Decentralized and Centralized Optimal Control of HVAC Systems

Mapping Intimacies ◽

10.18122/td.1880.boisestate ◽

2021 ◽

Author(s):

Ryan S. Montrose

Keyword(s):

Optimal Control ◽

Power Distribution ◽

Energy Savings ◽

Service Providers ◽

Hvac Systems ◽

Peer Communication ◽

Naturally Occurring ◽

Optimal Control Algorithms ◽

Optimal Controllers ◽

Thermostatically Controlled Loads

Utility service providers are often challenged with the synchronization of thermostatically controlled loads. Load synchronization, resulting from naturally occurring or demand response events, can damage power distribution equipment and reduce the grid's efficiency. Because thermostatically controlled loads constitute most of the power consumed by the grid at any given time, the proper control of such devices can lead to significant energy savings and improved grid stability. The contribution of this thesis is developing optimal control algorithms for both single-stage and variable-speed heat pump HVAC systems. Our control architecture allows for regulating home temperatures through selective peer-to-peer communication while simultaneously minimizing aggregate power consumption and aggregate load volatility. For comparison purposes, various low-level and centralized optimal controllers are explored and compared against their decentralized counterparts.

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Centralized and Decentralized Optimal Control of Variable Speed Heat Pumps

Energies ◽

10.3390/en14134012 ◽

2021 ◽

Vol 14 (13) ◽

pp. 4012

Author(s):

Ryan S. Montrose ◽

John F. Gardner ◽

Aykut C. Satici

Keyword(s):

Optimal Control ◽

Demand Response ◽

Power Distribution ◽

Energy Savings ◽

Service Providers ◽

Heat Pumps ◽

Variable Speed ◽

Peer Communication ◽

Naturally Occurring ◽

Thermostatically Controlled Loads

Utility service providers are often challenged with the synchronization of thermostatically controlled loads. Load synchronization, as a result of naturally occurring and demand-response events, has the potential to damage power distribution equipment. Because thermostatically controlled loads constitute most of the power consumed by the grid at any given time, the proper control of such devices can lead to significant energy savings and improved grid stability. The contribution of this paper is the development of an optimal control algorithm for commonly used variable speed heat pumps. By means of selective peer-to-peer communication, our control architecture allows for the regulation of home temperatures while simultaneously minimizing aggregate power consumption, and aggregate load volatility. An optimal centralized controller is also explored and compared against its decentralized counterpart.

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Data mining approach for improving the optimal control of HVAC systems: An event-driven strategy

Journal of Building Engineering ◽

10.1016/j.jobe.2021.102246 ◽

2021 ◽

Vol 39 ◽

pp. 102246

Author(s):

Junqi Wang ◽

Jin Hou ◽

Jianping Chen ◽

Qiming Fu ◽

Gongsheng Huang

Keyword(s):

Data Mining ◽

Optimal Control ◽

Hvac Systems ◽

Data Mining Approach ◽

Event Driven

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A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Energies ◽

10.3390/en14010156 ◽

2020 ◽

Vol 14 (1) ◽

pp. 156

Author(s):

Paige Wenbin Tien ◽

Shuangyu Wei ◽

John Calautit

Keyword(s):

Deep Learning ◽

Real Time ◽

Energy Demand ◽

Energy Savings ◽

Internal Heat ◽

Electrical Equipment ◽

Building Energy ◽

Hvac Systems ◽

Equipment Usage

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

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Optimal Control for a Hydraulic Recuperation System Using Endoreversible Thermodynamics

Applied Sciences ◽

10.3390/app11115001 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5001

Author(s):

Robin Masser ◽

Karl Heinz Hoffmann

Keyword(s):

Optimal Control ◽

Fuel Consumption ◽

Energy Savings ◽

Combustion Engine ◽

Mechanical Energy ◽

Hydraulic Systems ◽

Commercial Vehicles ◽

Hybrid Drive ◽

Environmental Considerations ◽

Onboard System

Energy savings in the traffic sector are of considerable importance for economic and environmental considerations. Recuperation of mechanical energy in commercial vehicles can contribute to this goal. One promising technology rests on hydraulic systems, in particular for trucks which use such system also for other purposes such as lifting cargo or operating a crane. In this work the potential for energy savings is analyzed for commercial vehicles with tipper bodies, as these already have a hydraulic onboard system. The recuperation system is modeled based on endoreversible thermodynamics, thus providing a framework in which realistic driving data can be incorporated. We further used dissipative engine setups for modeling both the hydraulic and combustion engine of the hybrid drive train in order to include realistic efficiency maps. As a result, reduction in fuel consumption of up to 26% as compared to a simple baseline recuperation strategy can be achieved with an optimized recuperation control.

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