A GARCH Forecasting Model to Predict Day-Ahead Electricity Prices

Garcia, Reinaldo C.; Contreras, Javier; Akkeren, Marco van; Garcia, João

doi:10.1109/tpwrs.2005.846044

Cited by 607 publications

(308 citation statements)

References 15 publications

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“…This approach can be very accurate, but it requires a lot of information, and the computational cost is very high [13]. More recently, generalized autoregressive conditional heteroskedastic -GARCH models [14] and the Wavelet-ARIMA technique [15] have also been proposed.…”

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

“…For the wholesale market participant status an organization must satisfy the requirements set out in the approved resolution of the Government of the Russian )HGHUDWLRQ RI 'HFHPEHU ʋ Rules of the wholesale electricity (capacity) market and in the Treaty of Accession to the trading system of the wholesale market. All subjects of the wholesale market are included in Non-Commercial Partnership "Market Council" [21].…”

Section: Resultsmentioning

confidence: 99%

Modeling prices of wholesale market of electric energy and power by the example of the UPS of the Ural

Mokhov

Demyanenko

2017

SHS Web Conf.

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Abstract. The article oversees forecasting model for deviations of the balancing market index and dayahead market index according to the maximum similarity sample for different levels of approximation in the context of positive and negative time-series value. The model was being tested on the factual data of the Integrated Power system of the Ural, Wholesale market for electricity and power of Russian Federation. Describes the price formation on the day-ahead market and the balancing market index. The necessity to use accurate forecasting methods consumption and prices of electrical energy and power to reduce penalties when the electric power industry entities on the energy exchange. The testing of mathematical models to predict the balancing market index deviations and day-ahead market based on a sample of maximum similarity with certain approximation equations for positive and negative values gave the prediction error of 3.3%.

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“…(2) represents the ARMA(1,1) process while (3) to (4) are the GARCH(1,1) processses. The model is commonly used to forecast short-term electricity prices because it is well suited for market with high volatility (Garcia et al 2005). We choose an order (1,1,1,1), which means that the error and its variability are determined by error at time t − 1.…”

Section: Common Components To All Modelsmentioning

confidence: 99%

Long-term Uncertainty of Hydropower Revenue Due to Climate Change and Electricity Prices

Gaudard

Gabbi²,

Bauder³

et al. 2016

Water Resour Manage

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Hydropower represents the world's largest renewable energy source. As a flexible technology, it enhances reliability and security of the electricity system. However, climate change and market liberalization may hinder investment due to the evolution of water runoffs and electricity prices. Both alter expected revenue and bring uncertainty. It increases risk and deters investment. Our research assesses how climate change and market fluctuation affect annual revenue. But this paper focuses on the uncertainty, rather than on forecasting. This transdisciplinary topic is investigated by means of a mixed method, i.e. both quantitative and qualitative. The quantitative approach uses established models in natural sciences and economics. The uncertainty is accounted for by applying various scenarios and various datasets coming from different models. Based on those results, uncertainty is discussed through an analysis discerning three dimensions of uncertainty. Uncertainty analysis requires the assessment of a large panel of models and data sets. It is therefore rarely carried out. The originality of the paper also lies on the combination of quantitative established models with a qualitative analysis. The results surprisingly show that the greenhouse gas scenarios may in fact represent a low source of uncertainty, unlike electricity prices. Like forecasting, the main uncertainties are actually case study related and depend on the investigated variables. It is also shown that the nature of uncertainty evolves. Runoff uncertainty goes from variability, i.e. inherent randomness, to epistemic, i.e. limitation of science. The reverse situation occurs with the electricity price. The implications for scientists and policy makers are discussed.

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A GARCH Forecasting Model to Predict Day-Ahead Electricity Prices

Cited by 607 publications

References 15 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

Modeling prices of wholesale market of electric energy and power by the example of the UPS of the Ural

Long-term Uncertainty of Hydropower Revenue Due to Climate Change and Electricity Prices

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