Ontological specification for the model integration in ICT building energy systems

2012 ◽  
pp. 939-950 ◽  
Author(s):  
R Guruz ◽  
P Katranuschkov ◽  
R Scherer ◽  
J Kaiser ◽  
J Grunewald ◽  
...  
Keyword(s):  
Energy Systems ◽  
Building Energy ◽  
Model Integration ◽  
Building Energy Systems

scholarly journals Optimum Renewable Fraction for Grid-connected Photovoltaic in Office Building Energy Systems in Indonesia

2018 ◽  
Vol 9 (4) ◽  
pp. 1866 ◽  
Author(s):  
Ayong Hiendro ◽  
Ismail Yusuf ◽  
F. Trias Pontia Wigyarianto ◽  
Kho Hie Khwee ◽  
Junaidi Junaidi
Keyword(s):  
Carbon Dioxide ◽  
Renewable Energy ◽  
Energy Systems ◽  
Building Energy ◽  
Office Building ◽  
Pv System ◽  
Optimum Result ◽  
Pv Systems ◽  
Building Energy Systems

<span lang="EN-US">This paper analyzes influences of renewable fraction on grid-connected photovoltaic (PV) for office building energy systems. The fraction of renewable energy has important contributions on sizing the grid-connected PV systems and selling and buying electricity, and hence reducing net present cost (NPC) and carbon dioxide (CO<sub>2</sub>) emission. An optimum result with the lowest total NPC for serving an office building is achieved by employing the renewable fraction of 58%, in which 58% of electricity is supplied from the PV and the remaining 42% of electricity is purchased from the grid. The results have shown that the optimum grid-connected PV system with an appropriate renewable fraction value could greatly reduce the total NPC and CO<sub>2</sub> emission.</span>

Expressing the Building Energy Systems Thermodynamic Seasonal Efficiency

2017 ◽  
Author(s):  
Karolis Januševičius ◽  
Juozas Bielskus ◽  
Vytautas Martinaitis ◽  
Giedrė Streckienė ◽  
Dovydas Rimdžius
Keyword(s):  
Reference Conditions ◽  
Energy Systems ◽  
Integrated Approach ◽  
Building Energy ◽  
Simulation Software ◽  
Calculation Procedure ◽  
Thermodynamic Efficiency ◽  
Engineering Practice ◽  
Time Step ◽  
Building Energy Systems

In order to reduce impact to environment, a qualitative approach of energy saving is global aspect that is included in various forms of CO2 emissions, primary energy limitations and benchmarks in EU and member countries policy. Exergy analysis allows expressing the quality of energy flows in comparison to ambient or other reference conditions. Despite of this valuable information, this concept is not widely used in engineering practice. The article suggests the calculation procedure for sessional or periodical thermodynamic (exergy) efficiency in relation to variable reference conditions. Knowledge about defined procedures unlocks the possibility to fill up the implementation gap for building system engineering practice where seasonal performance parameters are widely used to express efficiency. Prepared algorithm allows determining seasonal or periodic thermodynamic efficiency of individual elements and energy transfer chains in building energy systems. Defined calculation procedure workflow is suitable for integrated approach when coupled heat transfer and fluid flow processes are explored in short time steps with dynamic simulation software tools. Presented algorithm ensures result that fits in thermodynamically correct range 0-1 and helps to summarize separate time step results. By adding duration of specific conditions, this analysis enables to identify critical peak periods and base load conditions across operation period. The presented framework fills the gap in lack of systematic expression for seasonal thermodynamic efficiency and suggests the process for calculation procedures workflow.

scholarly journals A Pattern-Recognition-Based Ensemble Data Imputation Framework for Sensors from Building Energy Systems

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5947
Author(s):  
Liang Zhang
Keyword(s):  
Pattern Recognition ◽  
Missing Data ◽  
Energy Systems ◽  
Building Energy ◽  
Imputation Method ◽  
Validation Dataset ◽  
Data Imputation ◽  
Validation Data ◽  
Imputation Methods ◽  
Building Energy Systems

Building operation data are important for monitoring, analysis, modeling, and control of building energy systems. However, missing data is one of the major data quality issues, making data imputation techniques become increasingly important. There are two key research gaps for missing sensor data imputation in buildings: the lack of customized and automated imputation methodology, and the difficulty of the validation of data imputation methods. In this paper, a framework is developed to address these two gaps. First, a validation data generation module is developed based on pattern recognition to create a validation dataset to quantify the performance of data imputation methods. Second, a pool of data imputation methods is tested under the validation dataset to find an optimal single imputation method for each sensor, which is termed as an ensemble method. The method can reflect the specific mechanism and randomness of missing data from each sensor. The effectiveness of the framework is demonstrated by 18 sensors from a real campus building. The overall accuracy of data imputation for those sensors improves by 18.2% on average compared with the best single data imputation method.

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