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首页> 外文期刊>Energy and Buildings >A novel feature selection framework with Hybrid Feature-Scaled Extreme Learning Machine (HFS-ELM) for indoor occupancy estimation
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A novel feature selection framework with Hybrid Feature-Scaled Extreme Learning Machine (HFS-ELM) for indoor occupancy estimation

机译：一种具有混合特征量级极限学习机（HFS-ELM）的新颖特征选择框架，用于室内占用率估算

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摘要

Indoor occupancy estimation can be an important parameter for automating Air Conditioning and Mechanical Ventilation (ACMV) operations in buildings. In this work, we propose a feature selection framework for constructing an occupancy estimator. The framework has two main components: a filter component, which uses a filter method of feature selection and a wrapper component, which implements a wrapper method of feature selection with a machine learning based occupancy estimator. The framework is thus a kind of filter-wrapper hybrid feature selection method. However, the framework is novel in that it uses a combination of static and dynamic features. We use the static features for the purpose of speed, since filter methods of feature selection (which work with static features) are quite fast. Thus, the overall computation time of the framework is kept low, while ensuring good accuracy of estimation due to the use of dynamic features in the wrapper stage. To perform occupancy estimation within the proposed framework, we present a novel technique called the Hybrid Feature-Scaled Extreme Learning Machine (HFS-ELM). The HFS-ELM is a dynamic model of the occupancy level that extracts dynamic features from its inputs. The architecture of the HFS-ELM method is explained in detail. Experimental results in an office space show the effectiveness of the proposed framework. (C) 2017 Elsevier B.V. All rights reserved.
机译：室内占用率估算可能是使建筑物中的空调和机械通风（ACMV）操作自动化的重要参数。在这项工作中，我们提出了一种用于构建占用估算器的特征选择框架。该框架具有两个主要组件：过滤器组件和包装器组件，其中过滤器组件使用特征选择的过滤器方法，包装器组件使用基于机器学习的占用估计器实现特征选择的包装器方法。因此，该框架是一种过滤器/包装器混合特征选择方法。但是，该框架是新颖的，因为它结合了静态和动态功能。我们使用静态要素是为了提高速度，因为要素选择的过滤方法（适用于静态要素）非常快。因此，框架的总体计算时间保持较低，同时由于在包装阶段使用了动态功能，从而确保了良好的估计准确性。为了在建议的框架内执行占用率估计，我们提出了一种称为混合功能规模极限学习机（HFS-ELM）的新技术。 HFS-ELM是占用级别的动态模型，可从其输入中提取动态特征。详细说明了HFS-ELM方法的体系结构。在办公空间中的实验结果证明了所提出框架的有效性。 （C）2017 Elsevier B.V.保留所有权利。

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