Optimal HVAC System Operation Using Online Learning of Interconnected Neural Networks
| Autor: | Young-Jin Kim, Joao P. S. Catalao, Ye-Eun Jang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Optimization problem
Temperature control General Computer Science Artificial neural network business.industry 020209 energy Scheduling (production processes) Building model Control engineering 02 engineering and technology Overfitting Search algorithm HVAC 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business |
| Zdroj: | IEEE Transactions on Smart Grid. 12:3030-3042 |
| ISSN: | 1949-3061 1949-3053 |
| DOI: | 10.1109/tsg.2021.3051564 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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