scholarly journals Modeling and optimization of complex building energy systems with deep neural networks

Author(s):  
Yize Chen ◽  
Yuanyuan Shi ◽  
Baosen Zhang
2003 ◽  
Vol 125 (3) ◽  
pp. 331-342 ◽  
Author(s):  
Moncef Krarti

An overview of commonly used methodologies based on the artificial intelligence approach is provided with a special emphasis on neural networks, fuzzy logic, and genetic algorithms. A description of selected applications to building energy systems of AI approaches is outlined. In particular, methods using the artificial intelligence approach for the following applications are discussed: Prediction energy use for one building or a set of buildings (served by one utility), Modeling of building envelope heat transfer, Controlling central plants in buildings, and Fault detection and diagnostics for building energy systems.


Author(s):  
R Guruz ◽  
P Katranuschkov ◽  
R Scherer ◽  
J Kaiser ◽  
J Grunewald ◽  
...  

Author(s):  
Ayong Hiendro ◽  
Ismail Yusuf ◽  
F. Trias Pontia Wigyarianto ◽  
Kho Hie Khwee ◽  
Junaidi Junaidi

<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>


Sign in / Sign up

Export Citation Format

Share Document