Common Hybrid Agent Platform -- Sustaining the Collective

Serban, Radu; Guo, Hongliang; Salden, Alfons H.

doi:10.1109/snpd.2012.110

Cited by 6 publications

(2 citation statements)

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“…It studied the metro station energy saving problem from the modeling and controlling aspect. Multi-agent models were proposed for the complex interactions of energy consumption in the underground subways (Serban et al, 2012). Researchers in the project have also proposed adaptive and predictive control schemes for controlling ventilation subsystems to save energy (Giretti et al, 2012).…”

Section: Related Workmentioning

confidence: 99%

Monitoring and autonomous control of Beijing Subway HVAC system for energy sustainability

Wang

Feng

2017

Energy for Sustainable Development

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Section: Related Workmentioning

confidence: 99%

Monitoring and autonomous control of Beijing Subway HVAC system for energy sustainability

Wang

Feng

2017

Energy for Sustainable Development

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“…It studies the metro station energy saving mainly from the modeling and controlling aspect. Multi-agent and hybrid models were proposed to model the complex interactions of energy consumption in the underground subways [12], [11]. Adaptive and predictive control schemes were also proposed for controlling ventilation subsystems to save energy [7].…”

Section: Related Workmentioning

confidence: 99%

Sense, model and identify the load signatures of HVAC systems in metro stations

Wang

Feng

2014

2014 IEEE PES General Meeting | Conference &Amp; Exposition

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The Heating Ventilation and Air Conditioning (HVAC) systems in subway stations are energy consuming giants, each of which may consume over 10, 000 Kilowatts per day for cooling and ventilation. To save energy for the HVAC systems, it is critically important to firstly know the "load signatures" of the HVAC system, i.e., the quantity of heat imported from the outdoor environments and by the passengers respectively as a function of time, which will significantly benefit the control policies. In this paper, we present a novel sensing and learning approach to identify the load signature of the HVAC system in the subway stations. In particular, sensors and smart meters were deployed to monitor the indoor, outdoor temperatures, and the energy consumptions of the HVAC system in real-time. The number of passengers was counted by the ticket checking system. At the same time, the cooling supply provided by the HVAC system was inferred via the energy consumption logs of the HVAC system. Since the indoor temperature variations are driven by the difference of the loads and the cooling supply, linear regression model was proposed for the load signature, whose coefficients are derived via a proposed algorithm . We collected real sensing data and energy log data from HaiDianHuangZhuang Subway station from the duration of July. 2012 to Sept. 2012. The data was used to evaluate the coefficients of the regression model. The experiment validated the presented load signature, which may provide important contexts for smart control policies.

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