The Method of Short-term Forecast Electricity Load with Combined a Sinusoidal Function and an Artificial Neural Network

Gritsay, A. S.; Makarov, V. V.; Хамитов, Р. Н.; Tatevosyan, A. A.; Gritsay, Sergey N.

doi:10.1109/eiconrus.2019.8656796

Cited by 3 publications

(2 citation statements)

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“…Data especially for training data, if the production plan cannot be guaranteed to be regular, will lead to predictions interfered with by different production plans, resulting in increased error. Gritsay proposed a short-term power load forecasting method combining sinusoidal function and artificial neural network [14]. The artificial neural network is used to calculate the approximate coefficient to complete the load forecasting.…”

Section: Introductionmentioning

confidence: 99%

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Industrial Ultra-Short-Term Load Forecasting With Data Completion

Jiang

Wang

et al. 2020

IEEE Access

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Accurate and efficient ultra-short-term load forecasting is crucial for industrial power users to have stabilized and optimized operations. In this paper, we develop novel strategies for industrial power users to handle their challenges in ultra-short-term load forecasting. Firstly, this paper proposes a two-way Genetic Algorithm Back Propagation Neural Networks (GABPNN) missing data completion model to handle data loss, which is common power load data mining. A particle swarm optimization-supporting vector regression (PSO-SVR) algorithm is further used to integrate the two-way completion results with better accuracy. In addition, the paper introduces a combined ultra-short-term load forecasting model for industrial power users. The proposed model combines the Cubature Kalman filter (CKF) prediction model with good performance in nonlinear dynamic systems and the least square support vector machine (LS-SVM) prediction model with good performance in small-scale data prediction. The grey neural network is used to integrate the two algorithms, which further improves the accuracy of ultra-short-term load forecasting. Lastly, we test the proposed strategy in case study with real industrial data and demonstrate that the proposed model has a high degree of precision in load forecasting. INDEX TERMS Cubature Kalman filter; Least squares support vector machine; Grey neural network; PSO-SVR; Combined model; Clonal immune algorithm Haoyu Jiang received the bachelor degree from the

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Section: Introductionmentioning

confidence: 99%

“…The method is convenient and fast and is suitable for small loads. As for medium and long term-load forecasting, the time series in the forecasting process is relatively stable compared with the ultra-short-term, so the method of PSO-LSSVM is usually used [14].…”

Section: Introductionmentioning

confidence: 99%

Industrial Ultra-Short-Term Load Forecasting With Data Completion

Jiang

Wang

et al. 2020

IEEE Access

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Methodology formation of the training sample short-term forecasting electricity load for an energy supply company using data mining technologies

Khomutov

Хамитов

Gritsay

et al. 2021

J. Phys.: Conf. Ser.

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For effective management of the power system operation mode the predictive information about hourly electrical load of all consumers is required. Forecasting errors, as a rule, lead to a decline of the technological and economic indicators of the power system operation, due to unreasonable changes of the generating equipment operating mode, as well as the selection of a non-optimal scheme of electrical networks. This article is devoted to improving the accuracy of short-term load forecasting of delivery points cluster of energy sales company with the use artificial neural networks. One of the most important conditions for achieving high prediction accuracy is the quality of the data sample required for training and testing neural network algorithms for short-term loading forecast. The proposed methodology is based on the authors’ analysis uses the factors influencing the hourly power loading. The proposed methodology is based on the authors’ analysis uses the factors influencing the hourly power loading as well as methods for improving the convergence of learning algorithms for artificial neural networks.

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