A Cognitive System Architecture for Building Energy Management

Zucker, Gerhard; Sporr, Andreas; Kollmann, Stefan; Wendt, Alexander; Chaido, Lydia Siafara; Fernbach, Andreas

doi:10.1109/tii.2018.2815739

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…Cho et al [13] discussed the data gathering techniques by use of IoT and developed a hybrid Machine Learning approach which can be used in industries for predictive analysis. Various intelligent automation is built using the architecture, strategy, and technologies discussed above, Gerhard Zucker et al [14] developed an intelligent system for energy management of a building by using knowledge base and explained the methodology and algorithms which are used to implement the logic. They applied the proposed model on building to automate the operations of HVAC operations.…”

Section: Related Workmentioning

confidence: 99%

Building Cognitive Intelligence In Conveyor Systems using Intermediary Anomaly Detection And Handling (IADH) Technique

Poosapati*,

Manda,

Katneni

2019

IJITEE

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Industry 4.0 is characterized by the interconnection of industrial systems and automation to enable efficient and autonomous industrial operations. Automating the tasks done by humans involves processing a huge volume of data across multiple sources in the industry and incorporating intelligence into the machine from the insights extracted from the processed data. Classification techniques play a vital role in extracting the features and predicting the best possible action that can be taken based on the processed data. However in cases where the underlying business rules changes, the algorithms fail to detect these changes early, thereby impacting the overall accuracy of the model. In this paper, we presented the Intermediary Anomaly Detection and Handling (IADH) algorithm to overcome the problem mentioned above. IADH algorithm will help to quickly identify the changing business rules of the industry and alter the prediction of the model. The architecture of this model does not restrict to one specific industrial machine but enables it to be reusable across multiple industrial systems. The details of the test data collected, algorithm steps, prototype built and software modules built to develop the product with the IADH feature are discussed in this paper. The results of the model with IADH and without IADH are compared to notice the improvements of the proposed IADH Technique for the collected dataset..

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Section: Related Workmentioning

confidence: 99%

Building Cognitive Intelligence In Conveyor Systems using Intermediary Anomaly Detection And Handling (IADH) Technique

Poosapati*,

Manda,

Katneni

2019

IJITEE

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“…Optimizing EMS and DR at the building level relates to a BEMS framework developed for optimally operating the building facilities to reach the targets of energy efficiency and/or DR. Some intelligent optimization approaches, such as knowledgebased [46] and machine learning [47], are introduced for the BEMS of a building.…”

Section: Optimization At Building Levelmentioning

confidence: 99%

“…As an example, Zucker et al [46] introduce a novel concept of cognitive system architecture for BEMS. The cognitive system optimizes the control strategies in building system at both the automation level (i.e., logic conditions, scheduling, etc.)…”

Section: Optimization At Building Levelmentioning

confidence: 99%

Building energy management and demand response

Zhang¹

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I would like to take this opportunity to convey my appreciation to several individuals who helped me in preparation and completion of this study. First and foremost, I would like to convey my sincere gratitude to my supervisor, Associate Professor So Ping Lam, who gave me the chance to undertake this research works. His professional attitude, guidance, encouragement, and support deserve the special acknowledgment. I am extremely lucky to have a supervisor who are always patient on helping me to revise the draft papers, and who keep encouraging me and promptly responding my questions and queries when I encountered difficulties in the study. His effort on supervising and consulting students is surprising and ever-inspiring. His timely assistance and support is invaluable to the author. Without him, the completion of this study would not be a success and smooth sail journey. I would also like to express my appreciation to my previous supervisor, Assistant Professor Abhisek Ukil, who provide the opportunity to me on pursuing MEng degree in Nanyang Technological University (NTU), Singapore. His expertise and passion on research motivate me pursing the research study. My grateful thanks are also extended to Associate Professor Gooi Hoay Beng and Associate Professor Anutosh Chakraborty for being in my thesis advisory committee (TAC) members and providing valuable advises at each stage of my research. I would also like to extend my thanks to School of Electrical and Electronic Engineering for giving me an opportunity to pursue my MEng study. I am also thankful to Ms. Chia Nge Tak Heng and Ms. Lin Zhiren for their technical assistance. Last but not least, I am also thankful to all my colleagues in the Clean Energy Research Laboratory (CERL) for creating the pleasant working environment. vii Contents Statement of Originality iii Supervisor Declaration Statement iv Authorship Attribution Statement v Authorship Attribution Statement vi Acknowledgments vii

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“…In particular, the current trends in BEMS for modern buildings are contemplating the embedding of advanced monitoring systems capable of observing the environment and operating conditions, potentially permitting an intelligent management system to understand their state and rapidly focus on correcting and preventing energy inefficient behaviors. Consequently, further research is being carried out to develop data processing algorithms capable of extracting augmented information from large historical databases existing in current BEMS with the objective of extracting knowledge to support the creation of higher-level controllers [32].…”

