Optimal HVAC System Operation Using Online Learning of Interconnected Neural Networks

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Ye-Eun Jang (Autor) Outra; Young-Jin Kim (Autor) Outra; João P. S. Catalão (Autor) FEUP

Revista

Título: IEEE Transactions on Smart Grid

Vol. 12

Páginas: 3030-3042

ISSN: 1949-3053

Editora: IEEE

Indexação

ISI Web of Knowledge - 8 Citações

Scopus - 12 Citações

Outras Informações

ID Authenticus: P-00T-KFS

DOI: 10.1109/tsg.2021.3051564

Abstract (EN): Optimizing the operation of heating, ventilation, and air-conditioning (HVAC) systems is a challenging task that requires the modeling of complex nonlinear relationships among the HVAC load, indoor temperature, and outdoor environment. This article proposes a new strategy for optimal operation of an HVAC system in a commercial building. The system for indoor temperature control is divided into three sub-systems, each of which is modeled using an artificial neural network (ANN). The ANNs are then interconnected and integrated into an optimization problem for temperature set-point scheduling. The problem is reformulated to determine the optimal set-points using a deterministic search algorithm. After the optimal scheduling has been initiated, the ANNs undergo online learning repeatedly, mitigating overfitting. Case studies are conducted to analyze the performance of the proposed strategy, compared to strategies with a pre-determined temperature set-point, an ideal physics-based building model, and other types of machine learning-based modeling and scheduling methods. The case study results confirm that the proposed strategy is effective in terms of the HVAC energy cost, practical applicability, and training data requirements.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13