scholarly journals Ensemble Learning Model-Based Test Workbench for the Optimization of Building Energy Performance and Occupant Comfort

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 96075-96087
Author(s):  
Sanjeev Kumar T.M. ◽  
Ciji Pearl Kurian ◽  
Susan G. Varghese
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sidney Newton ◽  
Arezoo Shirazi ◽  
Pernille Christensen

PurposeTo achieve the building and property by 2050, decarbonisation goals will now require a significant increase in the rate of improvement in the energy performance of buildings. Occupant behaviour is crucial. This study seeks to guide the application of smart building technology in existing building stock to support improved building energy performance and occupant comfort.Design/methodology/approachThis study follows a logical partitioning approach to the development of a schema for building energy performance and occupant comfort. A review of the literature is presented to identify the characteristics that label and structure the problem elements. A smart building technology framework is overlaid on the schema. The framework is then applied to configure and demonstrate an actual technology implementation for existing building stock.FindingsThe developed schema represents the key components and relationships of building energy performance when combined with occupant comfort. This schema provides a basis for the definition of a smart building technologies framework for existing building stock. The study demonstrates a viable configuration of available smart building technologies that couple building energy performance with occupant comfort in the existing building stock. Technical limitations (such as relatively simple building management control regimes) and pragmatic limitations (such as change management issues) are noted for consideration.Originality/valueThis is the first development of a schema to represent how building energy performance can be coupled with occupant comfort in existing building stock using smart building technologies. The demonstration study applies one of many possible technology configurations currently available, and promotes the use of open source applications with push-pull functionality. The schema provides a common basis and guide for future studies.


2013 ◽  
Vol 7 (2) ◽  
pp. 83-99 ◽  
Author(s):  
Zheng O'Neill ◽  
Xiufeng Pang ◽  
Madhusudana Shashanka ◽  
Philip Haves ◽  
Trevor Bailey

Buildings ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 94
Author(s):  
Tara L. Cavalline ◽  
Jorge Gallegos ◽  
Reid W. Castrodale ◽  
Charles Freeman ◽  
Jerry Liner ◽  
...  

Due to their porous nature, lightweight aggregates have been shown to exhibit thermal properties that are advantageous when used in building materials such as lightweight concrete, grout, mortar, and concrete masonry units. Limited data exist on the thermal properties of materials that incorporate lightweight aggregate where the pore system has not been altered, and very few studies have been performed to quantify the building energy performance of structures constructed using lightweight building materials in commonly utilized structural and building envelope components. In this study, several lightweight concrete and masonry building materials were tested to determine the thermal properties of the bulk materials, providing more accurate inputs to building energy simulation than have previously been used. These properties were used in EnergyPlus building energy simulation models for several types of commercial structures for which materials containing lightweight aggregates are an alternative commonly considered for economic and aesthetic reasons. In a simple model, use of sand lightweight concrete resulted in prediction of 15–17% heating energy savings and 10% cooling energy savings, while use of all lightweight concrete resulted in prediction of approximately 35–40% heating energy savings and 30% cooling energy savings. In more complex EnergyPlus reference models, results indicated superior thermal performance of lightweight aggregate building materials in 48 of 50 building energy simulations. Predicted energy savings for the five models ranged from 0.2% to 6.4%.


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