Application of Neural Network Technologies for Price Forecasting in the Liberalized Electricity Market

Gerikh, Valentin; Kolosok, Irina; Kurbatsky, Victor; Tomin, Nikita

doi:10.2478/v10144-009-0020-4

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…It is to be noted that the SA algorithm can select a neural network model for which the input parameters will be the most significant. Experimental computations [15] have indicated a rather high accuracy forecasting based on intelligent neural approach in comparison with the regression models, and with traditional neural network forecasting. But these calculations have shown that intelligent model is not very good "at guessing" the peak zones.…”

Section: A Artificial Neural Network Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Model for Short-Term Forecasting in ElectricPower System

Kutbatsky¹,

Sidorov²,

Tomin³

et al. 2011

IJMLC

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The novel hybrid model for forecasting of time series characterized by nonlinear, nonstationary behavior is presented. Experimental studies on synthetic and real data are carried out and discussed. The application of developed approach for forecasting of price situation in the electricity market and for forecasting of the parameters of the expected operation conditions of the electric power system has demonstrated the accuracy improvement using the most common metrics. The system is examined using the benchmark data records from Australian national electricity market.

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Section: A Artificial Neural Network Modelsmentioning

confidence: 99%

“…The short-term EPF In the paper [15], [28] an intelligent approach based on the neural network technologies to EPF for different lead time intervals has been proposed. This approach has demonstrated good results in comparison with traditional neural network forecasting [6].…”

Section: Experimental Studiesmentioning

confidence: 99%

Hybrid Model for Short-Term Forecasting in ElectricPower System

Kutbatsky¹,

Sidorov²,

Tomin³

et al. 2011

IJMLC

Self Cite

View full text Add to dashboard Cite

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“…The short-term forecasting of power system parameters can be carried out both with the aid of classical approaches of dynamic estimation, statistical methods of analysis of time series and regressive models, and with the aid of artificial intelligence. Many techniques have been employed for such purposes, including machine learning techniques -artificial neural networks (ANNs) [12,10], support vector machines (SVMs) [8], random forest models [8,22] and etc. Moreover, time series models, like ARIMA, GARCH models [13,11], Kalman filter-based algorithm [9,30] have also been proven to be effective in the power system parameters forecasting.…”

Section: Introductionmentioning

confidence: 99%

Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning

Kurbatsky¹,

Tomin²,

Spiryaev³

et al. 2014

Preprint

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A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression.Apart from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

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