Optimizing legacy building operation: The evolution into data-driven predictive cyber-physical systems

Schmidt, Mischa; Moreno, María Ángeles Esteban; Schülke, Anett; Macek, Karel; Mařík, Karel; Pastor, Alfonso Gordaliza

doi:10.1016/j.enbuild.2017.05.002

Cited by 36 publications

(11 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Luo et al [ 45 ] surveyed the many platforms available in the literature for energy management system in smart buildings [ 46 , 47 , 48 , 49 , 50 ] and presented the development of an IoT-based platform that would produce day-ahead prediction of building energy demands. The predictive model would be based on the hybrid of k-means clustering and an artificial neural network.…”

Section: Case Studies Of the Iot Applied To Building Energy Systemmentioning

confidence: 99%

“…The system was found to improve cooling capacity by 14%, coefficient of performance by 46.3%, and allow for better microclimate control within the building (Figure 11). Luo et al [45] surveyed the many platforms available in the literature for energy management system in smart buildings [46][47][48][49][50] and presented the development of an IoTbased platform that would produce day-ahead prediction of building energy demands. The predictive model would be based on the hybrid of k-means clustering and an artificial neural network.…”

Section: Predictive Temperature Controlmentioning

confidence: 99%

See 1 more Smart Citation

Recent Advances in Internet of Things (IoT) Infrastructures for Building Energy Systems: A Review

Yaïci

Krishnamurthy²,

Entchev

et al. 2021

Sensors

View full text Add to dashboard Cite

This paper summarises a literature review on the applications of Internet of Things (IoT) with the aim of enhancing building energy use and reducing greenhouse gas emissions (GHGs). A detailed assessment of contemporary practical reviews and works was conducted to understand how different IoT systems and technologies are being developed to increase energy efficiencies in both residential and commercial buildings. Most of the reviewed works were invariably related to the dilemma of efficient heating systems in buildings. Several features of the central components of IoT, namely, the hardware and software needed for building controls, are analysed. Common design factors across the many IoT systems comprise the selection of sensors and actuators and their powering techniques, control strategies for collecting information and activating appliances, monitoring of actual data to forecast prospect energy consumption and communication methods amongst IoT components. Some building energy applications using IoT are provided. It was found that each application presented has the potential for significant energy reduction and user comfort improvement. This is confirmed in two case studies summarised, which report the energy savings resulting from implementing IoT systems. Results revealed that a few elements are user-specific then need all be considered in the decision processes. Last, based on the studies reviewed, a few aspects of prospective research were recommended.

show abstract

Section: Case Studies Of the Iot Applied To Building Energy Systemmentioning

confidence: 99%

Section: Predictive Temperature Controlmentioning

confidence: 99%

Recent Advances in Internet of Things (IoT) Infrastructures for Building Energy Systems: A Review

Yaïci

Krishnamurthy²,

Entchev

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Several studies addressed the type of buildings differentiating their use, like residential, commercial, office buildings or education facilities [21], limiting the generalization capabilities of these methods. Old buildings with their special requirements [20] have been treated by retrofitting the HVAC systems, but very few have addressed the control system for improving their performance and efficiency, like the case of a museum that requires an environment for the pictures conservation as an unavoidable physical constraint [18]. Other proposals bring useful metaphors for treating the model behavior, like considering an HVAC system as a cyber-physical system [20] because of its ''integration of computation and physical processes''.…”

Section: A Hvac Systemsmentioning

confidence: 99%

Autonomic Management Architecture for Multi-HVAC Systems in Smart Buildings

Aguilar¹,

Garcés-Jiménez²,

Gallego-Salvador³

et al. 2019

IEEE Access

View full text Add to dashboard Cite

This article proposes a self-managing architecture for multi-HVAC systems in buildings, based on the ''Autonomous Cycle of Data Analysis Tasks'' concept. A multi-HVAC system can be plainly seen as a set of HVAC subsystems, made up of heat pumps, chillers, cooling towers or boilers, among others. Our approach is used for improving the energy consumption, as well as to maintain the indoor comfort, and maximize the equipment performance, by means of identifying and selecting of a possible multi-HVAC system operational mode. The multi-HVAC system operational modes are the different combinations of the HVAC subsystems. The proposed architecture relies on a set of data analysis tasks that exploit the data gathered from the system and the environment to autonomously manage the multi-HVAC system. Some of these tasks analyze the data to obtain the optimal operational mode in a given moment, while others control the active HVAC subsystems. The proposed model is based on standard standard HVAC mathematical models, that are adapted on the fly to the contextual data sensed from the environment. Finally, two case studies, one with heterogeneous and another with homogeneous HVAC equipment, show the generality of the proposed autonomous management architecture for multi-HVAC systems.

show abstract

“…Machine learning applications can support to generate a digital twin. In order to use automatic control systems like energy-cyber-physical systems [14], system knowledge of building energy systems (BES) and their technical building equipment (TBE) must be acquired. If this knowledge is not digitally processable in existing buildings, it must be gained in a personnel-intensive way.…”

Section: Introductionmentioning

confidence: 99%

Automatic digital twin data model generation of building energy systems from piping and instrumentation diagrams

Stinner,

Wiecek,

Baranski

et al. 2021

Preprint

View full text Add to dashboard Cite

Buildings directly and indirectly emit a large share of current CO2 emissions. There is a high potential for CO2 savings through modern control methods in building automation systems (BAS) like model predictive control (MPC). For a proper control, MPC needs mathematical models to predict the future behavior of the controlled system. For this purpose, digital twins of the building can be used. However, with current methods in existing buildings, a digital twin set up is usually laborintensive. Especially connecting the different components of the technical system to an overall digital twin of the building is time-consuming. Piping and instrument diagrams (P&ID) can provide the needed information, but it is necessary to extract the information and provide it in a standardized format to process it further. In this work, we present an approach to recognize symbols and connections of P&ID from buildings in a completely automated way. There are various standards for graphical representation of symbols in P&ID of building energy systems. Therefore, we use different data sources and standards to generate a holistic training data set. We apply algorithms for symbol recognition, line recognition and derivation of connections to the data sets. Furthermore, the result is exported to a format that provides semantics of building energy systems. The symbol recognition, line recognition and connection recognition show good results with an average precision of 93.7%, which can be used in further processes like control generation, (distributed) model predictive control or fault detection. Nevertheless, the approach needs further research.

show abstract

Optimizing legacy building operation: The evolution into data-driven predictive cyber-physical systems

Cited by 36 publications

References 24 publications

Recent Advances in Internet of Things (IoT) Infrastructures for Building Energy Systems: A Review

Recent Advances in Internet of Things (IoT) Infrastructures for Building Energy Systems: A Review

Autonomic Management Architecture for Multi-HVAC Systems in Smart Buildings

Automatic digital twin data model generation of building energy systems from piping and instrumentation diagrams

Contact Info

Product

Resources

About