Machine Learning Models for Energy Optimization and Resource Consumption in Smart Factories

Hamed Nozari (), Agnieszka Szmelter-Jarosz () and Sepideh Samadi ()
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Hamed Nozari: Bio10
Agnieszka Szmelter-Jarosz: University of Gdańsk
Sepideh Samadi: Edinburgh Business School Heriot-Watt University

A chapter in Artificial Intelligence of Everything and Sustainable Development, 2025, pp 175-189 from Springer

Abstract: Abstract The integration of artificial intelligence (AI) in energy optimization has fundamentally transformed smart factory operations, driving improvements in efficiency, cost reduction, and sustainability. This research examines AI-driven energy management strategies, focusing on key technologies such as machine learning algorithms, predictive analytics, reinforcement learning, IoT-enabled monitoring, and digital twin technology. By leveraging AI, factories can analyze real-time data, optimize energy consumption, and proactively identify inefficiencies, enabling a more intelligent and adaptive approach to industrial energy management. Predictive analytics plays a critical role in energy demand forecasting, allowing factories to anticipate usage patterns and adjust consumption accordingly. IoT-based monitoring systems provide real-time insights into energy utilization, enhancing the ability to make data-driven decisions that reduce waste and improve operational efficiency. Reinforcement learning models autonomously regulate energy allocation, continuously learning and adapting to dynamic production environments. Meanwhile, digital twins create virtual simulations that allow manufacturers to test and refine energy optimization strategies before implementation, ensuring maximum efficiency with minimal risk. Despite its transformative potential, AI-powered energy management faces several challenges. Data quality issues can limit the accuracy of AI-driven predictions, while high implementation costs may deter widespread adoption. Cybersecurity risks pose significant concerns, as connected systems require robust protection against cyber threats. The integration of AI with legacy manufacturing systems further complicates deployment, necessitating interoperable solutions and cost-effective AI adoption strategies. Addressing these challenges requires enhanced data infrastructure, advanced cybersecurity protocols, and scalable AI solutions tailored to industrial settings. This study highlights both the innovations and challenges of AI-driven energy optimization, offering insights into its growing role in the future of smart manufacturing. The findings emphasize the need for continued advancements in AI, data analytics, and industrial automation to develop sustainable, intelligent, and energy-efficient industrial ecosystems capable of meeting the evolving demands of Industry 4.0 and beyond.

Keywords: AI-based energy optimization; Machine learning; Predictive analytics; Smart factories; IoT-enabled monitoring (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-981-96-7202-8_10

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