Pretraga

Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 1009990

Reducing Wind Power Forecast Error Based on Machine Learning Algorithms and Producers Merging

Srpak, Dunja; Havaš, Ladislav; Skok, Srđan; Polajžer, Boštjan
Reducing Wind Power Forecast Error Based on Machine Learning Algorithms and Producers Merging // Conference Proceedings 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe / Araneo, Rodolfo ; Martirano, Luigi (ur.).
Genova: Institute of Electrical and Electronics Engineers (IEEE), 2019. str. 1483-1488 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)

CROSBI ID: 1009990 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Reducing Wind Power Forecast Error Based on Machine Learning Algorithms and Producers Merging

Autori
Srpak, Dunja ; Havaš, Ladislav ; Skok, Srđan ; Polajžer, Boštjan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Conference Proceedings 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe / Araneo, Rodolfo ; Martirano, Luigi - Genova : Institute of Electrical and Electronics Engineers (IEEE), 2019, 1483-1488

ISBN
978-1-7281-0652-6

Skup
International Conference on Environment and Electrical Engineering ( IEEE 2019) ; Industrial and Commercial Power Systems Europe (EEEIC, I&CPS Europe 2019)

Mjesto i datum
Genova, Italija, 11.06.2019. - 14.06.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Machine Learning ; power system ; forecasting methods ; regulatory frameworks ; Wind Power Plants.

Sažetak
This paper proposes forecasting methods for Wind Power Plants` (WPPs`) generation based on Machine Learning Algorithms. In order to increase the precision of generation forecasts from WPPs further, the methodology is introduced of organising WPP owners in a so-called "ECO balance group”. The described theoretical bases have been applied to the WPPs in the Croatian transmission power system. Deviation calculations were made for the forecasted and realised generation of WPPs in 2015, when the proposed methods were not effective, and for 2019 when these methods had already been applied.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika



POVEZANOST RADA

Ustanove:
Sveučilište Sjever, Koprivnica

Profili:

Avatar Url Srđan Skok (autor)

Avatar Url Ladislav Havaš (autor)

Avatar Url Dunja Srpak (autor)

Citiraj ovu publikaciju:

Srpak, Dunja; Havaš, Ladislav; Skok, Srđan; Polajžer, Boštjan
Reducing Wind Power Forecast Error Based on Machine Learning Algorithms and Producers Merging // Conference Proceedings 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe / Araneo, Rodolfo ; Martirano, Luigi (ur.).
Genova: Institute of Electrical and Electronics Engineers (IEEE), 2019. str. 1483-1488 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Srpak, D., Havaš, L., Skok, S. & Polajžer, B. (2019) Reducing Wind Power Forecast Error Based on Machine Learning Algorithms and Producers Merging. U: Araneo, R. & Martirano, L. (ur.)Conference Proceedings 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe.
@article{article, author = {Srpak, Dunja and Hava\v{s}, Ladislav and Skok, Sr\djan and Polaj\v{z}er, Bo\v{s}tjan}, year = {2019}, pages = {1483-1488}, keywords = {Machine Learning, power system, forecasting methods, regulatory frameworks, Wind Power Plants.}, isbn = {978-1-7281-0652-6}, title = {Reducing Wind Power Forecast Error Based on Machine Learning Algorithms and Producers Merging}, keyword = {Machine Learning, power system, forecasting methods, regulatory frameworks, Wind Power Plants.}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Genova, Italija} }
@article{article, author = {Srpak, Dunja and Hava\v{s}, Ladislav and Skok, Sr\djan and Polaj\v{z}er, Bo\v{s}tjan}, year = {2019}, pages = {1483-1488}, keywords = {Machine Learning, power system, forecasting methods, regulatory frameworks, Wind Power Plants.}, isbn = {978-1-7281-0652-6}, title = {Reducing Wind Power Forecast Error Based on Machine Learning Algorithms and Producers Merging}, keyword = {Machine Learning, power system, forecasting methods, regulatory frameworks, Wind Power Plants.}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Genova, Italija} }

Podijeli:





Contrast
Increase Font
Decrease Font
Dyslexic Font
CROSBI koristi kolačiće (cookies) kako bi poboljšao funkcionalnost stranice.