A Simplified Building Controls Environment with a Reinforcement Learning Application

Dermardiros, Vasken; Bucking, Scott; Athienitis, Andreas

doi:10.26868/25222708.2019.211427

Cited by 4 publications

(12 citation statements)

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“…It is noteworthy that Dermardiros et al [61] and Gouda et al [33] do not compute any KPIs. Rather, the first benchmark is the room temperature and the operation of the heat source profiles, while the second compares the control rule tables.…”

Section: Number Of Computed Kpismentioning

confidence: 99%

“…This is because reducing the buildings' energy demand and the corresponding CO 2 emissions is one of the primary drivers in the development of new controllers. Among the eight contributions that do not present an energy consumption metric, [43,46,59,60] compute an energy cost metric, Dermardiros et al [61] depict the intermittent operation of the heat source, and [33,62] focus on occupants' thermal comfort. Glorennec et al [42] compare the obtained fuzzy logic control rules table with the specified baseline table.…”

Section: Energy Consumption Metricsmentioning

confidence: 99%

“…Fischer et al [57] quantify the backup heater usage as the total operating hours, and the heat pump usage through the mean part load ratio and the numbers of unit toggling. The latter is also adopted in [61]. Both Refs.…”

Section: Other Metricsmentioning

confidence: 99%

“…The P-PI-PID tag is assigned to the contributions that adopt proportional, integral, or derivative controls (e.g., [33,61,63]). As described in Section 2.1, these traditional controls require proper parameter tuning to ensure their high performance.…”

Section: Benchmarking Referencementioning

confidence: 99%

“…This implies that developers use a combination of the three identified methods, applying either the first and the second or the first and the third. It must be noted that multiple authors [18,61,76,91] rely on two traditional controls (e.g., on/off and RBC) to quantify the improvement gained. In 36% of the studies analyzed, the authors compare the tested control to just one established control.…”

Section: Benchmarking Referencementioning

confidence: 99%

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Benchmarking Approaches for Assessing the Performance of Building Control Strategies: A Review

Ceccolini

Sangi

2022

Energies

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In the last few decades, researchers have shown that advanced building controllers can reduce energy consumption without negatively impacting occupants’ wellbeing and help to manage building systems, which are becoming increasingly complex. Nevertheless, the lack of benefit awareness and demonstration projects undermines stakeholders’ trust, justifying the reluctance to approve new controls in the industry. Therefore, it is necessary to support the development of controls through solid arguments testifying to the performance gain that can be achieved. However, the absence of standardized and systematic testing methods limits the generalization of results and the ability to make fair cross-study comparisons. This study presents an overview of the different benchmarking approaches used to assess control performance. Our goal is to highlight trends, limitations, and controversies through analytics to support the definition of best practices, which remains a widely discussed topic in this research area. We aim to focus on simulation-based benchmarking, which is regarded as a promising solution to overcome the time and cost requirements related to field or hardware-in-the-loop testing. We identify and investigate four key steps relating to virtual benchmarking: defining the key performance indicators, specifying the reference control, characterizing the test scenarios, and post-processing the results. This work confirmed the expected heterogeneity, underlined recurrent features with the help of analytics, and recognized limits and open challenges.

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Section: Number Of Computed Kpismentioning

confidence: 99%

Section: Energy Consumption Metricsmentioning

confidence: 99%

Section: Other Metricsmentioning

confidence: 99%

Section: Benchmarking Referencementioning

confidence: 99%

Section: Benchmarking Referencementioning

confidence: 99%

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Benchmarking Approaches for Assessing the Performance of Building Control Strategies: A Review

Ceccolini

Sangi

2022

Energies

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User-centric approach to optimizing thermal comfort in university classrooms: Utilizing computer vision and Q-XGBoost reinforcement learning

Haifeng,

(Cynthia) Hou,

Gou

2024

Energy and Buildings

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A Review of Reinforcement Learning Applications to Control of Heating, Ventilation and Air Conditioning Systems

2022

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Reinforcement learning has emerged as a potentially disruptive technology for control and optimization of HVAC systems. A reinforcement learning agent takes actions, which can be direct HVAC actuator commands or setpoints for control loops in building automation systems. The actions are taken to optimize one or more targets, such as indoor air quality, energy consumption and energy cost. The agent receives feedback from the HVAC systems to quantify how well these targets have been achieved. The feedback is captured by a reward function designed by the developer of the reinforcement learning agent. A few reviews have focused on the reward aspect of reinforcement learning applications for HVAC. However, there is a lack of reviews that assess how the actions of the reinforcement learning agent have been formulated, and how this impacts the possibilities to achieve various optimization targets in single zone or multi-zone buildings. The aim of this review is to identify the action formulations in the literature and to assess how the choice of formulation impacts the level of abstraction at which the HVAC systems are considered. Our methodology involves a search string in the Web of Science database and a list of selection criteria applied to each article in the search results. For each selected article, a three-tier categorization of the selected articles has been performed. Firstly, the applicability of the approach to buildings with one or more zones is considered. Secondly, the articles are categorized by the type of action taken by the agent, such as a binary, discrete or continuous action. Thirdly, the articles are categorized by the aspects of the indoor environment being controlled, namely temperature, humidity or air quality. The main result of the review is this three-tier categorization that reveals the community’s emphasis on specific HVAC applications, as well as the readiness to interface the reinforcement learning solutions to HVAC systems. The article concludes with a discussion of trends in the field as well as challenges that require further research.

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A Simplified Building Controls Environment with a Reinforcement Learning Application

Cited by 4 publications

References 0 publications

Benchmarking Approaches for Assessing the Performance of Building Control Strategies: A Review

Benchmarking Approaches for Assessing the Performance of Building Control Strategies: A Review

User-centric approach to optimizing thermal comfort in university classrooms: Utilizing computer vision and Q-XGBoost reinforcement learning

A Review of Reinforcement Learning Applications to Control of Heating, Ventilation and Air Conditioning Systems

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