Stochastic multifactor modeling of spot electricity prices

Hayfavi, Azize; Talasli, Irem

doi:10.1016/j.cam.2013.10.008

Cited by 23 publications

(21 citation statements)

References 12 publications

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“…Electricity prices differ from all the other assets and even commodities due to its' unique features such as requirement of having constant balance between the supply and demand sides, demand inelasticity, oligopolistic generation side, and non-storability [6]. These features cause to some important characteristics of the electricity prices: High volatility, sharp price spikes, mean reverting process, and seasonality in different frequencies [7]. Due to all these idiosyncratic features and characteristics, forecasting the electricity prices accurately becomes a very challenging task.…”

Section: Introductionmentioning

confidence: 99%

Electricity Price Forecasting Using Recurrent Neural Networks

Uğurlu¹,

Öksüz²,

Taş³

2018

Preprint

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Accurate electricity price forecasting has become a substantial requirement since the liberalization of the electricity markets. Due to the challenging nature of the electricity prices, which includes high volatility, sharp price spikes and seasonality, various types of electricity price forecasting models still compete and can not outperform each other consistently. Neural Networks have been successfully used in machine learning problems and Recurrent Neural Networks (RNNs) have been proposed to address time-dependent learning problems. In particular, Long Short Term Memory and Gated Recurrent Units (GRU) are tailor-made for time series price estimation. In this paper, we propose to use Gated Recurrent Units as a new technique for electricity price forecasting. We have trained a variety of algorithms with rolling 3-year window and compared the results with the RNNs. In our experiments, 3-layered GRUs outperformed all other neural network structures and state of the art statistical techniques in a statistically significant manner in the Turkish day-ahead market.

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Section: Introductionmentioning

confidence: 99%

Electricity Price Forecasting Using Recurrent Neural Networks

Uğurlu¹,

Öksüz²,

Taş³

2018

Preprint

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“…Although there are some works [5,[30][31][32] on electricity price forecasting in the Turkish electricity market, the financial effect of electricity price forecasts' inaccuracy has not been investigated. Due to the nature of the Turkish market, with many zeros similar to the Spanish market [33], and an increasing renewable share similar to the German market [13], the Turkish market needs investigation, and can give some insight about the other Southeast Europe markets [34] in addition to Spanish and German markets.…”

Section: Turkish Marketmentioning

confidence: 99%

The Financial Effect of the Electricity Price Forecasts’ Inaccuracy on a Hydro-Based Generation Company

et al. 2018

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Electricity price forecasting has a paramount effect on generation companies (GenCos) due to the scheduling of the electricity generation scheme according to electricity price forecasts. Inaccurate electricity price forecasts could cause important loss of profits to the suppliers. In this paper, the financial effect of inaccurate electricity price forecasts on a hydro-based GenCo is examined. Electricity price forecasts of five individual and four hybrid forecast models and the ex-post actual prices are used to schedule the hydro-based GenCo using Mixed Integer Linear Programming (MILP). The financial effect measures of profit loss, Economic Loss Index (ELI) and Price Forecast Disadvantage Index (PFDI), as well as Mean Absolute Error (MAE) of the models are used for comparison of the data from 24 weeks of the year. According to the results, a hybrid model, 50% Artificial Neural Network (ANN)–50% Long Short Term Memory (LSTM), has the best performance in terms of financial effect. Furthermore, the forecast performance evaluation methods, such as Mean Absolute Error (MAE), are not necessarily coherent with inaccurate electricity price forecasts’ financial effect measures.

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“…To represent the different jump structures in temperature in the form of a single jump and a series of jumps, and are defined as fast and slow mean-reverting OU processes driven by compound Poisson processes with intensities of and , and and being mean-reversion parameters, respectively. Hayfavi and Talasli [22] use a somewhat similar mean-reverting jump process combination in their model of spot electricity prices.…”

Section: Modelmentioning

confidence: 99%

“…Now, the inversion will be applied tô( )0 (− ), wherê ( ) is defined in (22) and 0 is the characteristic function defined in (15). Let temperature in (9) be defined in shorthand notation as = * + Λ , where * is defined as in (16) and Λ = − ∫ 0 .…”

Section: Measuring Hddmentioning

confidence: 99%

Modeling Temperature and Pricing Weather Derivatives Based on Temperature

Tastan

Hayfavi

2017

Advances in Meteorology

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This study first proposes a temperature model to calculate the temperature indices upon which temperature-based derivatives are written. The model is designed as a mean-reverting process driven by a Levy process to represent jumps and other features of temperature. Temperature indices are mainly measured as deviations from a base temperature, and, hence, the proposed model includes jumps because they may constitute an important part of this deviation for some locations. The estimated value of a temperature index and its distribution in this model apply an inversion formula to the temperature model. Second, this study develops a pricing process over calculated index values, which returns a customized price for temperature-based derivatives considering that temperature has unique effects on every economic entity. This personalized price is also used to reveal the trading behavior of a hypothesized entity in a temperature-based derivative trade with profit maximization as the objective. Thus, this study presents a new method that does not need to evaluate the risk-aversion behavior of any economic entity.

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Stochastic multifactor modeling of spot electricity prices

Cited by 23 publications

References 12 publications

Electricity Price Forecasting Using Recurrent Neural Networks

Electricity Price Forecasting Using Recurrent Neural Networks

The Financial Effect of the Electricity Price Forecasts’ Inaccuracy on a Hydro-Based Generation Company

Modeling Temperature and Pricing Weather Derivatives Based on Temperature

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