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In modern smart building climate control systems, accurate detection of unusual behavior in temperature sensors (outliers) can help reduce or prevent waste of energy consumption in a Heating, Ventilation and Air Conditioning (HVAC) system. In this work, we propose online learning-distance based outlier detection method. In the new method, we train and tune a multilayer neural network to learn a nonlinear...
In this article, we propose a novel method to detect the occupancy behavior of a building through the temperature and/or possible heat source information, which can be used for energy reduction, security monitoring for emerging smart buildings. Our work is based on a realistic building simulation program, EnergyPlus, from Department of Energy. EnergyPlus can model the various time-series inputs to...
Building energy accounts large amount of the total energy consumption, and smart building energy control leads to high energy efficiency and significant energy savings. A compact and accurate building thermal model is important for designing the efficient energy control system. In this paper, we propose an accurate thermal behavior modeling technique for general and complicated buildings. This new...
In this article, we propose a new behavioral thermal modeling method for fast building performance analysis, which is critical for energy-efficient smart building control and management. The new approach is based on two recurrent neutral network architecture to obtain the compact nonlinear thermal models for complicated building. We start with a more realistic building simulation program, EnergyPlus,...
The thermal performance of building envelope is a major requirement for generating a comfortable and hygienic interior environment. A roof is a main part of building envelope and provides protection from thermal damage caused by the excessive hot sunshine. In order to improve this thermal roof performance, the use of the air gap in the roof can be considered. This study presents a new type of open-web...
In this paper, we study the problem of anomaly detection in high-dimensional network streams. We have developed a new technique, called Stream Projected Outlier deTector (SPOT), to deal with the problem of anomaly detection from high-dimensional data streams. We conduct a case study of SPOT in this paper by deploying it on 1999 KDD Intrusion Detection application. Innovative approaches for training...
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