Renewable Energy
M. Esmaeili Shayan
Abstract
Environmentally sustainable metropolitan environments are characterized by their ability to effectively produce and distribute power while reducing their impact on the environment. Smart homes are essential in smart cities since they enhance sustainability and efficiency in urban settings. A key advantage ...
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Environmentally sustainable metropolitan environments are characterized by their ability to effectively produce and distribute power while reducing their impact on the environment. Smart homes are essential in smart cities since they enhance sustainability and efficiency in urban settings. A key advantage of smart homes is their capacity to diminish energy use and carbon emissions. This is accomplished by optimizing energy consumption in home appliances, which is customized to fulfill the individual requirements and preferences of consumers. However, there is still a need for further academic research to investigate and improve the functioning of intelligent residential homes in microgrids. To efficiently manage microgrids, it is crucial to gather and analyze large amounts of electrical data related to power production from microgrid sources and energy consumption of the loads. This study examines the use of Non-Intrusive Load Monitoring (NILM) methods to monitor electrical parameters of different loads in microgrids. The research focuses on the application of affordable smart meters that are equipped with Internet of Things (IoT) capabilities. An empirical study showcases the possibility of collecting significant data on microgrid operation via the deployment of an operational microgrid that integrates a hybrid wind-solar power source with a variety of home appliances.
Environment
M. Moallem; H. Hassanpour; A. A. Pouyan
Abstract
Smart homes enable many people, especially the elderly and patients, to live alone and maintain their independence and comfort. The realization of this goal depends on monitoring all activities in the house to report any observed anomaly immediately to their relatives or nurses. Anomaly detection in ...
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Smart homes enable many people, especially the elderly and patients, to live alone and maintain their independence and comfort. The realization of this goal depends on monitoring all activities in the house to report any observed anomaly immediately to their relatives or nurses. Anomaly detection in smart homes, just by existing data, is not an easy task. In this work, we train a recurrent network with raw outputs of binary sensors, including motion and door sensors, to predict which sensor will be switched on/off in the next event, and how long this on/off mode will last. Then, using Beam Search, we extend this event into k sequences of consecutive events to determine the possible range of upcoming activities. The error of this prediction i.e., the distance between these possible sequences and the real string of events is evaluated using several innovative methods for measuring the spatio-temporal similarity of the sequences. Modeling this error as a Gaussian distribution allows to assess the likelihood of anomaly scores. The input sequences that are ranked higher than a certain threshold will be considered as abnormal activities. The results of the experiments showed that this method enables the detection of abnormal activities with desirable accuracy.
