Evaluating the Impact of Feature Dimensionality on Price Prediction in the Indian Electricity Market.
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| Title: | Evaluating the Impact of Feature Dimensionality on Price Prediction in the Indian Electricity Market. |
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| Authors: | Melepurakkal, Subeekrishna1 (AUTHOR), Raghunadhan, Lekshmi Remadevi1 (AUTHOR) rr_lekshmi@cb.amrita.edu |
| Source: | Energies (19961073). Aug2026, Vol. 19 Issue 16, p3910. 29p. |
| Subject Terms: | *Prediction models, *Electricity markets, *Deep learning, *Machine learning, *Market volatility, *Energy industry forecasting, *Forecasting |
| Abstract: | Accurate forecasting of electricity prices is essential for efficient operation and decision-making in deregulated power markets, particularly in the Indian electricity market, characterized by high volatility and dynamic pricing. This study presents a comparative analysis of statistical, machine learning, and deep learning methods for electricity price prediction in the Indian market, with a focus on feature dimensionality. The evaluated models include autoregressive-integrated-moving average, seasonal autoregressive-integrated-moving average, seasonal autoregressive-integrated-moving average with exogenous variables, categorical boosting, random forest, long short-term memory, bidirectional long short-term memory, and a hybrid convolutional neural network-bidirectional long short-term memory model. Historical data available on the Indian Energy Exchange webpage are deployed in this study. The models are analyzed for varying input vector sizes with features that include date, type of day, day of the week, previous day, month, and year market prices. The results indicate improved predictive performance of all model while increasing the input feature dimensionality from five to seven. The results indicate that while increasing the feature size from five to seven increases the prediction accuracy, the gains become marginal beyond seven, emphasizing the importance of feature relevance over feature quantity. From a theoretical perspective, the study highlights the dominance of short-term temporal dependencies in MCP prediction and provides empirical evidence for the point of diminishing returns in feature expansion. From a practical standpoint, the results endorse the choice of computationally efficient and interpretable models for real-world deployment. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 196655337 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Evaluating the Impact of Feature Dimensionality on Price Prediction in the Indian Electricity Market. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Melepurakkal%2C+Subeekrishna%22">Melepurakkal, Subeekrishna</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Raghunadhan%2C+Lekshmi+Remadevi%22">Raghunadhan, Lekshmi Remadevi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rr_lekshmi@cb.amrita.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Aug2026, Vol. 19 Issue 16, p3910. 29p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br />*<searchLink fieldCode="DE" term="%22Electricity+markets%22">Electricity markets</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Market+volatility%22">Market volatility</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+industry+forecasting%22">Energy industry forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate forecasting of electricity prices is essential for efficient operation and decision-making in deregulated power markets, particularly in the Indian electricity market, characterized by high volatility and dynamic pricing. This study presents a comparative analysis of statistical, machine learning, and deep learning methods for electricity price prediction in the Indian market, with a focus on feature dimensionality. The evaluated models include autoregressive-integrated-moving average, seasonal autoregressive-integrated-moving average, seasonal autoregressive-integrated-moving average with exogenous variables, categorical boosting, random forest, long short-term memory, bidirectional long short-term memory, and a hybrid convolutional neural network-bidirectional long short-term memory model. Historical data available on the Indian Energy Exchange webpage are deployed in this study. The models are analyzed for varying input vector sizes with features that include date, type of day, day of the week, previous day, month, and year market prices. The results indicate improved predictive performance of all model while increasing the input feature dimensionality from five to seven. The results indicate that while increasing the feature size from five to seven increases the prediction accuracy, the gains become marginal beyond seven, emphasizing the importance of feature relevance over feature quantity. From a theoretical perspective, the study highlights the dominance of short-term temporal dependencies in MCP prediction and provides empirical evidence for the point of diminishing returns in feature expansion. From a practical standpoint, the results endorse the choice of computationally efficient and interpretable models for real-world deployment. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=196655337 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19163910 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 3910 Subjects: – SubjectFull: Prediction models Type: general – SubjectFull: Electricity markets Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Market volatility Type: general – SubjectFull: Energy industry forecasting Type: general – SubjectFull: Forecasting Type: general Titles: – TitleFull: Evaluating the Impact of Feature Dimensionality on Price Prediction in the Indian Electricity Market. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Melepurakkal, Subeekrishna – PersonEntity: Name: NameFull: Raghunadhan, Lekshmi Remadevi IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 16 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
