Development of an energy evaluation methodology to make multiple predictions of the HVAC&R system energy demand for office buildings

Abstract HVAC&R systems are the most energy consuming building services, representing approximately half of the final energy use in the building sector. Despite their significant energy use, there is a lack of a consistent and homogeneous framework to efficiently guide research, mainly due to the complexity and variety of HVAC&R systems, but also to insufficient rigor in their energy analysis. Quantifying the energy consumption characteristics of HVAC&R system is complicated, because the energy savings provided by this system depend on various factors. This research evaluates energy consumption characteristics of HVAC&R systems, with the aim of establishing a common idea for the analysis of building energy efficiency. The objective of this study is to develop an energy evaluation methodology and a simple simulation program that may be used by engineers and designers to assess the effectiveness and economic benefits of HVAC&R systems. Our approach deals with the concept of HVAC&R system energy use aggregation levels that are composed of subsystems. To carry out a techno-economical estimation of HVAC&R systems considering different types of subsystems, the matrix combination analyzed, and a total of 960 HVAC&R systems can be implemented for a large-scale office building. The methodology of energy analysis that was carried out in this study highlights how to plan and design toward utilizing the most effective HVAC&R systems.

[1]  H. Božić,et al.  Energy efficiency indicators , 2009 .

[2]  Ivan Korolija,et al.  Influence of building parameters and HVAC systems coupling on building energy performance , 2011 .

[3]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[4]  Ayşin Sev,et al.  SPACE EFFICIENCY IN HIGH-RISE OFFICE BUILDINGS , 2009 .

[5]  M. V. Frank,et al.  Predictions of Energy Savings in HVAC Systems by Lumped Models (Preprint) , 2010 .

[6]  Kamel Ghali,et al.  Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm , 2009 .

[7]  Luis Pérez-Lombard,et al.  The map of energy flow in HVAC systems , 2011 .

[8]  Alberto Hernandez Neto,et al.  Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption , 2008 .

[9]  Mehdi Shahrestani,et al.  Characterising the energy performance of centralised HVAC&R systems in the UK , 2013 .

[10]  Tianzhen Hong,et al.  Building simulation: an overview of developments and information sources , 2000 .

[11]  Andrew Kusiak,et al.  Multi-objective optimization of HVAC system with an evolutionary computation algorithm , 2011 .

[12]  Standard Ashrae Thermal Environmental Conditions for Human Occupancy , 1992 .

[13]  Berhane H. Gebreslassie,et al.  Design of environmentally conscious absorption cooling systems via multi-objective optimization and life cycle assessment , 2009 .

[14]  Luis Pérez-Lombard,et al.  Constructing HVAC energy efficiency indicators , 2012 .

[15]  Lihua Xie,et al.  HVAC system optimization—in-building section , 2005 .

[16]  Dashamir Marini,et al.  Optimization of HVAC systems for distributed generation as a function of different types of heat sources and climatic conditions , 2013 .

[17]  Moncef Krarti,et al.  Optimization of envelope and HVAC systems selection for residential buildings , 2011 .

[18]  Edward Henry Mathews,et al.  Developing cost efficient control strategies to ensure optimal energy use and sufficient indoor comfort , 2000 .

[19]  P. G. Luscuere,et al.  An exergy application for analysis of buildings and HVAC systems , 2010 .

[20]  K. F. Fong,et al.  HVAC system optimization for energy management by evolutionary programming , 2006 .