A system for energy conservation through personalized learning mechanism

Several challenges exist in developing smart buildings such as the development of context aware algorithms and real-time control systems, the integration of numerous sensors to detect various parameters, integration changes in the existing electrical infrastructure, and high cost of deployment. Another major challenge is to optimize the energy usage in smart buildings without compromising the comfort level of individuals. However, the success of this task requires in depth knowledge of the individual and group behaviour inside the smart building. To solve the aforementioned challenges, we have designed and developed a Smart Personalised System for Energy Management (SPSE), a low cost context aware system integrated with personalized and collaborative learning capabilities to understand the real-time behaviour of individuals in a building for optimizing the energy usage in the building. The context aware system constitutes a wearable device and a wireless switchboard that can continuously monitor several functions such as the real-time monitoring and localization of the presence of the individual, real-time monitoring and detection of the usage of switch board and equipment, and their time of usage by each individual. Using the continuous data collected from the context aware system, personalized and group algorithms can be developed for optimizing the energy usage with minimum sensors. In this work, the context aware system was tested extensively for module performance and for complete integrated device performance. The study found the proposed system provides the opportunity to collect data necessary for developing a personalized system for smart buildings with minimum sensors.