Section: Research Problemmentioning

confidence: 99%

Advanced energy management strategies for HVAC systems in smart buildings

Sala Cardoso

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The efficacy of the energy management systems at dealing with energy consumption in buildings has been a topic with a growing interest in recent years due to the ever-increasing global energy demand and the large percentage of energy being currently used by buildings. The scale of this sector has attracted research effort with the objective of uncovering potential improvement avenues and materializing them with the help of recent technological advances that could be exploited to lower the energetic footprint of buildings. Specifically, in the area of heating, ventilating and air conditioning installations, the availability of large amounts of historical data in building management software suites makes possible the study of how resource-efficient these systems really are when entrusted with ensuring occupant comfort. Actually, recent reports have shown that there is a gap between the ideal operating performance and the performance achieved in practice. Accordingly, this thesis considers the research of novel energy management strategies for heating, ventilating and air conditioning installations in buildings, aimed at narrowing the performance gap by employing data-driven methods to increase their context awareness, allowing management systems to steer the operation towards higher efficiency. This includes the advancement of modeling methodologies capable of extracting actionable knowledge from historical building behavior databases, through load forecasting and equipment operational performance estimation supporting the identification of a building’s context and energetic needs, and the development of a generalizable multi-objective optimization strategy aimed at meeting these needs while minimizing the consumption of energy. The experimental results obtained from the implementation of the developed methodologies show a significant potential for increasing energy efficiency of heating, ventilating and air conditioning systems while being sufficiently generic to support their usage in different installations having diverse equipment. In conclusion, a complete analysis and actuation framework was developed, implemented and validated by means of an experimental database acquired from a pilot plant during the research period of this thesis. The obtained results demonstrate the efficacy of the proposed standalone contributions, and as a whole represent a suitable solution for helping to increase the performance of heating, ventilating and air conditioning installations without affecting the comfort of their occupants. L’eficàcia dels sistemes de gestió d’energia per afrontar el consum d’energia en edificis és un tema que ha rebut un interès en augment durant els darrers anys a causa de la creixent demanda global d’energia i del gran percentatge d’energia que n’utilitzen actualment els edificis. L’escala d’aquest sector ha atret l'atenció de nombrosa investigació amb l’objectiu de descobrir possibles vies de millora i materialitzar-les amb l’ajuda de recents avenços tecnològics que es podrien aprofitar per disminuir les necessitats energètiques dels edificis. Concretament, en l’àrea d’instal·lacions de calefacció, ventilació i climatització, la disponibilitat de grans bases de dades històriques als sistemes de gestió d’edificis fa possible l’estudi de com d'eficients són realment aquests sistemes quan s’encarreguen d'assegurar el confort dels seus ocupants. En realitat, informes recents indiquen que hi ha una diferència entre el rendiment operatiu ideal i el rendiment generalment assolit a la pràctica. En conseqüència, aquesta tesi considera la investigació de noves estratègies de gestió de l’energia per a instal·lacions de calefacció, ventilació i climatització en edificis, destinades a reduir la diferència de rendiment mitjançant l’ús de mètodes basats en dades per tal d'augmentar el seu coneixement contextual, permetent als sistemes de gestió dirigir l’operació cap a zones de treball amb un rendiment superior. Això inclou tant l’avanç de metodologies de modelat capaces d’extreure coneixement de bases de dades de comportaments històrics d’edificis a través de la previsió de càrregues de consum i l’estimació del rendiment operatiu dels equips que recolzin la identificació del context operatiu i de les necessitats energètiques d’un edifici, tant com del desenvolupament d’una estratègia d’optimització multi-objectiu generalitzable per tal de minimitzar el consum d’energia mentre es satisfan aquestes necessitats energètiques. Els resultats experimentals obtinguts a partir de la implementació de les metodologies desenvolupades mostren un potencial important per augmentar l'eficiència energètica dels sistemes de climatització, mentre que són prou genèrics com per permetre el seu ús en diferents instal·lacions i suportant equips diversos. En conclusió, durant aquesta tesi es va desenvolupar, implementar i validar un marc d’anàlisi i actuació complet mitjançant una base de dades experimental adquirida en una planta pilot durant el període d’investigació de la tesi. Els resultats obtinguts demostren l’eficàcia de les contribucions de manera individual i, en conjunt, representen una solució idònia per ajudar a augmentar el rendiment de les instal·lacions de climatització sense afectar el confort dels seus ocupants

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A Cognitive System Architecture for Building Energy Management

Cited by 10 publications

References 24 publications

Building Cognitive Intelligence In Conveyor Systems using Intermediary Anomaly Detection And Handling (IADH) Technique

Building Cognitive Intelligence In Conveyor Systems using Intermediary Anomaly Detection And Handling (IADH) Technique

Building energy management and demand response

Advanced energy management strategies for HVAC systems in smart buildings

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