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Locational Marginal...
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Cantillo-Luna, Sergio
(author)
Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets
- Article/chapterEnglish2022
Publisher, publication year, extent ...
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2022-01-01
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MDPI AG,2022
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printrdacarrier
Numbers
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LIBRIS-ID:oai:DiVA.org:kth-307063
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https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-307063URI
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https://doi.org/10.3390/en15010293DOI
Supplementary language notes
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Language:English
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Summary in:English
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Subject category:ref swepub-contenttype
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Subject category:art swepub-publicationtype
Notes
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QC 20220314
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Electricity markets provide valuable data for regulators, operators, and investors. The use of machine learning methods for electricity market data could provide new insights about the market, and this information could be used for decision-making. This paper proposes a tool based on multi-output regression method using support vector machines (SVR) for LMP forecasting. The input corresponds to the active power load of each bus, in this case obtained through Monte Carlo simulations, in order to forecast LMPs. The LMPs provide market signals for investors and regulators. The results showed the high performance of the proposed model, since the average prediction error for fitting and testing datasets of the proposed method on the dataset was less than 1%. This provides insights into the application of machine learning method for electricity markets given the context of uncertainty and volatility for either real-time and ahead markets.
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Added entries (persons, corporate bodies, meetings, titles ...)
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Moreno-Chuquen, Ricardo
(author)
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Chamorro Vera, Harold R.KTH,Elkraftteknik(Swepub:kth)u12rc2ga
(author)
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Riquelme-Dominguez, Jose Miguel
(author)
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Gonzalez-Longatt, Francisco
(author)
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KTHElkraftteknik
(creator_code:org_t)
Related titles
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In:Energies: MDPI AG15:11996-1073
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