What is it about?
This research focuses on predicting the energy consumption of energy-intensive facilities using a type of artificial intelligence called Long Short-Term Memory (LSTM). By learning from past electricity usage data, the model can estimate how much energy a facility will need in the future. Accurate forecasts help managers plan better, reduce costs, and identify ways to use energy more efficiently. The approach is flexible and can be applied to different kinds of industrial settings, making it a valuable tool for improving sustainability in manufacturing and production.
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Why is it important?
Energy use in large industrial facilities is difficult to predict because it depends on many complex and variable factors. This work is important because it shows how artificial intelligence can provide accurate forecasts where traditional methods often fall short. By applying an LSTM model, the study demonstrates a timely and practical solution for improving energy efficiency, reducing costs, and supporting sustainability goals in energy-intensive industries.
Perspectives
From my perspective as a researcher, this study highlights the potential of combining advanced machine learning methods with real industrial challenges. I believe that tools like LSTM models can bridge the gap between academic research and practical applications, giving facility managers more reliable insights. In future work, I see great opportunities to refine these models and extend them to other sectors, further supporting the transition toward smarter and greener energy systems.
Murat Ayaz
Kocaeli Universitesi
Read the Original
This page is a summary of: LSTM-based estimation of energy consumption in energy-intensive facilities, Engineering Research Express, February 2025, Institute of Physics Publishing,
DOI: 10.1088/2631-8695/adb378.
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