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Internet of Things Applications for Energy Management in Buildings Using Artificial Intelligence—A Case Study

Author

Listed:
  • Izabela Rojek

    (Faculty of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland)

  • Dariusz Mikołajewski

    (Faculty of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland)

  • Adam Mroziński

    (Faculty of Mechanical Engineering, Bydgoszcz University of Science and Technology, Kaliskiego 7, 85-796 Bydgoszcz, Poland)

  • Marek Macko

    (Faculty of Mechatronics, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland)

  • Tomasz Bednarek

    (Faculty of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland)

  • Krzysztof Tyburek

    (Faculty of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland)

Abstract

IoT applications for building energy management, enhanced by artificial intelligence (AI), have the potential to transform how energy is consumed, monitored, and optimized, especially in distributed energy systems. By using IoT sensors and smart meters, buildings can collect real-time data on energy usage patterns, occupancy, temperature, and lighting conditions.AI algorithms then analyze this data to identify inefficiencies, predict energy demand, and suggest or automate adjustments to optimize energy use. Integrating renewable energy sources, such as solar panels and wind turbines, into distributed systems uses IoT-based monitoring to ensure maximum efficiency in energy generation and use. These systems also enable dynamic energy pricing and load balancing, allowing buildings to participate in smart grids by storing or selling excess energy.AI-based predictive maintenance ensures that renewable energy systems, such as inverters and batteries, operate efficiently, minimizing downtime. The case studies show how IoT and AI are driving sustainable development by reducing energy consumption and carbon footprints in residential, commercial, and industrial buildings. Blockchain and IoT can further secure transactions and data in distributed systems, increasing trust, sustainability, and scalability. The combination of IoT, AI, and renewable energy sources is in line with global energy trends, promoting decentralized and greener energy systems. The case study highlights that adopting IoT and AI for energy management offers not only environmental benefits but also economic benefits, such as cost savings and energy independence. The best achieved accuracy was 0.8179 (RMSE 0.01). The overall effectiveness rating was 9/10; thus, AI-based IoT solutions are a feasible, cost-effective, and sustainable approach to office energy management.

Suggested Citation

  • Izabela Rojek & Dariusz Mikołajewski & Adam Mroziński & Marek Macko & Tomasz Bednarek & Krzysztof Tyburek, 2025. "Internet of Things Applications for Energy Management in Buildings Using Artificial Intelligence—A Case Study," Energies, MDPI, vol. 18(7), pages 1-28, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1706-:d:1623199
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    References listed on IDEAS

    as
    1. Tamara Gajić & Marko D. Petrović & Ana Milanović Pešić & Momčilo Conić & Nemanja Gligorijević, 2024. "Innovative Approaches in Hotel Management: Integrating Artificial Intelligence (AI) and the Internet of Things (IoT) to Enhance Operational Efficiency and Sustainability," Sustainability, MDPI, vol. 16(17), pages 1-24, August.
    2. Hyung-Chul Jo & Hyang-A Park & Soon-Young Kwon & Kyeong-Hee Cho, 2024. "Home Energy Management Systems (HEMSs) with Optimal Energy Management of Home Appliances Using IoT," Energies, MDPI, vol. 17(12), pages 1-15, June.
    3. Giuseppe Piras & Sofia Agostinelli & Francesco Muzi, 2024. "Digital Twin Framework for Built Environment: A Review of Key Enablers," Energies, MDPI, vol. 17(2), pages 1-27, January.
    4. Izabela Rojek & Dariusz Mikołajewski & Krzysztof Galas & Adrianna Piszcz, 2025. "Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities," Energies, MDPI, vol. 18(2), pages 1-19, January.
    5. Izabela Rojek & Dariusz Mikołajewski & Adam Mroziński & Marek Macko, 2023. "Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage," Energies, MDPI, vol. 16(18), pages 1-26, September.
    6. Habib Sadri & Ibrahim Yitmen & Lavinia Chiara Tagliabue & Florian Westphal & Algan Tezel & Afshin Taheri & Goran Sibenik, 2023. "Integration of Blockchain and Digital Twins in the Smart Built Environment Adopting Disruptive Technologies—A Systematic Review," Sustainability, MDPI, vol. 15(4), pages 1-46, February.
    7. Si Ying Tan & Araz Taeihagh, 2020. "Smart City Governance in Developing Countries: A Systematic Literature Review," Sustainability, MDPI, vol. 12(3), pages 1-29, January.
    8. Mangirdas Morkūnas & Yufei Wang & Jinzhao Wei, 2024. "Role of AI and IoT in Advancing Renewable Energy Use in Agriculture," Energies, MDPI, vol. 17(23), pages 1-20, November.
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