Proceedings ArticleDOI
Development of Methods for Optimizing Reactive Power Modes Based on Neural Network Technologies
TLDR
A program has been developed that implements prediction algorithms using neural networks, as well as optimizing the reactive power mode, to reduce electric power consumption, and losses in electrical networks.Abstract:
The high cost of electric power, as well as the considerable length and branching of electrical networks, necessitate reduce electric power consumption, and losses in electrical networks. One of solutions of this problem is optimizing the reactive power mode. Reducing the reactive power factor at the point of common coupling (PCC) to the economic level established by the power system is not taking into account that in a complex network, power flows with a non-optimal arrangement of compensating devices and improper determination of their power can reach large values, that resulting in an increase in losses in the network. A program has been developed that implements prediction algorithms using neural networks, as well as optimizing the reactive power mode.read more
Citations
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Теорія ймовірності і математична статистика (укр.)Probability theory and mathematical statistics (en.)
О. В. Прищепа,O. V. Pryshchepa +1 more
Book ChapterDOI
Statistical methods of air pollution forecasting
TL;DR: In this article, the correlations examined in chapter 5 are often performed with the aid of multiple-parameter linear regression methods, which have been used for developing practical methods of urban air pollution forecasting.
Journal ArticleDOI
Methodology Calculation for Reactive Power Compensation in Industrial Enterprises
TL;DR: Proposed method takes into account real cost of power equipment, which can be used in reactive power compensation system at designed enterprise and provides choice of option that meets technical requirements of regulatory documents and has a minimum annual cost.
Proceedings ArticleDOI
Forecasting of Electricity Generation by Solar Panels Using Neural Networks with Incomplete Initial Data
TL;DR: The creation of the solution based on neural networks that allows forecasting energy production using solar panels with high accuracy on the basis of conventional meteorological data is created.
References
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Book
Neural Networks And Learning Machines
TL;DR: Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together.
Book
Optimization of Power System Operation
TL;DR: The author did not name the algorithm, but it is likely to be VAR Optimization by Evolutionary Algorithm, which is a very simple and straightforward way to go about solving the problem of how to Optimize Power Dispatch.
Journal ArticleDOI
Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines
TL;DR: The results indicate that the proposed LS-SVM model is an accurate and a quick prediction method for electricity energy consumption of Turkey.