Forecasting energy prices in a competitive market

Bastian, Jeff; Zhu, Jinxiang; Banunarayanan, Venkat; Mukerji, R.

doi:10.1109/67.773811

Cited by 131 publications

(56 citation statements)

References 4 publications

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“…Moreover, deregulation brings electricity prices uncertainty, placing higher requirements on forecasting. In particular, accuracy in forecasting these electricity prices is very critical, since more accuracy in forecasting reduces the risk of under/over estimating the revenue from the generators for the power companies and provides better risk management [3]. Forecast errors have significant implications for profits, market shares and ultimately shareholder value [4].…”

Section: Introductionmentioning

confidence: 99%

Short-term electricity prices forecasting in a competitive market: A neural network approach

Catalão

Mariano

Mendes

et al. 2007

Electric Power Systems Research

359

178

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This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market. Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgement of future price movements, and for consumers to maximize their utilities. A three-layered feedforward neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting next-week electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed neural network approach, reporting the results from the electricity markets of mainland Spain and California.

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Section: Introductionmentioning

confidence: 99%

Short-term electricity prices forecasting in a competitive market: A neural network approach

Catalão

Mariano

Mendes

et al. 2007

Electric Power Systems Research

359

178

View full text Add to dashboard Cite

show abstract

“…The network parameters of the system are given in [64]. The network consists of 6 generator-buses, 21 load-buses and 43 branches, of which 4 branches, (6,9), (6,10), (4,12) and (28,27), are under-load-tap-setting transformer branches.…”

Section: Numerical Resultsmentioning

confidence: 99%

Intelligent energy management

Raghavan

Khaligh

2012

Proceedings of the Workshop on Performance Metrics for Intelligent Systems

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“…The simulation methods which are currently being used by the electric power industry range from the bubble-diagram type contract path models to production simulation models with full electrical representation, such as GE-MAPS software [10]. The production simulation models by nature of their chronological simulation patterns, will consider time varying systems limits and characteristics.…”

Section: Forecasting Models Based On Simulation Methodsmentioning

confidence: 99%

“…Many techniques and models have been developed for forecasting whole sale electricity prices, especially for short term price forecasting [3]. The state of art techniques for electricity price forecasting are categorized into equilibrium analysis [5], simulation methods [10], econometric methods [11], time series [12]- [14], intelligent systems [15]- [17] and volatility analysis [18]. Time series and intelligent systems are commonly used for day-ahead price forecasting.…”

Section: Introductionmentioning

confidence: 99%

A Review of Price Forecasting Problem and Techniques in Deregulated Electricity Markets

Singh¹,

Mohanty²

2015

JPEE

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In deregulated electricity markets, price forecasting is gaining importance between various market players in the power in order to adjust their bids in the day-ahead electricity markets and maximize their profits. Electricity price is volatile but non random in nature making it possible to identify the patterns based on the historical data and forecast. An accurate price forecasting method is an important factor for the market players as it enables them to decide their bidding strategy to maximize profits. Various models have been developed over a period of time which can be broadly classified into two types of models that are mainly used for Electricity Price forecasting are: 1) Time series models; and 2) Simulation based models; time series models are widely used among the two, for day ahead forecasting. The presented work summarizes the influencing factors that affect the price behavior and various established forecasting models based on time series analysis, such as Linear regression based models, nonlinear heuristics based models and other simulation based models.

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Forecasting energy prices in a competitive market

Cited by 131 publications

References 4 publications

Short-term electricity prices forecasting in a competitive market: A neural network approach

Short-term electricity prices forecasting in a competitive market: A neural network approach

Intelligent energy management

A Review of Price Forecasting Problem and Techniques in Deregulated Electricity Markets

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