Open AccessMethodology formation of the training sample short-term forecasting electricity load for an energy supply company using data mining technologies
Author(s) -
Stanislav Khomutov,
Р. Н. Хамитов,
A. S. Gritsay,
Nikolay Serebryakov
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1901/1/012070
Subject(s) - artificial neural network , term (time) , sample (material) , computer science , electric power system , electricity , demand forecasting , electric power industry , convergence (economics) , power (physics) , mains electricity , electric power , mode (computer interface) , industrial engineering , reliability engineering , data mining , operations research , machine learning , engineering , voltage , chemistry , physics , chromatography , quantum mechanics , economic growth , electrical engineering , economics , operating system
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.