A Framework for Electricity Price Spike Analysis With Advanced Data Mining Methods

Zhao, Jun; Zhang, Dong; Li, Xue; Wong, Kit Po

doi:10.1109/tpwrs.2006.889139

Cited by 146 publications

(106 citation statements)

References 20 publications

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“…If the probability is larger than the threshold, a spike is predicted to occur, regardless of whether this probability is less than the probability of non-spikes. This modification is performed because many spikes occur when their occurrence probabilities are smaller than 50% [17].…”

Section: Probability Thresholdmentioning

confidence: 99%

“…Electricity demand and supply are among the candidate inputs for the compound classifier since the relations of these variables are known to drive the movement in the price spikes to a large extent [17]. Therefore, total electricity generation (i.e., internal supply (sup)) and electricity demand (d) in Finland, both lagged up to 200 h before a forecast hour, are selected.…”

Section: Price Spike Module: Compound Classifiermentioning

confidence: 99%

“…Spikes have been incorporated into a Markov-switching model and diffusion models with the addition of a Poisson jump component [15]. Data mining techniques have been applied to the spike forecasting problem and have achieved promising results [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Price Forecasting in the Day-Ahead Energy Market by an Iterative Method with Separate Normal Price and Price Spike Frameworks

Voronin

Partanen

2013

Energies

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Abstract:A forecasting methodology for prediction of both normal prices and price spikes in the day-ahead energy market is proposed. The method is based on an iterative strategy implemented as a combination of two modules separately applied for normal price and price spike predictions. The normal price module is a mixture of wavelet transform, linear AutoRegressive Integrated Moving Average (ARIMA) and nonlinear neural network models. The probability of a price spike occurrence is produced by a compound classifier in which three single classification techniques are used jointly to make a decision. Combined with the spike value prediction technique, the output from the price spike module aims to provide a comprehensive price spike forecast. The overall electricity price forecast is formed as combined normal price and price spike forecasts. The forecast accuracy of the proposed method is evaluated with real data from the Finnish Nord Pool Spot day-ahead energy market. The proposed method provides significant improvement in both normal price and price spike prediction accuracy compared with some of the most popular forecast techniques applied for case studies of energy markets.

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Section: Probability Thresholdmentioning

confidence: 99%

Section: Price Spike Module: Compound Classifiermentioning

confidence: 99%

See 1 more Smart Citation

Price Forecasting in the Day-Ahead Energy Market by an Iterative Method with Separate Normal Price and Price Spike Frameworks

Voronin

Partanen

2013

Energies

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“…With the aim of dealing with this peculiarity, the authors in [14] proposed a data mining framework based on both support vector machines and probability classifiers.…”

Section: Related Workmentioning

confidence: 99%

Improving Time Series Forecasting by Discovering Frequent Episodes in Sequences

Martínez‐Álvarez

Troncoso

Riquelme

2009

Advances in Intelligent Data Analysis VIII

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Abstract. This work aims to improve an existing time series forecasting algorithm -LBF-by the application of frequent episodes techniques as a complementary step to the model. When real-world time series are forecasted, there exist many samples whose values may be specially unexpected. By the combination of frequent episodes and the LBF algorithm, the new procedure does not make better predictions over these outliers but, on the contrary, it is able to predict the apparition of such atypical samples with a great accuracy. In short, this work shows how to detect the occurrence of anomalous samples in time series improving, thus, the general forecasting scheme. Moreover, this hybrid approach has been successfully tested on electricity-related time series.

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“…Dispatch strategies normally can provide quick solutions to improve the current situation of system operation and reduce carbon emissions dramatically. On the other hand, exploiting renewable energy is another effective way to mitigate energy source deficiency, control GHGs emissions, and achieve smart grid vision [4][5][6]. Wind power being one of the most appealing renewable energy resources has gained widespread concerns during the last decades.…”

Section: Introductionmentioning

confidence: 99%

Sequential quadratic programming particle swarm optimization for wind power system operations considering emissions

Zhang

Yao

et al. 2013

J. Mod. Power Syst. Clean Energy

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In this paper, a computation framework for addressing combined economic and emission dispatch (CEED) problem with valve-point effects as well as stochastic wind power considering unit commitment (UC) using a hybrid approach connecting sequential quadratic programming (SQP) and particle swarm optimization (PSO) is proposed. The CEED problem aims to minimize the scheduling cost and greenhouse gases (GHGs) emission cost. Here the GHGs include carbon dioxide (CO 2 ), nitrogen dioxide (NO 2 ), and sulphur oxides (SO x ). A dispatch model including both thermal generators and wind farms is developed. The probability of stochastic wind power based on the Weibull distribution is included in the CEED model. The model is tested on a standard system involving six thermal units and two wind farms. A set of numerical case studies are reported. The performance of the hybrid computational method is validated by comparing with other solvers on the test system.

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A Framework for Electricity Price Spike Analysis With Advanced Data Mining Methods

Cited by 146 publications

References 20 publications

Price Forecasting in the Day-Ahead Energy Market by an Iterative Method with Separate Normal Price and Price Spike Frameworks

Price Forecasting in the Day-Ahead Energy Market by an Iterative Method with Separate Normal Price and Price Spike Frameworks

Improving Time Series Forecasting by Discovering Frequent Episodes in Sequences

Sequential quadratic programming particle swarm optimization for wind power system operations considering emissions

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