To realize building energy conservation, appropriate operation of building energy systems is necessary. A chilled water pump, an essential component for chilled water transportation in building cooling systems, consumes substantial energy. Hence, its operation should be optimized. Previous studies on optimal pump control mostly focused on pump speed/frequency control, while the control of pump running number is usually too passive to realize energy-saving objectives. Moreover, existing relevant studies have some disadvantages, such as (1) too complex a workflow for maintenance; (2) dependence on accurate system performance models that take substantial data and labor to establish; and (3) high requirements on monitoring and sensors. To tackle those problems, this article proposes a simple, feasible approach to optimize the running number (on/off status) of chilled water pumps for building energy conservation. The proposed method is merely based on similarity/affinity laws and pump performance curves feasible for engineering practices. It has been implemented on a real cooling system in a battery factory. Our results suggest that: (1) based on similarity/affinity laws and pump performance curves, the estimation of potential targeted pump working points is accurate enough for optimal control (the flow rate estimation error is less than 2%, the frequency estimation error is less than 1 Hz); (2) the energy-saving effect of this control method is evident (20% of pump energy is saved by the proposed method compared to the former control logic); (3) the water grid operation condition is maintained well: cooling supply is not sacrificed by the control intervention (compared to the working condition before the intervention, grid pressure difference changed by 1.4%, flow rate changed by 2.6%). Regarding the low preconditions, simple workflow, and acceptable energy-saving performance of the proposed method, it is suitable for energy conservation in building cooling systems.
As a space for daily life, the community directly affects residents’ lives and has a significant impact on residents’ health. Integrating the concept of health into community construction can promote comprehensive and full-cycle health protection. This study explored the potential contribution of the DGNB system to community health and well-being and collected residents’ perceptions. A community assessment model was established to analyze how the community environment would affect residents’ health. The results show that compared with other community evaluation systems, the DGNB system has a more balanced weight and more comprehensive content, covering many factors that influence physical health, mental health, and social health. Residents pay more attention to personal safety, lifestyle, physical environment, community service, and management, which are related to their well-being and health. The assessment model is helpful to improve the community healthy environment and residents’ life quality.
To conserve building energy, optimal operation of a building’s energy systems, especially heating, ventilation and air-conditioning (HVAC) systems, is important. This study focuses on the optimization of the central chiller plant, which accounts for a large portion of the HVAC system’s energy consumption. Classic optimal control methods for central chiller plants are mostly based on system performance models which takes much effort and cost to establish. In addition, inevitable model error could cause control risk to the applied system. To mitigate the model dependency of HVAC optimal control, reinforcement learning (RL) algorithms have been drawing attention in the HVAC control domain due to its model-free feature. Currently, the RL-based optimization of central chiller plants faces several challenges: (1) existing model-free control methods based on RL typically adopt single-agent scheme, which brings high training cost and long training period when optimizing multiple controllable variables for large-scaled systems; (2) multi-agent scheme could overcome the former problem, but it also requires a proper coordination mechanism to harmonize the potential conflicts among all involved RL agents; (3) previous agent coordination frameworks (identified by distributed control or decentralized control) are mainly designed for model-based control methods instead of model-free controllers. To tackle the problems above, this article proposes a multi-agent, model-free optimal control approach for central chiller plants. This approach utilizes game theory and the RL algorithm SARSA for agent coordination and learning, respectively. A data-driven system model is set up using measured field data of a real HVAC system for simulation. The simulation case study results suggest that the energy saving performance (both short- and long-term) of the proposed approach (over 10% in a cooling season compared to the rule-based baseline controller) is close to the classic multi-agent reinforcement learning (MARL) algorithm WoLF-PHC; moreover, the proposed approach’s nature of few pending parameters makes it more feasible and robust for engineering practices than the WoLF-PHC algorithm.
This paper examines the impact of financial market development, financial crises and deposit insurance on bank risk based on macro data of 86 countries during the period 1998-2014.The results show that banking sector development and stock market development have *The authors would like to thank the editor and anonymous reviewers for their valuable comments and helpful suggestions.
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