Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA Models

Conejo, Antonio J.; Plazas, M.A.; Espínola, Rosa; Molina, Ana I.

doi:10.1109/tpwrs.2005.846054

Cited by 822 publications

(474 citation statements)

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“…Extremely high prices with no assessable reasons are the consequence of bidding strategies, which are confidential. We decided to use only historical price data to forecast the future prices, not only because this selection enables a fair comparison between the ARIMA models in [12,15] and our neural network approach, but also because it reveals a good compromise between accuracy and time consumption.…”

Section: Electricity Prices Forecastingmentioning

confidence: 99%

“…For the sake of a fair comparison, the fourth week of February, May, August, and November are selected, i.e., weeks with particularly good price behaviour are deliberately not chosen. This results in an uneven accuracy distribution throughout the year that reflects reality [15]. For the Californian market, one week of year 2000 has been selected to further assess the validity of the proposed approach.…”

Section: Case Studiesmentioning

confidence: 99%

“…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%

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

Catalão

Mariano

Mendes

et al. 2007

Electric Power Systems Research

356

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: Electricity Prices Forecastingmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

See 1 more Smart Citation

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

Catalão

Mariano

Mendes

et al. 2007

Electric Power Systems Research

356

178

View full text Add to dashboard Cite

show abstract

“…Similarly, a day-ahead electricity price forecasting tool using the wavelet transform and ARIMA models is proposed by [16]. The paper discussed use of the wavelet transform to decompose the historical and usually ill-behaved price series into set of better-behaved constitutive series.…”

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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“…Traditional wind power prediction methods include regression analysis [4], time series [5], Kalman filtering method [6] etc. But the randomness and nonlinear of wind power constrains the application of traditional prediction methods.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Power Prediction Based on Empirical Mode Decomposition and Extreme Learning Machine

Wu¹,

Liu²

2016

Proceedings of the 2016 5th International Conference on Environment, Materials, Chemistry and Power Electronics

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Abstract. Wind power prediction is important for the power system with plenty of wind power. This paper studies the method combined with empirical mode decomposition and extreme learning machine for short-term wind power prediction. The empirical mode decomposition method is utilized to decompose the signal of wind power into sequences with different characteristic scale. The extreme learning machine method is used to model and predict each sequence. Eventually, the prediction results of each sequence are added to obtain the final wind-power prediction results. The simulation result shows that the proposed method in this study improves the prediction accuracy of wind power prediction.

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Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA Models

Cited by 822 publications

References 20 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

Forecasting spot electricity market prices using time series models

Short-Term Wind Power Prediction Based on Empirical Mode Decomposition and Extreme Learning Machine

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