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A Predictive Model for Forecasting Locational Marginal Price in Electricity Markets /

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Predictive Model for Forecasting Locational Marginal Price in Electricity Markets /Zigfred Nino C Viray.
Author:	Viray, Zigfred Nino C.,
Description:	1 electronic resource (125 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
Notes:	Advisors: Biemer, Steven; Blackford, Joseph P. Committee members: Bierce, Jonathan.
Contained By:	Dissertations Abstracts International86-06B.
Subject:	Engineering.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31639821
ISBN:	9798346764601

A Predictive Model for Forecasting Locational Marginal Price in Electricity Markets /
Viray, Zigfred Nino C.,

A Predictive Model for Forecasting Locational Marginal Price in Electricity Markets /Zigfred Nino C Viray. - 1 electronic resource (125 pages)

Source: Dissertations Abstracts International, Volume: 86-06, Section: B.

This praxis develops a predictive model for forecasting Locational Marginal Prices (LMP), considering their time-varying and locational characteristics to deliver valuable price insights for U.S. electricity market participants, including electric utilities and power producers. These insights can aid in formulating trading strategies to manage price fluctuations and risks within LMP-based markets.Statistical and machine learning predictive models were implemented and tested using publicly available electricity market data to forecast LMP across different timeframes, pricing conditions, and locations within the Pennsylvania-New Jersey-Maryland Interconnection (PJM), which operates the world's largest electricity market. Feature engineering was applied to capture the spatiotemporal nature of LMPs, and a Recursive Feature Elimination (RFE) process was used to rank features, resulting in a simpler and more efficient model.Performance metrics were assessed for all predictive models across various locations, pricing scenarios, and forecasting horizons. This approach enables selecting the most suitable model for specific market conditions, locations, and timeframes for improved decision-making in electricity markets.

English

ISBN: 9798346764601Subjects--Topical Terms:

210888
Engineering.
Subjects--Index Terms:

Electricity market

A Predictive Model for Forecasting Locational Marginal Price in Electricity Markets /
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520 $a This praxis develops a predictive model for forecasting Locational Marginal Prices (LMP), considering their time-varying and locational characteristics to deliver valuable price insights for U.S. electricity market participants, including electric utilities and power producers. These insights can aid in formulating trading strategies to manage price fluctuations and risks within LMP-based markets.Statistical and machine learning predictive models were implemented and tested using publicly available electricity market data to forecast LMP across different timeframes, pricing conditions, and locations within the Pennsylvania-New Jersey-Maryland Interconnection (PJM), which operates the world's largest electricity market. Feature engineering was applied to capture the spatiotemporal nature of LMPs, and a Recursive Feature Elimination (RFE) process was used to rank features, resulting in a simpler and more efficient model.Performance metrics were assessed for all predictive models across various locations, pricing scenarios, and forecasting horizons. This approach enables selecting the most suitable model for specific market conditions, locations, and timeframes for improved decision-making in electricity markets.
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653 $a Forecasting
653 $a Locational marginal price
653 $a Machine learning
653 $a Predictive model
653 $a Recursive feature elimination
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