Optimizing Energy Savings on University Campus Using IoT and Automated Planning

• Kassia C. S. Lopes UFC
• Wellington Umbelino UFC
• Ana Lívia Lopes UFC
• Arthur R. da Silva UFC
• Cecília M. C. Mesquita UFC
• Marcus V. Nogueira UFC
• Maria E. S. Marques UFC
• Pedro N. Damacena UFC
• William M. Brito UFC
• Viviane Menezes UFC
• Michel Sales UFC

DOI: https://doi.org/10.5753/eniac.2025.14421

Abstract

Population growth and economic development have driven increased energy consumption. In public buildings, HVAC and lighting systems account for a significant portion of this consumption. The adoption of automation technologies, using Internet of Things (IoT)-based devices, can help reduce energy waste in these types of environments. However, solutions based exclusively on IoT tend to disregard the dynamic nature of spaces and user behavior. To overcome this limitation, Artificial Intelligence-based approaches, such as Automated Planning, have been used to manage these devices and achieve goals such as minimizing energy consumption while maintaining user comfort and safety. In this context, this work proposes the integration of a physical layer composed of sensors and actuators into an automated planning system, enabling devices to self-adapt to specific environmental conditions and needs in real time. A preliminary version of the proposed system was deployed on the Federal University of Ceará Campus in Quixadá (UFC-Quixadá), enabling intelligent monitoring and control of devices in real environments.

References

Aleesha, M. and Laseena, C. A. (2022). Mqtt protocol for resource constrained iot applications: A review. In Proceedings of the International Conference on Systems, Energy and Environment 2022 (ICSEE 2022). Disponível em: [link] ou DOI: 10.2139/ssrn.4299372. Acesso em: 21 mai. 2025.

Amurim, A. D., da Silva, J. I., Ortiz, M. D., Rego, P. A., and de Souza, J. N. (2021). Uma solução de iot baseada no fiware para gerenciamento de recursos energéticos e serviços acadêmicos em um campus universitário. In Workshop de Computação Urbana (CoUrb), pages 265–278. SBC.

Coles, A., Coles, A., Fox, M., and Long, D. (2009). Temporal planning in domains with linear processes. pages 1671–1676.

da Conceição, W. N. E. and de Resende Costa, R. M. (2023). Análise do protocolo mqtt para comunicação iot através de um cenário de comunicação. In Anais da Escola Regional de Redes de Computadores (ERRC), Juiz de Fora, MG, Brasil. SBC - Sociedade Brasileira de Computação. ISSN 2595-5911.

Dias, U., Hell, M., Medeiros, Á., Silveira, D., and de Aguiar, E. (2019). Self-organizing fuzzy rule-based approach for dealing with the classification of indoor environments for iot applications. In Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), pages 1044–1055. SBC.

ENGIE (2024). Anuário estatístico de energia elétrica. Disponível em: [link]. Acesso em: 19 mai. 2025.

Fox, M. and Long, D. (2003). Pddl2.1: An extension to pddl for expressing temporal planning domains. J. Artif. Intell. Res. (JAIR), 20:61–124.

Georgievski, I., Schlatow, J., and Aiello, M. (2023). Ai temporal planning for energy smart buildings. Energy Informatics, 6(1):1–17.

Ghallab, M., Nau, D., and Traverso, P. (2004). Automated Planning: theory and practice. Elsevier.

Haslum, P., Lipovetzky, N., Magazzeni, D., Muise, C., Brachman, R., Rossi, F., and Stone, P. (2019). An introduction to the planning domain definition language, volume 13. Springer.

Ma, J., Bouloukakis, G., Kattepur, A., Yus, R., and Conan, D. (2023a). Demsa: a dt- enabled middleware for self-adaptive smart spaces. In Proceedings of the 1st International Workshop on Middleware for Digital Twin, pages 1–6.

Ma, J., Bouloukakis, G., Kattepur, A., Yus, R., and Conan, D. (2023b). DEMSA: a DT- enabled middleware for self-adaptive smart spaces. In Midd4DT ’23: Proceedings of the 1st International Workshop on Middleware for Digital Twin, pages 1–6, Bologna, Italy. ACM/IFIP.

Mesquita, R. P., Teixeira, E. H., Ribeiro, A. J., Gandolpho, B. D., and Pontes, F. L. F. (2024). Computer vision applied to smart markets: a case study for empty shelf detection. In Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), pages 460–471. SBC.

Rio, L. S. D., Costa, M. R., and Silva, J. A. (2018). Proposta de ambientes inteligentes iot sob a ótica da eficiência energética. In Encontro Anual de Tecnologia da Informação, pages 122–130. SBC.

Rojek, I., Mikołajewski, D., Mroziński, A., Macko, M., Bednarek, T., and Tyburek, K. (2025). Internet of things applications for energy management in buildings using artificial intelligence—a case study. Energies, 18(7):1706.

Russell, S. J. and Norvig, P. (2004). Inteligência artificial. Elsevier.

Universidade Federal do Ceará (2022). Superintendência de infraestrutura e gestão ambiental - Água e energia. Disponível em: [link]. Acesso em: 19 mai. 2025.