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Author Ciarreta, A.; Martinez, B.; Nasirov, S.
Title Forecasting electricity prices using bid data Type
Year 2023 Publication International Journal of Forecasting Abbreviated Journal Int. J. Forecast.
Volume 39 Issue 3 Pages (down) 1253-1271
Abstract Market liberalization and the expansion of variable renewable energy sources in power systems have made the dynamics of electricity prices more uncertain, leading them to show high volatility with sudden, unexpected price spikes. Thus, developing more accurate price modeling and forecasting techniques is a challenge for all market par-ticipants and regulatory authorities. This paper proposes a forecasting approach based on using auction data to fit supply and demand electricity curves. More specifically, we fit linear (LinX-Model) and logistic (LogX-Model) curves to historical sale and purchase bidding data from the Iberian electricity market to estimate structural parameters from 2015 to 2019. Then we use time series models on structural parameters to predict day-ahead prices. Our results provide a solid framework for forecasting electricity prices by capturing the structural characteristics of markets.& COPY; 2022 The Author(s). Published by Elsevier B.V. on behalf of International Institute of Forecasters. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0169-2070 ISBN Medium
Area Expedition Conference
Notes WOS:001035472900001 Approved
Call Number UAI @ alexi.delcanto @ Serial 1850
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