Energy Management Strategy for Zone Cooling Load Demand Reduction in Commercial Buildings: A Data-Driven Approach
| Autor: | Lan Lan, Wayes Tushar, Kristin L. Wood, Ngian Chung Wong, Chau Yuen, Clement Lork, Stewart Tai, Thirugnanam Kannan |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
Energy management business.industry 020209 energy Cooling load 0211 other engineering and technologies Thermal comfort Context (language use) 02 engineering and technology Thermal energy storage Industrial and Manufacturing Engineering Setpoint Chiller boiler system Control and Systems Engineering Control theory 021105 building & construction HVAC 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering business |
| Zdroj: | IEEE Transactions on Industry Applications. 55:7281-7299 |
| ISSN: | 1939-9367 0093-9994 |
| DOI: | 10.1109/tia.2019.2930599 |
| Popis: | Optimal scheduling and robust sequence control of a central chiller system with thermal energy storage may not be a straightforward solution to reduce building cooling load demand (CLD) due to unpredictable thermal comfort requirements and the stochastic nature of occupancy-dependent CLD. One potential way to address this challenge is to design and develop an energy management strategy (EMS) for a zone-variable air volume unit. This paper proposes an EMS to reduce CLD by deciding the zone reference setpoint temperature based on fuzzy logic strategy (FLS) and zone thermal models. The objective is to reduce zone CLD while maintaining zone temperature and zone thermal comfort margin within the threshold limits. In this context, first, we modeled the zone thermal behavior using an artificial neural network (ANN) approach to predict zone temperature for next time slot, modeled the zone cooling energy behavior using a regression approach to predict zone CLD for the next time slot, and estimated the zone occupancy status using noise data. Second, we implemented zone thermal feedback model and comfort margin based on zone thermal conditions and predicted mean vote index, respectively. Finally, we implemented an EMS that includes both demand supply strategy and cooling load demand reduction strategy based on one time-slot ahead prediction. The novelty of the proposed EMS lies within the zone CLD reduction using FLS, predicting zone thermal behaviors using an ANN and a regression approach, estimating zone occupancy using noise data, and maintaining zone thermal comfort margin within the threshold limits. Historical data from a commercial building in Singapore are used to demonstrate the effectiveness of the proposed EMS. |
| Databáze: | OpenAIRE |
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