FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2912295345> ?p ?o ?g. }

• W2912295345 endingPage "625" @default.
• W2912295345 startingPage "610" @default.
• W2912295345 abstract "Until recently, detailed information on the power system state to estimate future spot prices by regression analysis was generally restricted to qualified parties. However, to ensure transparency in operation, the Spanish Transmission System Operator has launched an informative web in which a sizable amount of real-time energy-related data can be consulted through a graphical interface. Undoubtedly, this provides the opportunity for non-qualified parties to develop applications and algorithms in which price forecast and maybe knowledge about how price is determined are required. This paper approaches the use of data extracted from that interface with two aims: the prediction of the day-ahead price in a simple way, and the exploration of the influence that the underlying energy drivers have on it. For the prediction we specified a quantile regression model based on Gradient Boosted Regression Trees. It improves the accuracy over multiple linear regression models at the cost of more complexity, and still it has simpler specification and tuning compared to other machine learning approaches. The calculated metrics show that our model produces remarkably low prediction errors when using the median as point prediction method (RMSE = 2.78 €/MWh, MAE = 1.94 €/MWh, and MAPE = 0.059). Interestingly, the quantile regression model also allows to inherently define prediction intervals, with a different interpretation of accuracy. Our results show that on average 90% of times the prediction error will not exceed 6.8 €/MWh. We also implemented a partial dependence analysis on that model. This implementation—as far as we know the first time employed to analyze the formation of electricity prices—has shown to be of significant usefulness in detecting highly non-linear relationships." @default.
• W2912295345 created "2019-02-21" @default.
• W2912295345 creator A5031789341 @default.
• W2912295345 creator A5083977048 @default.
• W2912295345 creator A5089174958 @default.
• W2912295345 date "2019-04-01" @default.
• W2912295345 modified "2023-10-13" @default.
• W2912295345 title "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression" @default.
• W2912295345 cites W1970211605 @default.
• W2912295345 cites W1998025025 @default.
• W2912295345 cites W2000162856 @default.
• W2912295345 cites W2039240409 @default.
• W2912295345 cites W2050660109 @default.
• W2912295345 cites W2054458966 @default.
• W2912295345 cites W2066035378 @default.
• W2912295345 cites W2076743673 @default.
• W2912295345 cites W2088548330 @default.
• W2912295345 cites W2088794999 @default.
• W2912295345 cites W2089217930 @default.
• W2912295345 cites W2090457102 @default.
• W2912295345 cites W2105624819 @default.
• W2912295345 cites W2108647521 @default.
• W2912295345 cites W2170831719 @default.
• W2912295345 cites W2268377817 @default.
• W2912295345 cites W2272544568 @default.
• W2912295345 cites W2286305802 @default.
• W2912295345 cites W2295452988 @default.
• W2912295345 cites W2315598830 @default.
• W2912295345 cites W2552679408 @default.
• W2912295345 cites W2570991997 @default.
• W2912295345 cites W2571217044 @default.
• W2912295345 cites W2591064656 @default.
• W2912295345 cites W2595940211 @default.
• W2912295345 cites W2605614336 @default.
• W2912295345 cites W2625224297 @default.
• W2912295345 cites W2735666795 @default.
• W2912295345 cites W2740700484 @default.
• W2912295345 cites W2741080543 @default.
• W2912295345 cites W275578392 @default.
• W2912295345 cites W2767678610 @default.
• W2912295345 cites W2772858445 @default.
• W2912295345 cites W2781819803 @default.
• W2912295345 cites W2783421848 @default.
• W2912295345 cites W2799827709 @default.
• W2912295345 cites W2802586787 @default.
• W2912295345 cites W2808604553 @default.
• W2912295345 cites W2887464323 @default.
• W2912295345 cites W3124621140 @default.
• W2912295345 cites W3163650977 @default.
• W2912295345 doi "https://doi.org/10.1016/j.apenergy.2019.01.213" @default.
• W2912295345 hasPublicationYear "2019" @default.
• W2912295345 type Work @default.
• W2912295345 sameAs 2912295345 @default.
• W2912295345 citedByCount "31" @default.
• W2912295345 countsByYear W29122953452020 @default.
• W2912295345 countsByYear W29122953452021 @default.
• W2912295345 countsByYear W29122953452022 @default.
• W2912295345 countsByYear W29122953452023 @default.
• W2912295345 crossrefType "journal-article" @default.
• W2912295345 hasAuthorship W2912295345A5031789341 @default.
• W2912295345 hasAuthorship W2912295345A5083977048 @default.
• W2912295345 hasAuthorship W2912295345A5089174958 @default.
• W2912295345 hasConcept C105795698 @default.
• W2912295345 hasConcept C119599485 @default.
• W2912295345 hasConcept C119857082 @default.
• W2912295345 hasConcept C124101348 @default.
• W2912295345 hasConcept C127413603 @default.
• W2912295345 hasConcept C139945424 @default.
• W2912295345 hasConcept C146733006 @default.
• W2912295345 hasConcept C149782125 @default.
• W2912295345 hasConcept C150217764 @default.
• W2912295345 hasConcept C152877465 @default.
• W2912295345 hasConcept C154945302 @default.
• W2912295345 hasConcept C206658404 @default.
• W2912295345 hasConcept C2781104810 @default.
• W2912295345 hasConcept C33923547 @default.
• W2912295345 hasConcept C41008148 @default.
• W2912295345 hasConcept C45804977 @default.
• W2912295345 hasConcept C48921125 @default.
• W2912295345 hasConcept C50644808 @default.
• W2912295345 hasConcept C63817138 @default.
• W2912295345 hasConcept C83546350 @default.
• W2912295345 hasConceptScore W2912295345C105795698 @default.
• W2912295345 hasConceptScore W2912295345C119599485 @default.
• W2912295345 hasConceptScore W2912295345C119857082 @default.
• W2912295345 hasConceptScore W2912295345C124101348 @default.
• W2912295345 hasConceptScore W2912295345C127413603 @default.
• W2912295345 hasConceptScore W2912295345C139945424 @default.
• W2912295345 hasConceptScore W2912295345C146733006 @default.
• W2912295345 hasConceptScore W2912295345C149782125 @default.
• W2912295345 hasConceptScore W2912295345C150217764 @default.
• W2912295345 hasConceptScore W2912295345C152877465 @default.
• W2912295345 hasConceptScore W2912295345C154945302 @default.
• W2912295345 hasConceptScore W2912295345C206658404 @default.
• W2912295345 hasConceptScore W2912295345C2781104810 @default.
• W2912295345 hasConceptScore W2912295345C33923547 @default.
• W2912295345 hasConceptScore W2912295345C41008148 @default.
• W2912295345 hasConceptScore W2912295345C45804977 @default.

• next