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|Title:||Machine learning technique for energy efficiency analysis of HVAC systems||Authors:||Ma, Biao||Keywords:||DRNTU::Engineering||Issue Date:||2014||Abstract:||Energy usage in air-conditioning system accounts for approximately 50% of the total energy consumption in commercial building sector in Singapore, presenting a significant opportunity for minimizing the energy consumption improving the energy efficiency of its operation. Machine learning techniques have been introduced to facilitate the modeling and optimizing of HVAC system as they are able to handle nonlinear dynamic and complex data of HVAC system. Machine learning techniques such as Support Vector Machine (SVM) and Back Propagation (BP) have been developed and grown for decades. They have proven their power in nonlinear and complex systems through successful and effective applications in business, medical and industrial fields. Extreme Learning Machine (ELM) is a new member of machine learning technique community but it has been gaining increasing attention for its outstanding performance in learning speed and adaptability. This project explores the use of machine learning techniques of ELM and SVM to derive at a better analysis of energy consumption and efficiency of large air-conditioning systems. A HVAC system simulation software, DesignBuilder was being used to set up the layout and conditions for simulation as the desired experimental lab located at Lecture Theatre 24 was not ready for use. The data collected from DesignBuilder was analyzed by ELM and SVM to examine the various factors like air temperature and CO2 level and occupancy level’s effect on energy consumption. The most influencing factor on energy consumption would then be identified. In addition, the performances of ELM and SVM in terms of testing Root Mean Square Error (RMSE) and testing time were compared to find out which machine learning technique is more suitable in handling HVAC data. Discoveries made using the machine learning techniques can be captured and organized in a format for later recall and communication to others.||URI:||http://hdl.handle.net/10356/61491||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
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