A Azzedine Yahiaoui

A Collaborative Distributed Control and Building Performance Simulation Based on Systems Engineering Practice

Building itself is like a complex system with a number of physical processes that interact with each other and with the environment. From the control point of view, this system is composed of multi dynamic subsystems that must be monitored and controlled in order to achieve occupants’ well-being at lowest energy use possible. However, environmental and occupants change in a building, and this increases the complexity in applying control systems. For this reason, this paper presents a framework for the application of systems engineering (SE) concept - a systematic approach for adapting procedures, tools, and standards to all practical problems - in the design of control systems by run-time coupling for building performance applications. Although this is based on SE good practice and corresponding SE standards, the development life cycle of control systems are covered ranging from the operational needs to implementation, operation and disposal. As an essential step toward this goal, namely using computer simulations to evaluate the impact of advanced control systems on buildings indoor operation and energy efficiency, this paper describes a collaborative distributed simulation that was developed and implemented between different simulation tools such as ESP-r and Matlab/Simulink. The paper also addresses a study of verification and validation (V&V) issues within this framework: inconsistency checking, traceability issues and all requirements related in SE standards, especially EIA-632.

Modelling and evaluating dynamic switching networks using stochastic Petri nets

Stochastic Petri net is a formalism developed in the field of computer science for modelling system performance. This work presents an approach based on the concept of an approximate analytical model to evaluate the performance of a dynamic network using stochastic Petri nets (SPNs). The proposed approach assists the analyst in producing a simple model that can be easily generalized. This model presents a technique, which increases the importance state using the network. A formal technique using stochastic Petri nets is also proposed to graphically investigate the steady state performance measures. It is shown that from the measured parameters and under the buffered strategy, the baseline model with basic switches which provide a low probability for the performance of networks is also presented.