Challenges and Solutions in Managing a Real-Time Database of Monitored Buildings

Autor(es)

Terada, Lucas Zenichi ; Cortez, Juan Carlos ; Volotao, Levi Santos ; Soares, Joao ; Rider, Marcos J. ; Vale, Zita

• Tipo de documento: Artigo Científico
• Fonte: 2023 15TH SEMINAR ON POWER ELECTRONICS AND CONTROL, SEPOC; v. N/A, p. 6-pg., 2023-01-01.

Resumo

Real databases that store building detection data pose significant challenges, including handling real-time data generated by remote sensing systems and effectively analyzing large volumes of Internet of Things (IoT) sensor data. Proposed solutions involve scalable databases and detection information models, while ensuring data quality and addressing measurement errors are crucial considerations. This paper explores the management of a real database containing building detection data, focusing on real-time data acquired from remote sensing systems and IoT sensors in smart buildings. The study investigates the application of advanced data management techniques and detection information models to address scalability, fault tolerance, and consistency issues in the database. It highlights the importance of utilizing local energy consumption and PV generation data for effective energy management strategies. The research presents methodologies that incorporates easily applicable algorithms and heuristics to evaluate and manipulate real datasets from monitored buildings, enabling accurate day-ahead predictions. Additionally, machine learning algorithms are employed for forecasting purposes. Through a compelling test case, the effectiveness of the proposed methodology is demonstrated, showcasing its potential to overcome database challenges and provide valuable insights for smarter building control systems and efficient energy management. (AU)

• Processo FAPESP: 21/11380-5 - CPTEn - Centro Paulista de Estudos da Transição Energética
• Beneficiário: Luiz Carlos Pereira da Silva
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Ciência para o Desenvolvimento
• Processo FAPESP: 22/09171-1 - Desenvolvimento de modelo preditivo para recarga inteligente de veículos elétricos baseado em dados na nuvem
• Beneficiário: Lucas Zenichi Terada
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Mestrado
• Processo FAPESP: 22/13957-0 - Gerenciamento da Energia Elétrica num Eletroposto de Recarga com Geração Intermitente e Armazenamento de Energia
• Beneficiário: Levi Santos Volotão
• Modalidade de apoio: Bolsas no Brasil - Iniciação Científica
• Processo FAPESP: 20/13002-5 - Algoritmo de Recarga Inteligente de Veículos Elétricos Considerando a Integração de Recursos Elétricos Distribuídos: Microsserviço para Plataformas de Eletromobilidade IoT
• Beneficiário: Lucas Zenichi Terada
• Modalidade de apoio: Bolsas no Brasil - Mestrado