AdaHeat: A general adaptive intelligent agent for domestic heating control

Copyright © 2015, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.Improving the energy efficiency of domestic heating systems can lead to a major reduction in energy consumption and the corresponding CO2 emissions. To this end, intelligent domestic heating agents (IDHAs) aim to operate domestic heating systems more efficiently with minimum user input. In this work, we propose a new general IDHA that balances heating cost and thermal discomfort in an infinite horizon optimization manner, learns an adaptive thermal model of the system under control on-line and plans a heating schedule that fully exploits the probabilistic occupancy estimates. Importantly, our agent adapts to the user preferences in balancing heating cost and thermal discomfort, as it relies on a single parametrization variable that is learned on-line, and is able to consider a wide range of heating systems typically employed in domestic settings. The backbone of our IDHA is an adaptive model predictive control approach along with a new general planning algorithm that utilizes dynamic programming. We present a thorough evaluation of our approach, and show its effectiveness in terms of Pareto efficiency and usability criteria against state-of-the-art IDHAs. By so doing, we also conduct a comprehensive characterization of existing IDHAs to provide significant insights about their performance in different operational settings

Poster abstract. Applying extended kalman filters to adaptive thermal modelling in homes

A key challenge for intelligent domestic heating systems is to obtain sufficient knowledge of the thermal dynamics of the home to build an adaptive thermal model. We present a study where stochastic grey-box modelling is used to develop thermal models and an extended Kalman filter is used for parameter estimation for a room in a family hom

Dynamic Lognormal Shadowing Framework for the Performance Evaluation of Next Generation Cellular Systems

Performance evaluation tools for wireless cellular systems are very important for the establishment and testing of future internet applications. As the complexity of wireless networks keeps growing, wireless connectivity becomes the most critical requirement in a variety of applications (considered also complex and unfavorable from propagation point of view environments and paradigms). Nowadays, with the upcoming 5G cellular networks the development of realistic and more accurate channel model frameworks has become more important since new frequency bands are used and new architectures are employed. Large scale fading known also as shadowing, refers to the variations of the received signal mainly caused by obstructions that significantly affect the available signal power at a receiver’s position. Although the variability of shadowing is considered mostly spatial for a given propagation environment, moving obstructions may significantly impact the received signal’s strength, especially in dense environments, inducing thus a temporal variability even for the fixed users. In this paper, we present the case of lognormal shadowing, a novel engineering model based on stochastic differential equations that models not only the spatial correlation structure of shadowing but also its temporal dynamics. Based on the proposed spatio-temporal shadowing field we present a computationally efficient model for the dynamics of shadowing experienced by stationary or mobile users. We also present new analytical results for the average outage duration and hand-offs based on multi-dimensional level crossings. Numerical results are also presented for the validation of the model and some important conclusions are drawn.</jats:p

Dynamic Lognormal Shadowing Framework for the Performance Evaluation of Next Generation Cellular Systems

Performance evaluation tools for wireless cellular systems are very important for the establishment and testing of future internet applications. As the complexity of wireless networks keeps growing, wireless connectivity becomes the most critical requirement in a variety of applications (considered also complex and unfavorable from propagation point of view environments and paradigms). Nowadays, with the upcoming 5G cellular networks the development of realistic and more accurate channel model frameworks has become more important since new frequency bands are used and new architectures are employed. Large scale fading known also as shadowing, refers to the variations of the received signal mainly caused by obstructions that significantly affect the available signal power at a receiver&#8217;s position. Although the variability of shadowing is considered mostly spatial for a given propagation environment, moving obstructions may significantly impact the received signal&#8217;s strength, especially in dense environments, inducing thus a temporal variability even for the fixed users. In this paper, we present the case of lognormal shadowing, a novel engineering model based on stochastic differential equations that models not only the spatial correlation structure of shadowing but also its temporal dynamics. Based on the proposed spatio-temporal shadowing field we present a computationally efficient model for the dynamics of shadowing experienced by stationary or mobile users. We also present new analytical results for the average outage duration and hand-offs based on multi-dimensional level crossings. Numerical results are also presented for the validation of the model and some important conclusions are drawn