Probabilistic Neural Network based Managing Algorithm for Building Automation System

A building automation system is a centralized intelligent system, which controls the operation of energy, security, water, and safety by the help of hardware and software modules. The general software modules employed for automation process have an algorithm with pre-determined decisions. However, such pre-determined decision algorithms won’t work in a proper manner at all situations like a human brain. Therefore a human biological inspired algorithms are developed in recent days and termed as neural network algorithms. The Probabilistic Neural Network (PNN) is a kind of artificial neural network algorithm which has the ability to take decisions same as like of human brains in an efficient way. Hence a building automation system is proposed in the work based on PNN for verifying the effectiveness of neural network algorithms over the traditional pre-determined decision making algorithms. The experimental work is further extended to verify the performances of the basic neural network algorithm called Convolution Neural Network (CNN).

IP-TUNNELING OF KNX BUILDING AUTOMATION SYSTEM AS AN IOT REABILITY STUDY

At the present time the smart technology enhanced to be utilized for the human life in many elds, especially in the houses. The building automation experienced a rapid growth in techniques and methods to provide an advanced management for operational advantages in buildings and develop the equipment in the houses to the consumption of energy and operation. When the KNX system has been developed to be the most important building automation, the ethernet system has evolved to be global communication system and use it as automation system. Among the various technological developments is IoT, which is the essential development the future achievement via the internet techniques, Meanwhile the different available communication mediums of KNX and the need of utilizing IP network in compiling extensive areas of KNX has led us to conduct this comparison.

Development of a Grey Box Thermal Dynamic Model without Building Construction Knowledge

Model Predictive Controllers (MPC) in building Heating Ventilation and Air Conditioning (HVAC) systems have demonstrated significant energy savings when compared to typical on/off controllers. MPCs require information about the building’s thermal dynamics which is challenging to model, especially for older structures without buildings specifications. This research investigates the ability to develop a grey box thermal dynamic model that can determine the net thermal dynamics, without any building construction information. Sensors were installed within a test cell to monitor the building automation system (BAS) points, and collect building element surface temperature data. The simulation program Simulink was used to develop and test iterations of grey box models. The final model, that relies solely on BAS points, is able to predict the ambient temperature for a 3-hour Prediction Window to within 1.7% accuracy. This model demonstrates the potential for more buildings to implement HVAC MPC systems with grey box thermal dynamic modeling

Development of a Grey Box Thermal Dynamic Model without Building Construction Knowledge

Model Predictive Controllers (MPC) in building Heating Ventilation and Air Conditioning (HVAC) systems have demonstrated significant energy savings when compared to typical on/off controllers. MPCs require information about the building’s thermal dynamics which is challenging to model, especially for older structures without buildings specifications. This research investigates the ability to develop a grey box thermal dynamic model that can determine the net thermal dynamics, without any building construction information. Sensors were installed within a test cell to monitor the building automation system (BAS) points, and collect building element surface temperature data. The simulation program Simulink was used to develop and test iterations of grey box models. The final model, that relies solely on BAS points, is able to predict the ambient temperature for a 3-hour Prediction Window to within 1.7% accuracy. This model demonstrates the potential for more buildings to implement HVAC MPC systems with grey box thermal dynamic modeling