Real-world implementation and cost of a cloud-based MPC retrofit for HVAC control systems in commercial buildings

Bird, Max; Daveau, Camille; O’Dwyer, Edward; Acha, Salvador; Shah, Nilay

doi:10.1016/j.enbuild.2022.112269

Cited by 36 publications

(12 citation statements)

References 23 publications

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“…This study entails the development of an integrated information system that leverages contemporary software architecture, namely microservices architecture, which has been implemented in various types of systems, including land administration systems, 40 greenhouse systems, 41 aquaculture value chain traceability systems, 42 energy management systems, [43][44][45][46][47] smart distribution transformer area, 48 control system, 49 groundwater management system, 50 smart water usage, 51 F I G U R E A5 IoT application UI screenshots.…”

Section: Discussionmentioning

confidence: 99%

“…This study entails the development of an integrated information system that leverages contemporary software architecture, namely microservices architecture, which has been implemented in various types of systems, including land administration systems, 40 greenhouse systems, 41 aquaculture value chain traceability systems, 42 energy management systems, 43–47 smart distribution transformer area, 48 control system, 49 groundwater management system, 50 smart water usage, 51 by utilizing the semantic web and internet of things (IoT) technologies. The designed Hazelnut Classification System (HDS) addresses the categorization of 18 Turkish hazelnut cultivars, offering comprehensive information tailored to diverse stakeholder needs.…”

Section: Discussionmentioning

confidence: 99%

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Microservices‐based databank for Turkish hazelnut cultivars using IoT and semantic web technologies

Aydin,

Aldara

2024

Concurrency and Computation

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SummaryInformation and communication technologies (ICTs) can play a crucial role in facilitating access to comprehensive information on the quality standards of Turkish hazelnut cultivars. In this regard, this study introduces a Hazelnut Databank System (HDS) that utilizes the microservices architecture, an integrated software system supported by the Internet of Things (IoT) and semantic web, to categorize Turkish hazelnut cultivars. The study focuses on developing microservices using various programming languages and frameworks. Specifically, C# on the .NET Core Framework was used for both microservices and the web‐based application implemented through the ASP.NET Core MVC Framework. Mobile‐based software applications were created using Xamarin. Forms, and the IoT application was developed using the Python programming language. The data storage is facilitated through the MS SQL Server database. Additionally, the study incorporates the implementation of a hazelnut species classification system using the DNN + ResNet50 machine learning model, achieving an impressive accuracy rate of 95.77%. The overall usability of the system was evaluated, resulting in a score of 42 out of 50. By providing detailed information on Turkish hazelnut cultivars, the HDS has the potential to greatly improve hazelnut production quality in Turkey and increase awareness of hazelnut agriculture among relevant stakeholders.

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Section: Discussionmentioning

confidence: 99%

“…This study entails the development of an integrated information system that leverages contemporary software architecture, namely microservices architecture, which has been implemented in various types of systems, including land administration systems, 40 greenhouse systems, 41 aquaculture value chain traceability systems, 42 energy management systems, 43–47 smart distribution transformer area, 48 control system, 49 groundwater management system, 50 smart water usage, 51 by utilizing the semantic web and internet of things (IoT) technologies. The designed Hazelnut Classification System (HDS) addresses the categorization of 18 Turkish hazelnut cultivars, offering comprehensive information tailored to diverse stakeholder needs.…”

Section: Discussionmentioning

confidence: 99%

Microservices‐based databank for Turkish hazelnut cultivars using IoT and semantic web technologies

Aydin,

Aldara

2024

Concurrency and Computation

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“…Furthermore, even when real-world testing is carried out, specific data collection and control implementation details are often limited to certain building types [108]. Against this backdrop and motivation, Bird et al [109] developed a cloud-based monitoring platform for optimizing HVAC systems in commercial buildings. This platform includes a universal MPC framework for building HVAC systems, which can be applied to all modern building management systems at minimal upfront and Fig.…”

Section: Advanced Control Applications 421 Controls For Energy-saving...mentioning

confidence: 99%

Advanced controls on energy reliability, flexibility and occupant-centric control for smart and energy-efficient buildings

Liu

Zhang

Sun

et al. 2023

Energy and Buildings

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“…Max Bird et al proposed HVAC control based on a model predictive control (MPC) algorithm. Results show the proposed approach can achieve an improvement of 1.7% cost saving while ensuring thermal comfort compared to the baseline [19]. Rui Tang et al achieved a 19-22% energy reduction by modulating the demand power of a building's chillers to improve indoor thermal comfort [20].…”

Section: Building Energy System Managementmentioning

confidence: 99%

Performance Assessment and Comparative Analysis of Photovoltaic-Battery System Scheduling in an Existing Zero-Energy House Based on Reinforcement Learning Control

et al. 2023

Energies

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The development of distributed renewable energy resources and smart energy management are efficient approaches to decarbonizing building energy systems. Reinforcement learning (RL) is a data-driven control algorithm that trains a large amount of data to learn control policy. However, this learning process generally presents low learning efficiency using real-world stochastic data. To address this challenge, this study proposes a model-based RL approach to optimize the operation of existing zero-energy houses considering PV generation consumption and energy costs. The model-based approach takes advantage of the inner understanding of the system dynamics; this knowledge improves the learning efficiency. A reward function is designed considering the physical constraints of battery storage, photovoltaic (PV) production feed-in profit, and energy cost. Measured data of a zero-energy house are used to train and test the proposed RL agent control, including Q-learning, deep Q network (DQN), and deep deterministic policy gradient (DDPG) agents. The results show that the proposed RL agents can achieve fast convergence during the training process. In comparison with the rule-based strategy, test cases verify the cost-effectiveness performances of proposed RL approaches in scheduling operations of the hybrid energy system under different scenarios. The comparative analysis of test periods shows that the DQN agent presents better energy cost-saving performances than Q-learning while the Q-learning agent presents more flexible action control of the battery with the fluctuation of real-time electricity prices. The DDPG algorithm can achieve the highest PV self-consumption ratio, 49.4%, and the self-sufficiency ratio reaches 36.7%. The DDPG algorithm outperforms rule-based operation by 7.2% for energy cost during test periods.

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Real-world implementation and cost of a cloud-based MPC retrofit for HVAC control systems in commercial buildings

Cited by 36 publications

References 23 publications

Microservices‐based databank for Turkish hazelnut cultivars using IoT and semantic web technologies

Microservices‐based databank for Turkish hazelnut cultivars using IoT and semantic web technologies

Advanced controls on energy reliability, flexibility and occupant-centric control for smart and energy-efficient buildings

Performance Assessment and Comparative Analysis of Photovoltaic-Battery System Scheduling in an Existing Zero-Energy House Based on Reinforcement Learning Control

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