Price forecasting in deregulated electricity markets - a bibliographical survey

Daneshi, H.; Daneshi, Ali

doi:10.1109/drpt.2008.4523487

Cited by 14 publications

(7 citation statements)

References 75 publications

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“…Nevertheless, understanding of the price dynamics (and the resulting increased predictability) is paramount for all market participants and regulators, for the purpose of planning, trading, risk management or alternatively market design (see e.g. Daneshi and Daneshi, 2008).…”

Section: Introductionmentioning

confidence: 99%

On the market impact of wind energy forecasts

2010

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Section: Introductionmentioning

confidence: 99%

On the market impact of wind energy forecasts

2010

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“…2. As the literature does not agree on an unambiguous allocation to a model category, this illustration (and subsequent model descriptions) is based on the categories of Niimura ( 2006), Haghi and Tafreshi (2007), Daneshi and Daneshi (2008) 4. A comprehensive overview is given by Bollerslev (2009) and Teräsvirta (2009).…”

Section: Performance Of Time Series Modelsmentioning

confidence: 99%

“…Haghi and Tafreshi (2007) classify 40 models from 1998 to 2006 and use categories game-theoretic methods, simulation methods and statistical methods, in which time series models are either stationary or non-stationary. Daneshi and Daneshi (2008) conduct a bibliographical survey on electricity price forecasting techniques from 1993 to 2007 and classify over 100 publications as time series, NN types or other forecast techniques.…”

Section: Introductionmentioning

confidence: 99%

Forecasting performance of time series models on electricity spot markets: a quasi-meta-analysis

Gürtler

Paulsen

2017

IJESM

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Purpose Empirical publications on the time series modeling and forecasting of electricity prices vary widely regarding the conditions, and the findings make it difficult to generalize results. Against this background, it is surprising that there is a lack of statistics-based literature reviews on the forecasting performance when comparing different models. The purpose of the present study is to fill this gap. Design/methodology/approach The authors conduct a comprehensive literature analysis from 2000 to 2015, covering 86 empirical studies on the time series modeling and forecasting of electricity spot prices. Various statistics are presented to characterize the empirical literature on electricity spot price modeling, and the forecasting performance of several model types and modifications is analyzed. The key issue of this study is to offer a comparison between different model types and modeling conditions regarding their forecasting performance, which is referred to as a quasi-meta-analysis, i.e. the analysis of analyses to achieve more general findings independent of the circumstances of single studies. Findings The authors find evidence that generalized autoregressive conditional heteroscedasticity models outperform their autoregressive–moving-average counterparts and that the consideration of explanatory variables improves forecasts. Originality/value To the best knowledge of the authors, this paper is the first to apply the methodology of meta-analyses in a literature review of the empirical forecasting literature on electricity spot markets.

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“…This tool is normally based on single-round auctions [1], and considers the hours of the market horizon one at a time. A single-round auction is performed every hour to determine the resulting market clearing price in that hour and also the accepted production and consumption bids.…”

Section: Introductionmentioning

confidence: 99%