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Internet of Things Based Occupancy Detection Using Ensemble Classifier for Smart Buildings

机译:基于事物的互联网占用检测使用智能建筑物的集合分类器

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摘要

Buildings account for a large share in energy consumption in day to day life. Occupancy based models can help in modeling whether the building or the particular room is occupied or not. Occupancy detection mechanisms can help in automating the electrical appliances and make them operational only in presence of the person in the room. This helps in creating energy aware scenarios which can contribute to the energy efficiency and reduction in power tariff. In this work, we take the approach of occupancy modeling by the help of Ensemble Models constructed using Random Forests, Logistic Regression and Support Vector Machine classifiers. The ensemble approach undertaken in this work is Voting and the weights of classifiers to the meta-model are fine-tuned using a Differential Evolution optimization algorithm. The results were found to be of high accuracy, i.e., 98.8% and 98.7% on the given test sets.
机译:建筑物在日常生活中占据了能源消耗的大量份额。 基于占用的模型可以帮助建模建筑物或特定房间是否被占用。 占用检测机制可以帮助自动化电器,并使它们仅在房间内的人的存在下运行。 这有助于创建能够有助于能源效率和减少电价的能量意识的情景。 在这项工作中,我们通过使用随机森林,逻辑回归和支持向量机分类器构建的集合模型来采取占用模型的方法。 在这项工作中进行的集合方法是投票,并且使用差分演进优化算法进行微调对元模型的分类器的重量。 发现结果具有高精度,即在给定的测试集中的98.8%和98.7%。

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