SwePub
Sök i LIBRIS databas

  Utökad sökning

id:"swepub:oai:DiVA.org:ltu-70294"
 

Sökning: id:"swepub:oai:DiVA.org:ltu-70294" > Exploiting Artifici...

Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices

Giovanelli, Christian (författare)
Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University
Sierla, Seppo (författare)
Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University
Ryutaro, Ichise (författare)
National Institute of Informatics, Tokyo
visa fler...
Vyatkin, Valeriy (författare)
Luleå tekniska universitet,Datavetenskap,Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University
visa färre...
 (creator_code:org_t)
2018-07-21
2018
Engelska.
Ingår i: Energies. - : MDPI. - 1996-1073. ; 115:7
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • The increase of distributed energy resources in the smart grid calls for new ways to profitably exploit these resources, which can participate in day-ahead ancillary energy markets by providing flexibility. Higher profits are available for resource owners that are able to anticipate price peaks and hours of low prices or zero prices, as well as to control the resource in such a way that exploits the price fluctuations. Thus, this study presents a solution in which artificial neural networks are exploited to predict the day-ahead ancillary energy market prices. The study employs the frequency containment reserve for the normal operations market as a case study and presents the methodology utilized for the prediction of the case study ancillary market prices. The relevant data sources for predicting the market prices are identified, then the frequency containment reserve market prices are analyzed and compared with the spot market prices. In addition, the methodology describes the choices behind the definition of the model validation method and the performance evaluation coefficient utilized in the study. Moreover, the empirical processes for designing an artificial neural network model are presented. The performance of the artificial neural network model is evaluated in detail by means of several experiments, showing robustness and adaptiveness to the fast-changing price behaviors. Finally, the developed artificial neural network model is shown to have better performance than two state of the art models, support vector regression and ARIMA, respectively

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

Dependable Communication and Computation Systems
Kommunikations- och beräkningssystem

Publikations- och innehållstyp

ref (ämneskategori)
art (ämneskategori)

Hitta via bibliotek

  • Energies (Sök värdpublikationen i LIBRIS)

Till lärosätets databas

Hitta mer i SwePub

Av författaren/redakt...
Giovanelli, Chri ...
Sierla, Seppo
Ryutaro, Ichise
Vyatkin, Valeriy
Om ämnet
NATURVETENSKAP
NATURVETENSKAP
och Data och informa ...
och Datavetenskap
Artiklar i publikationen
Energies
Av lärosätet
Luleå tekniska universitet

Sök utanför SwePub

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy