Abstract-This research focuses primarily on modelling of an existing building using Revit architecture and importing the model in energy analysis software Green Building studio, applying the energy efficient parameters for light and thermal energy in order to obtain the optimum energy saving and minimise the loss of energy in the building. In this context, subject building refers to the existing building which is individual residential home located in Tambaram IV. INTRODUCTIONRetrofitting an existing building can oftentimes be more costeffective than building a new facility. Since buildings consume a significant amount of energy, particularly for heating and cooling, and because existing buildings comprise the largest segment of the built environment, it is important to initiate energy conservation retrofits to reduce energy consumption and the cost of heating, cooling, and lighting buildings. II. BUILDING INFORMATION MODELLINGBuilding information modelling (BIM) is a new way of approaching the design and documentation of building projects. BIM Models and manages not just graphics, but also information that allows the automatic generation of drawings and reports, design analysis, schedule simulation, facilities management, and more ultimately enabling the building team to make better-informed decisions. A. Revit architectureThe energy analytical model feature in Revit building design software provides tools for fast, flexible creation of models for energy simulation. Autodesk Revit Architecture software provides architects the tools to easily capture and analyse design concepts, and more accurately maintain coordinated and reliable design data through documentation and construction.Energy analytical models are created to suit different design stage needs, workflows and precision preferences. A model to be created directly from architectural building elements and room/space elements, or manually using conceptual massing.

Abstract-This paper presents an efficient and scalable framework called Reliable and Energy Efficient Framework (REEF) for reliable data collection in Wireless Sensor Networks. REEF employs a distributed scheme which enables it to scale to large networks. It partitions the network in to clusters where the node with highest residual energy in a neighborhood become the cluster head. REEF forms a virtual backbone connecting the cluster heads and the sink by selecting some nodes from each cluster as gateway nodes. Sensor nodes report sensed data to their respective cluster heads which use an outlier detection algorithm to detect faulty data. REEF significantly cuts down on energy consumption by ensuring that a large number of sensor nodes can go into a deep sleep mode, in which the radio as well as CPU are switched off, for a major part of their life time. Simulation results demonstrate that REEF uses as low as 50% of the energy for the same accuracy when compared to a recently proposed scheme based on passive listening.