Sun’iy neyron tarmoqlar yordamida elektr energiyasi iste’molini prognozlash va energiya taqsimotini matematik optimallashtirish

Forecasting electricity consumption with artificial neural networks and mathematical optimization of energy allocation

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Published: 2026-10-31
DOI: https://doi.org/10.66073/ReFocus-Conf/0910157
Keywords:
artificial neural networks, LSTM, electricity consumption, load forecasting, linear programming, energy allocation, mathematical optimization, forecast uncertainty

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Kalimbetova Rayhan Yusupovna
Karakalpak State University image/svg+xml
Erimbatova Dilnoza Nurulla qizi
Karakalpak State University image/svg+xml

Abstract

This paper proposes an approach to forecasting electricity consumption with an LSTM artificial neural network and optimizing the allocation of energy among sources by linear programming. A mathematical model is formulated that links the hourly demand forecast to the energy balance, the available capacities of the sources and costs. The paper addresses the distinction between forecast accuracy and economic efficiency, chronological data splitting and the treatment of uncertainty. The work is based on theoretical modeling and presents no empirical experiments or verified savings figures. It is recommended that the proposed model be tested on real data and extended with environmental criteria.

Article Details

Issue

2026: Proceedings of the International Scientific and Practical Conference “Contribution of Women Scientists of Uzbekistan to the Development of the Country” (Nukus, October 9–10, 2026)

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Articles
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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Kalimbetova Rayhan Yusupovna, & Erimbatova Dilnoza Nurulla qizi. (2026). Forecasting electricity consumption with artificial neural networks and mathematical optimization of energy allocation. Partner Conferences of the International Scientific Journal Research Focus, 791-794. https://doi.org/10.66073/ReFocus-Conf/0910157

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