Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models

Weron, Rafał; Misiorek, Adam

doi:10.1016/j.ijforecast.2008.08.004

Cited by 317 publications

(242 citation statements)

References 31 publications

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“…We will measure the models' ability to offer a point forecast in this way too. As by Weron and Misiorek [21], we will compare performance by looking at the MAE averaged over the week. Weron and Misiorek [21] calculate a quasi mean average percentage error (MAPE) by introducing a weighed MAE.…”

Section: Point Vs Interval Forecastmentioning

confidence: 99%

“…Weron and Misiorek [21] offer a good survey of day-ahead forecast methods and benchmark a range of the time series methods against each other. Other articles that benchmarks day-ahead forecasting methods include the work of Conejo et al [4], who compare three different time series models, neural network and wavelet models for the PJM Interconnection day-ahead price, and that of Nogales et al [16], who compare two different time series models for the Spanish and the Californian day-ahead market prices.…”

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confidence: 99%

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Benchmarking time series based forecasting models for electricity balancing market prices

2013

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In the trade-off between bidding in the day-ahead electricity market and the real time balancing market, producers need good forecasts for balancing market prices to make informed decisions. A range of earlier published models for forecasting of balancing market prices, including a few extensions, is benchmarked. The models are benchmarked both for one hour-ahead and dayahead forecast, and both point and interval forecasts are compared. None of the benchmarked models produce informative day-ahead point forecasts, suggesting that information available before the closing of the day-ahead market is efficiently reflected in the day-ahead market price rather than the balancing market price. Evaluation of the interval forecasts reveals that models without balancing state information overestimate variance, making them unsuitable for scenario generation.

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Section: Point Vs Interval Forecastmentioning

confidence: 99%

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confidence: 99%

Benchmarking time series based forecasting models for electricity balancing market prices

2013

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“…In [19], simple time series models with and without exogenous variables, ARMAX and ARMA process, is studied where the system load has been taken as the only exogenous variable. In this paper, as each hour displays a distinct price profile reflecting the daily variation of demand, costs, and operational constraints, separately modeling for each hour of…”

Section: It Is Reported That Test Results On the Electricity Market Omentioning

confidence: 99%

“…This leads to a total of 24 models a day. In [19] a separate modeling for each hour of the week (leading to a total of 168 different models) has been discussed and concluded being unsatisfactory and time consuming. In this paper, however, the same hourly models are assumed to be used for every day of the week.…”

Section: Forecasting Spot Electricity Market Prices Using Time Seriesmentioning

confidence: 99%

Forecasting spot electricity market prices using time series models

Mazengia

Tuấn

2008

2008 IEEE International Conference on Sustainable Energy Technologies

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The worldwide electricity industry is in an era where an overwhelming transition towards deregulation is taking place. Since its start in the early 1980s, the industry has been in a continuous change to a different atmosphere; the ultimate benefit being providing the enduse customer with a reliable but yet cheaper electricity supply. In the old monopolistic system, utilities were the only authoritative body to set the tariff based on their aggregate cost. In the contrary, as a newly emerging structure, deregulation has come-up with a new way of functioning; leading generation, transmission and distribution to be independent activities. This market is generally a customer driven market and since the early days of deregulation, price forecasting has become an important task to all the market players engaged. Unless each body is fairly aware of the market, it might result in losses to a generating company or inadequate supply to the system; resulting in a huge crisis. Hence, understanding the market behavior is of vital importance for the well-functioning of the industry and the benefit of each party.This thesis addresses the importance of electricity price forecasting in the deregulated market. After a comprehensive review of previous works in the subject, it is concluded that forecast accuracy varies depending on the forecast method used and the electricity market under study. In this thesis multiple linear regression approach are proposed to predict next day's electricity prices. The developed models are tested in the Nord Pool and the Ontario electricity markets and satisfactory results are achieved. Comparing the forecast results from the two markets, results in the Nord Pool market are significantly more accurate than the Ontario market. This arises from the fact that Ontario market is very volatile and its market prices are hardly predictable. The forecast results in this thesis work show that the proposed models perform very well with the absence of volatility. For instance, the developed models for the first week of December 2007 generated results with weekly Mean Absolute Percentage Error (MAPE) of about 5.04%. In early spring even better results are achieved where the models generated results with a daily MAPE of up to as low as 1.83% and weekly MAPE of about 2.96%. On the other hand, during summer where prices vary considerably even between hours of the same day, the models generated a relatively poor forecast. A day with a price of as low as 2.96 EURO/MWh to as high as 23.52 EURO/MWh is observed and this price fluctuation contributed for poor forecast. For instance, the models generated forecasts with a weekly MAPE of about 13% for the period from August 29 to Sept. 4, 2007. For the Ontario market, these models generated forecast results with a weekly MAPE of about 17%.

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“…The adequacy of MRS models for forecasting in general has been questioned by Bessec and Bouabdallah (2005). However, as Weron and Misiorek (2008) have shown, regime-switching models may behave better than their linear competitors in volatile periods. They might also have an edge in density forecasts, but this has to be verified yet.…”

Section: Discussionmentioning

confidence: 99%

Goodness-of-fit testing for the marginal distribution of regime-switching models with an application to electricity spot prices

Janczura

Weron

2012

AStA Adv Stat Anal

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This paper complements a recently published study (Janczura and Weron in AStA-Adv Stat Anal 96(3):385-407, 2012) on efficient estimation of Markov regime-switching models. Here, we propose a new goodness-of-fit testing scheme for the marginal distribution of such models. We consider models with an observable (like threshold autoregressions) as well as a latent state process (like Markov regimeswitching). The test is based on the Kolmogorov-Smirnov supremum-distance statistic and the concept of the weighted empirical distribution function. The motivation for this research comes from a recent stream of literature in energy economics concerning electricity spot price models. While the existence of distinct regimes in such data is generally unquestionable (due to the supply stack structure), the actual goodness-offit of the models requires statistical validation. We illustrate the proposed scheme by testing whether commonly used Markov regime-switching models fit deseasonalized electricity prices from the NEPOOL (US) day-ahead market.

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Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models

Cited by 317 publications

References 31 publications

Benchmarking time series based forecasting models for electricity balancing market prices

Benchmarking time series based forecasting models for electricity balancing market prices

Forecasting spot electricity market prices using time series models

Goodness-of-fit testing for the marginal distribution of regime-switching models with an application to electricity spot prices

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