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EMUENERGY: Framework para teste de algoritmos de gestão energética de edifícios
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Resumo(s)
A crescente complexidade dos sistemas de gestão energética em edifícios inteligentes tem vindo a
evidenciar a necessidade de plataformas flexíveis que permitam avaliar e validar estratégias de controlo
antes da sua implementação em contexto real. Contudo, as ferramentas atualmente disponíveis
apresentam limitações significativas, nomeadamente a ausência de um ambiente unificado, realista e
de fácil utilização que permita testar diferentes estratégias de controlo energético, comparar cenários
distintos e visualizar os seus impactos energéticos, económicos e de conforto.
Neste contexto, a presente dissertação propõe uma nova framework modular em Python, a
EmuEnergy, para simulação e comparação de algoritmos de controlo energético de edifícios. A framework
integra o motor de simulação EnergyPlus através da sua API Python, permitindo que investigadores
implementem as suas próprias estratégias de controlo e avaliem o respetivo desempenho
através de métricas energéticas, económicas, ambientais e de conforto térmico.
Foram implementados e comparados três algoritmos de controlo com diferentes abordagens, algoritmo
baseado em regras, algoritmo preditivo baseado em modelos e algoritmo baseado em otimização,
aplicados a dois edifícios de referência do Departamento de Energia dos Estados Unidos (DOE).
Os resultados demonstraram poupanças energéticas de até 13,7% e melhorias de conforto térmico de
até 92,5% das horas em conforto, confirmando a aplicabilidade da framework a diferentes tipologias
de edifícios e estratégias de controlo.
The growing complexity of energy management systems in smart buildings has highlighted the need for flexible platforms that enable control strategies to be evaluated and validated before they are implemented in a real-world setting. However, the tools currently available have significant limitations, notably the lack of a unified, realistic and user-friendly environment that allows for the testing of different energy control strategies, the comparison of distinct scenarios, and the visualisation of their energy, economic and comfort impacts. In this context, this thesis proposes the development of a modular framework in Python, EmuEnergy, for the simulation and comparison of building energy control algorithms. The framework integrates the EnergyPlus simulation engine via its Python API, allowing researchers to implement their own control strategies and evaluate their performance using energy, economic, environmental and thermal comfort metrics. Three control algorithms with different approaches were implemented and compared: a rule-based algorithm, a model-based predictive algorithm and an optimisation-based algorithm, applied to two reference buildings from the US Department of Energy (DOE). The results demonstrated energy savings of up to 13.7% and improvements in thermal comfort of up to 92.5% of hours spent in comfort, confirming the framework’s applicability to different building types and control strategies.
The growing complexity of energy management systems in smart buildings has highlighted the need for flexible platforms that enable control strategies to be evaluated and validated before they are implemented in a real-world setting. However, the tools currently available have significant limitations, notably the lack of a unified, realistic and user-friendly environment that allows for the testing of different energy control strategies, the comparison of distinct scenarios, and the visualisation of their energy, economic and comfort impacts. In this context, this thesis proposes the development of a modular framework in Python, EmuEnergy, for the simulation and comparison of building energy control algorithms. The framework integrates the EnergyPlus simulation engine via its Python API, allowing researchers to implement their own control strategies and evaluate their performance using energy, economic, environmental and thermal comfort metrics. Three control algorithms with different approaches were implemented and compared: a rule-based algorithm, a model-based predictive algorithm and an optimisation-based algorithm, applied to two reference buildings from the US Department of Energy (DOE). The results demonstrated energy savings of up to 13.7% and improvements in thermal comfort of up to 92.5% of hours spent in comfort, confirming the framework’s applicability to different building types and control strategies.
Descrição
Palavras-chave
Building Energy Management Smart Buildings Energy Simulation Gestão energética de edifícios Edifícios inteligentes Simulação energética
Contexto Educativo
Citação
Editora
Licença CC
Sem licença CC