Context aware building energy management system with heterogeneous wireless network architecture

Energy wastage in buildings is to be minimized to reduce the carbon footprint of electricity. Wireless sensor and actor networks (WSAN) have been providing solutions for effective energy management within buildings. In this paper, we present a decisive server based context aware energy management system for smart buildings through Cyber Physical System (CPS) models. A layered architecture for building energy management is proposed to enhance scalability of the system. Heterogeneous wireless network based multiple radio gateway is proposed and implemented to make the system more adaptive to different applications catering to variable data rates. A smart room test bed is deployed in the IIT Hyderabad campus, where the decisive server collects various physical parameters through sensors, and based on the context generates wireless control messages to power electronics based actuators. Integrating context awareness into the system increases the efficiency in terms of energy savings and was observed to be significant, around 30%. The paper also presents a detailed analysis on the turnaround time required to realise the real saving after recovering investments. Applications are developed to integrate smart phones and tabloids providing web enablement to the end user. In this paper, each of the sensors and actuators in the smart room are associated with a state machine, which enables modelling of the system using Hybrid automata for future scope of applications

Neural Network based Short Term Forecasting Engine To Optimize Energy And Big Data Storage Resources Of Wireless Sensor Networks

Energy efficient wireless networks is the primary research goal for evolving billion device applications like IoT, smart grids and CPS. Monitoring of multiple physical events using sensors and data collection at central gateways is the general architecture followed by most commercial, residential and test bed implementations. Most of the events monitored at regular intervals are largely redundant/minor variations leading to large wastage of data storage resources in Big data servers and communication energy at relay and sensor nodes. In this paper a novel architecture of Neural Network (NN) based day ahead steady state forecasting engine is implemented at the gateway using historical database. Gateway generates an optimal transmit schedules based on NN outputs thereby reducing the redundant sensor data when there is minor variations in the respective predicted sensor estimates. It is observed that NN based load forecasting for power monitoring system predicts load with less than 3% Mean Absolute Percentage Error (MAPE). Gateway forward transmit schedules to all power sensing nodes day ahead to reduce sensor and relay nodes communication energy. Matlab based simulation for evaluating the benefits of proposed model for extending the wireless network life time is developed and confirmed with an emulation scenario of our testbed. Network life time is improved by 43% from the observed results using proposed model

Usability evaluation of a web-based tool for supporting holistic building energy management

This paper presents the evaluation of the level of usability of an intelligent monitoring and control interface for energy efficient management of public buildings, called BuildVis, which forms part of a Building Energy Management System (BEMS.) The BEMS ‘intelligence’ is derived from an intelligent algorithm component which brings together ANN-GA rule generation, a fuzzy rule selection engine, and a semantic knowledge base. The knowledge base makes use of linked data and an integrated ontology to uplift heterogeneous data sources relevant to building energy consumption. The developed ontology is based upon the Industry Foundation Classes (IFC), which is a Building Information Modelling (BIM) standard and consists of two different types of rule model to control and manage the buildings adaptively. The populated rules are a mix of an intelligent rule generation approach using Artificial Neural Network (ANN) and Genetic Algorithms (GA), and also data mining rules using Decision Tree techniques on historical data. The resulting rules are triggered by the intelligent controller, which processes available sensor measurements in the building. This generates ‘suggestions’ which are presented to the Facility Manager (FM) on the BuildVis web-based interface. BuildVis uses HTML5 innovations to visualise a 3D interactive model of the building that is accessible over a wide range of desktop and mobile platforms. The suggestions are presented on a zone by zone basis, alerting them to potential energy saving actions. As the usability of the system is seen as a key determinate to success, the paper evaluates the level of usability for both a set of technical users and also the FMs for five European buildings, providing analysis and lessons learned from the approach taken