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一种用于基于行为的智能能源环境以实现协同制造空间的网络传感器融合方法。

A Network Sensor Fusion Approach for a Behaviour-Based Smart Energy Environment for Co-Making Spaces.

作者信息

Chang Teng-Wen, Huang Hsin-Yi, Hung Chung-Wen, Datta Sambit, McMinn Terrance

机构信息

College of Design, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan.

Department of Electrical Engineering, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan.

出版信息

Sensors (Basel). 2020 Sep 25;20(19):5507. doi: 10.3390/s20195507.

DOI:10.3390/s20195507
PMID:32992965
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7582833/
Abstract

User behaviour and choice is a significant parameter in the consumption patterns of energy in the built environment. This paper introduces a behavior-based approach for developing smart energy applications. With the rapid development of wireless sensor networks and the Internet of Things (IoT), human-computer interfaces can be created through the mapping of user experiences. These applications can provide users with dynamic feedback on their energy consumption patterns in their built environment. The paper describes a "Sensible Energy System" (SENS) that is based on user experience design methods with sensor network technology. Through SENS, solar energy simulation is combined with device consumption data in order to achieve an IoT network to facilitate the interaction between user behaviors and electricity consumption. The interaction between users and devices through SENS can not only optimize power consumption, but also provide consumers with additional choice and dynamic decision making regarding their consumption. This article provides an (1) understanding and analysis of users' spatial interaction, explains the (2) planning of the new smart environment design and user experiences, discusses (3) designing a suitable Wireless sensor network (WSN) agent and energy connection, describes (4) the information that has been collected, and (5) incorporates a rooftop solar potential simulation for predicting energy outputs into the sensor network model.

摘要

用户行为和选择是建筑环境中能源消费模式的一个重要参数。本文介绍了一种基于行为的方法来开发智能能源应用。随着无线传感器网络和物联网（IoT）的快速发展，可以通过映射用户体验来创建人机界面。这些应用可以为用户提供关于其建筑环境中能源消费模式的动态反馈。本文描述了一种基于传感器网络技术的用户体验设计方法的“智能能源系统”（SENS）。通过SENS，太阳能模拟与设备消耗数据相结合，以实现一个物联网网络，促进用户行为与电力消耗之间的交互。通过SENS，用户与设备之间的交互不仅可以优化功耗，还可以为消费者提供关于其消费的额外选择和动态决策。本文提供了（1）对用户空间交互的理解和分析，解释了（2）新智能环境设计和用户体验的规划，讨论了（3）设计合适的无线传感器网络（WSN）代理和能源连接，描述了（4）所收集的信息，并（5）将用于预测能源输出的屋顶太阳能潜力模拟纳入传感器网络模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/4b212a578aac/sensors-20-05507-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/13fee9c344ac/sensors-20-05507-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/52d2091a1ba5/sensors-20-05507-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/5ecca37c2572/sensors-20-05507-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/d0a0849db068/sensors-20-05507-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/37087f3d5580/sensors-20-05507-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/8f3cbf82483c/sensors-20-05507-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/ad64d210cb54/sensors-20-05507-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/3cfbbb49014c/sensors-20-05507-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/46ba05a587d9/sensors-20-05507-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/3c762a4b1976/sensors-20-05507-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/69ef63c34d52/sensors-20-05507-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/5b4c0556d2fa/sensors-20-05507-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/6c15b5230b45/sensors-20-05507-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/4b212a578aac/sensors-20-05507-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/13fee9c344ac/sensors-20-05507-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/52d2091a1ba5/sensors-20-05507-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/5ecca37c2572/sensors-20-05507-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/d0a0849db068/sensors-20-05507-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/37087f3d5580/sensors-20-05507-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/8f3cbf82483c/sensors-20-05507-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/ad64d210cb54/sensors-20-05507-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/3cfbbb49014c/sensors-20-05507-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/46ba05a587d9/sensors-20-05507-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/3c762a4b1976/sensors-20-05507-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/69ef63c34d52/sensors-20-05507-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/5b4c0556d2fa/sensors-20-05507-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/6c15b5230b45/sensors-20-05507-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b21/7582833/4b212a578aac/sensors-20-05507-g014.jpg

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