Algorithms and methodologies for decision support in energy efficiency on buildings

Dissertação apresentada na faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Electrotécnica e de ComputadoresBuildings worldwide account for approximately 40 percent of the global energy consumption and the resulting carbon footprint significantly exceeds those of all transportation combined. However, large and attractive opportunities to reduce energy use in buildings exist today. To reach ambitious energy efficiency goals, the building sector must undergo through technological innovation, informed customer choices, and smart business decisions. Existing building simulation tools provide users with key building performance indicators,such as energy use and demand. However, these tools do not deal with activities performed by building occupants and with the resulting utilization of spaces. At best, they rely on assumptions referring to human behavior. As a result, energy prediction often does not represent the real building utilization. Therefore, it is assumed that user behavior is one of the most important input parameter influencing the results of building performance simulations. A methodology for constructing an energy consumption model that reflects the human behavior dynamics and occupancy patterns within a building is presented. This research will provide a possible methodology for the pillars of future work in modeling the building usage under real patterns of utilization. A simulator has been developed from a model where both human behavior and building have been incorporated. Simulations have been performed to test different behavioral situations where the developed models and algorithms have been applied for prediction purposes. The proposed methodologies focus on the applicability of a rule-based expert system to support the simulator and stochastic modeling. The building’s occupant behavior is modeled with a hidden Markov model and the building’s spaces are described as Markov chains

Modeling cloud resources using machine learning

Cloud computing is a new Internet infrastructure paradigm where management optimization has become a challenge to be solved, as all current management systems are human-driven or ad-hoc automatic systems that must be tuned manually by experts. Management of cloud resources require accurate information about all the elements involved (host machines, resources, offered services, and clients), and some of this information can only be obtained a posteriori. Here we present the cloud and part of its architecture as a new scenario where data mining and machine learning can be applied to discover information and improve its management thanks to modeling and prediction. As a novel case of study we show in this work the modeling of basic cloud resources using machine learning, predicting resource requirements from context information like amount of load and clients, and also predicting the quality of service from resource planning, in order to feed cloud schedulers. Further, this work is an important part of our ongoing research program, where accurate models and predictors are essential to optimize cloud management autonomic systems.Postprint (published version

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems