Forecasting Power Prices Using a Hybrid Fundamental-Econometric Model

González, Virginia; Contreras, Javier; Bunn, Derek W.

doi:10.1109/tpwrs.2011.2167689

Cited by 66 publications

(40 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Approaches to modeling the supply and demand side based on time series and econometrics are developed by different authors, e. g., Eydeland and Wolyniec (2003) or Lyle and Elliott (2009) aim to find prices from derivatives. The following works are comparable to our work because hourly electricity spot prices are modeled: Gonzáles et al (2012) and Karakatsani and Bunn (2010), who forecast electricity spot prices for the British market; Vehviläinen and Pyykkönen (2005), who forecast electricity spot prices in the Nordic market; and Cartea and Villaplana (2008), who forecast electricity spot prices for PJM and the British and the Nordic markets. A recent work using a time series approach including a supply stack model was developed by Liebl (2013).…”

Section: Introductionmentioning

confidence: 77%

Are fundamentals enough? Explaining price variations in the German day-ahead and intraday power market

2016

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Are Fundamentals Enough? Explaining Price Variations in the German Day-Ahead and Intraday Power MarketBy Christian Pape*, Simon Hagemann and Christoph Weber AbstractEuropean electricity market participants are encouraged to balance intraday deviations from their day-ahead schedules via trades in the intraday market. Together with the increasing production of variable renewable energy sources, the intraday market is gaining importance. We investigate the explanatory power of a fundamental modeling approach explicitly accounting for mustrun operations of combined heat and power plants (CHP) and intraday peculiarities such as a shortened intraday supply stack. The fundamental equilibria between every hour's supply stack and aggregated demand in 2012 and 2013 are modeled to yield hourly price estimates. The major benefits of a fundamental modeling approach are the ability to account for non-linearities in the supply stack and the ability to combine time-varying information consistently. The empirical results show that fundamental modeling explains a considerable share of spot price variance. However, differences between the fundamental and actual prices persist and are explored using regression models. The main differences can be attributed to (avoided) start up-costs, market states and trading behavior.

show abstract

Section: Introductionmentioning

confidence: 77%

Are fundamentals enough? Explaining price variations in the German day-ahead and intraday power market

2016

View full text Add to dashboard Cite

show abstract

“…As mentioned in [10,21], this market has features that are difficult to forecast due to influences from dominant players, which are reflected in historical data. The EMP historical data used for the Spanish market date back to the year 2002, allowing a clear and fair comparison with the already published results from other proposed methodologies, considering the same four test weeks of the year 2002, which are consistent with the four seasons.…”

Section: Spanish Market Resultsmentioning

confidence: 99%

Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term

et al. 2016

View full text Add to dashboard Cite

Abstract:The uncertainty and variability in electricity market price (EMP) signals and players' behavior, as well as in renewable power generation, especially wind power, pose considerable challenges. Hence, enhancement of forecasting approaches is required for all electricity market players to deal with the non-stationary and stochastic nature of such time series, making it possible to accurately support their decisions in a competitive environment with lower forecasting error and with an acceptable computational time. As previously published methodologies have shown, hybrid approaches are good candidates to overcome most of the previous concerns about time-series forecasting. In this sense, this paper proposes an enhanced hybrid approach composed of an innovative combination of wavelet transform (WT), differential evolutionary particle swarm optimization (DEEPSO), and an adaptive neuro-fuzzy inference system (ANFIS) to forecast EMP signals in different electricity markets and wind power in Portugal, in the short-term, considering only historical data. Test results are provided by comparing with other reported studies, demonstrating the proficiency of the proposed hybrid approach in a real environment.

show abstract

“…In the past, people have done a large number of experiments on the price forecast in the different fields such as stock market [1][2][3], electricity market [4][5][6][7][8][9][10][11][12], Gold market [13], and so on. The price forecasting models usually include time series model [10,13], Sliding Windows Model (SWM) [9], various NN [1,2,5,7], GM (1, 1) [8][9], support vector machine [11][12], wavelet [5,6], grey system theory [10], fundamental econometric model [4] and fractal theory model [14].…”

Section: Introductionmentioning

confidence: 99%

Price Forecasting for Cell Phone Market Using Adaptive Sliding Window and Adaptive RBF NN

Zhu

Cao

Zhou

et al. 2013

Journal of Algorithms & Computational Technology

View full text Add to dashboard Cite

In order to get the excellent accuracy for price forecast in the cell phone market, a novel improved Sliding Window (SW) model based on adaptive windows width and a novel improved Radial Basis Function (RBF) Neural Network (NN) model based on adaptive spread are proposed and the Disturbance Factors Model (DFM) is used in this paper. All of the three kinds of price forecasting models are utilized to verify the accuracy. The cell phone price is extracted from different websites and used as the model verification data. And the experimental results of the forecasting average accuracy based on the DFM obtain 94.61 percent. The experimental results of the forecasting average accuracy based on the ARBF NN model obtain 97.88 percent. The experimental results of the forecasting average accuracy based on the Adaptive SW Model (ASWM) obtain 99.64 percent. Although the results based on the DFM are not very good, it is still a satisfactory result. Since it is at least not a very serious result which proves that it is worth to do further researches in the field of the cell phone market based on the DFM. The results based on the ASWM and the ARBF NN models are satisfied. The improved methods enhance the forecast accuracy compared to the original model. In the field of the price forecast on the cell phone market, the improved methods have a good performance which is valuable and useful not only for businesses, but also for consumers.Keywords: Price forecasting, cell phone market, disturbance factor, adaptive sliding windows, adaptive RBF neural network * Corresponding author. zqy@hyit.edu.cn INTRODUCTIONAs the development of network technology and the popularity of online stores, recently, there is a growing emphasis on researches of commodity price forecasting methods. The commodity price forecasting method is the basis of market forecast analysis, commodity production and sales decisions. It is an important issue in the field of market forecast which plays a key role in commodity production, sales and many other issues. This issue can be seen as the data processing and data analysis issues based on time series. In the past, people have done a large number of experiments on the price forecast in the different fields such as stock market [1][2][3] [14]. There are a lot of improved methods based on these put forward in their researches: forecasting day-ahead electricity price by modified relief algorithm and hybrid neural network [15]; forecasting electricity price with extreme learning machine and bootstrapping [16]; forecasting day-ahead price of electricity markets by mutual information technique and cascaded neuro-evolutionary algorithm [17]; a hybrid model for day-ahead price forecasting [18]; self-adaptive radial basis function neural network for shortterm electricity price forecasting [19]; forecasting day-ahead price of electricity markets by a new fuzzy neural network [20]; a price forecast method of commercial aircraft based on I-GM(0, N) model [21]; forecasting price using an integrated approach [22], and so o...

show abstract

Forecasting Power Prices Using a Hybrid Fundamental-Econometric Model

Cited by 66 publications

References 15 publications

Are fundamentals enough? Explaining price variations in the German day-ahead and intraday power market

Are fundamentals enough? Explaining price variations in the German day-ahead and intraday power market

Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term

Price Forecasting for Cell Phone Market Using Adaptive Sliding Window and Adaptive RBF NN

Contact Info

Product

Resources

About