Smart Environment Vectorization : An Approach to Learning of User Lighting Preferences

The automation of smart environment systems is one of the main goals of smart home researching. This paper focus on learning user lighting preference, considering a working field like a standard office. A review of the smart environment and devices setup is done, showing a real configuration for test purposes. Suitable learning machine techniques are exposed in order to learn these preferences, and suggest the actions the smart environment should execute to satisfy the user preferences. Learning machine techniques proposed are fed with a database, so a proposal for the vectorization of data is described and analyzed.Ministerio de Educación y Ciencia TSI2006-13390-C02-02Junta de Andalucía TIC-214

Energy-saving policies in grid computing and smart environments

Texto completo descargado desde TeseoThis work studies the problem of energy consumption growth in two spheres: Grid-Computing and Smart Environments. These problems are tackled through the establishment of energy-saving policies developed for each environment in order to save the maximum energy as possible. In the Grid-Computing environment, seven energypolicies were designed in an attempt to minimize energy consumption through shutting resources down and booting them. It is proved that approximately 40% of energy can be saved. Efficiency of various grid locations was compared using Data Envelopment Analysis methodology. In Smart Environments where sensors perceive lighting conditions, the energy-saving policy adjusts lighting in order to satisfy user preferences and prevents energy from being wasted. A set of wireless sensors were deployed on two offices at the department of Computer Languages and Systems. The dataset created over several months was employed to extract information about user lighting preferences, from the application of which it is proven that around 70% of energy can be saved in lighting appliances.Premio Extraordinario de Doctorado U

Modeling Smart Homes for Prediction Algorithms

This paper reviews the goals of the Domoweb project and the solutions adopted to achieve them. As a result we enjoy a great support to develop smart home techniques and solutions. As a consequence of the acquired experiences a Smart home model is proposed as a division of four main categories. In relation with the smart home model, we show the essential features a smart environment prediction algorithm should satisfy and a procedure to select relevant information from the model to achieve artificial intelligence based solutions.Ministerio de Educación y Ciencia TSI2006-13390-C02-0