Machine Learning for Optimizing Energy Consumption in Industry

Machine Learning for Optimizing Energy Consumption in Industry

A. E. Bajmagambetow

72,27 €
IVA incluido
Disponible
Editorial:
KS OmniScriptum Publishing
Año de edición:
2026
ISBN:
9786630407815
72,27 €
IVA incluido
Disponible

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Sobre IBD

This book is devoted to the research and development of machine learning methods for forecasting and optimizing energy consumption at industrial facilities. The study is based on real-world data on daily electricity consumption at Qarmet mines for the period 2014-2025. A comparative analysis of RNN, LSTM, GRU, and Transformer models was conducted for short-term time series forecasting. Based on the experimental results, the Transformer model with a 30-day observation window and forward multi-step forecasting was found to be the most effective. Based on the forecasts, a method was developed to smooth peak loads while maintaining the total volume of energy consumption. A software package was implemented to provide forecasting, optimization, and visualization of results. The proposed approach can be used as a decision-support tool to improve energy efficiency and plan energy consumption regimes for industrial enterprises.

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