Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets

Yamin, H.Y.

doi:10.1016/s0142-0615(04)00042-0

Cited by 22 publications

(32 citation statements)

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“…Recent day-ahead electricity price forecasting was done by (Wang et al 2019b;Yamin et al 2004), while (Nazar et al 2018) studied a hybrid model for simultaneous load and price forecasting. Machine learning algorithms have also been applied to modeling parameters related to grid capacity, e. g. (Staudt et al 2018), who predict re-dispatch measures in the German electricity market with an artificial neural network, or (Fainti et al 2016), who study line overloads applying an ANN which was trained using the Levenberg-Marquardt algorithm.…”

Section: Related Workmentioning

confidence: 99%

Forecasting cross-border power transmission capacities in Central Western Europe using artificial neural networks

et al. 2019

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Flow-based Market Coupling (FBMC) provides welfare gains from cross-border electricity trading by efficiently providing coupling capacity between bidding zones. In the coupled markets of Central Western Europe, common regulations define the FBMC methods, but transmission system operators keep some degrees of freedom in parts of the capacity calculation. Besides, many influencing factors define the flow-based capacity domain, making it difficult to fundamentally model the capacity calculation and to derive reliable forecasts from it. In light of this challenge, the given contribution reports findings from the attempt to model the capacity domain in FBMC by applying Artificial Neural Networks (ANN). As target values, the Maximum Bilateral Exchanges (MAXBEX) have been chosen. Only publicly available data has been used as inputs to make the approach reproducible for any market participant. It is observed that the forecast derived from the ANN yields similar results to a simple carry-forward method for a one-hour forecast, whereas for a longer-term forecast, up to twelve hours ahead, the network outperforms this trivial approach. Nevertheless, the overall low accuracy of the prediction strongly suggests that a more detailed understanding of the structure and evolution of the flow-based capacity domain and its relation to the underlying market and infrastructure characteristics is needed to allow market participants to derive robust forecasts of FMBC parameters.

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Section: Related Workmentioning

confidence: 99%

Forecasting cross-border power transmission capacities in Central Western Europe using artificial neural networks

et al. 2019

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“…For instance, power can't be put away financially and transmission clog may counteract free trade among control regions. In this way, power value development indicates exceptionally extraordinary, really, the best, unpredictability among all items [1]. Individuals in power industry know about burden determining as foreseeing power burden has turned into a significant undertaking for the correct arranging and activity of intensity framework [2].…”

Section: Introductionmentioning

confidence: 99%

Different ANN Models for Short Term Electricity Price Forecasting

Sarada*¹,

Ram²

2019

IJRTE

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In a deregulated electiricity market, price forecasting is gaining demand with application of Artificial Neural Network (ANN). The paper deals with price forecasting with different ANN models.like Back Propagation Neural Network( BPNN), Radial Bias Function Neural Network (RBFNN) and Genectic Algorithm based Neural Network (GANN). A contextual investigation is made with the downloaded data of the day-ahead pool market prices of the California Pool Market using the above four different ANN models and the results are compared.

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“…Some short-term electricity price methods have been proved to be easy to fall into local optimum [1][2], and the prediction accuracy had to be improved; besides, many researches failed to quantify the bidding strategies of power generation enterprises [3][4][5], which affected the shortterm electricity price.…”

Section: Introductionmentioning

confidence: 99%

Period-decoupled Short-term Price Prediction Model Based on Artificial Neural Network and Least Squares-Support Vector Machine Approach Optimized by Bacterial Colony Chemotaxis Algorithm

Song¹,

Yang²,

Zhang³

2015

Proceedings of the 5th International Conference on Computer Engineering and Networks — PoS(CENet2015)

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The short-term price prediction result is an important bidding strategy basis for generating the enterprise consumers. Firstly, the index system for factors affecting the short-term electricity price was constructed and the core influencing factors were selected based on Artificial Neural Network (ANN); then, Bacterial Colony Chemotaxis (BCC) algorithm was built to determine the extra-parameters used in the least squares-support vector machine (LS-SVM) for short-term electricity price forecasting. In order to avoid BCC trapped in the local optimum, a perturbation was applied to the population to extricate itself out of the local optimal and start a new search and optimized BCC algorithm.

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Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets

Cited by 22 publications

References 0 publications

Forecasting cross-border power transmission capacities in Central Western Europe using artificial neural networks

Forecasting cross-border power transmission capacities in Central Western Europe using artificial neural networks

Different ANN Models for Short Term Electricity Price Forecasting

Period-decoupled Short-term Price Prediction Model Based on Artificial Neural Network and Least Squares-Support Vector Machine Approach Optimized by Bacterial Colony Chemotaxis Algorithm

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