" >RIV/61989100:27240/18:10240148 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27730/18:10240148

Result on the web

<a href="http://dx.doi.org/10.1109/WF-IoT.2018.8355123" target="_blank" >http://dx.doi.org/10.1109/WF-IoT.2018.8355123</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/WF-IoT.2018.8355123" target="_blank" >10.1109/WF-IoT.2018.8355123</a>

Result language

angličtina

Original language name

Machine learning based electric load forecasting for short and long-term period

Original language description

Electricity is currently the most important energy vector in the domestic sector and industry. Unlike fuels, electricity is hard and expensive to store. This creates the need of precise coupling between generation and demand. In addition, the transmission lines of electric power need to be sized for a given maximum power, and overloading them may result in blackout or electrical accidents. For these reasons, energy consumption forecasting is vital. The time scale for forecasting depends on who is interested in such prediction. Grid operators have to predict the electricity demand for the next day, to program the generation accordingly. Grid designers have to predict energy consumption at the scale of years, to ensure that the infrastructure is sufficient. On the other hand, smart grid controllers with almost instant response time may need a prediction on the order of minutes. We have seen that changing the time scale in electricity load forecasting changes the approach, and that depending on the scale different methods should be used to ensure the highest accuracy with the smallest computational cost. We show here how forecasting accuracy decreases with the increase of time scale due to the impossibility of using of all variables. Several well established computational models were compared on three different regression based criteria and the results revealed that boosting model was able to outperform their competitors in most of the comparisons.

Czech name

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Czech description

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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)

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)

Publication year

2018

Confidentiality

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

Article name in the collection

IEEE World Forum on Internet of Things, WF-IoT 2018 - Proceedings

ISBN

978-1-4673-9944-9

ISSN

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e-ISSN

neuvedeno

Number of pages

6

Pages from-to

511-516

Publisher name

IEEE

Place of publication

Piscataway

Event location

Singapur

Event date

Feb 5, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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