Combined use of nonintrusive monitoring techniques and energy recipes to reduce energy hungry behaviours

F. Reda, S. Karjalainen, M. Tuomisto
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引用次数: 1

Abstract

The fact that occupant behaviour has a large effect on buildings' energy consumption is well accepted. Occupants affect energy consumption by their behaviours: using lighting, appliances, thermostats etc. and interacting with envelope components such as windows and blinds. During last decades, specific ICT solutions have been developed for addressing behavioural changes toward energy saving and consequently leading to energy conscious occupant behaviours. This paper presents a mean of interaction between sensors and users for tackling behavioural changes toward energy saving in homes. This is based on premade rules and instructions (referred to as recipes), meant for tackling energy hungry everyday life actions. Moreover, it investigates the use of nonintrusive appliance load monitoring (NIALM) system. Among all the available off the shelfs sensors, NIALM seems to be very promising, especially if combined with self-learning algorithms, for detecting energy consumption of electrical appliances. This combination can potentially solve existing constrains: detecting and identifying appliances with very small or continuous electricity consumption or that turn on or turn off slowly. Literature information have been cross checked with home energy consumption monitored data in order to create pre-formulated energy recipes for empowering occupants addressing specific energy hungry behaviours. Besides establishing virtuous occupant behaviours, devoted to a rational and judicious use of energy, the adoption of energy recipes aims to generate critical knowledge about the intrinsic meaning of technical variables.
结合使用非侵入式监测技术和能源配方来减少能源饥渴行为
人们普遍认为,居住者的行为对建筑物的能源消耗有很大的影响。居住者通过他们的行为影响能源消耗:使用照明、电器、恒温器等,并与窗户和百叶窗等外壳组件相互作用。在过去的几十年里,已经开发了具体的ICT解决方案,以解决节能行为的变化,从而导致能源意识的居住者行为。本文提出了一种传感器和用户之间的交互方法,以解决家庭节能行为的变化。这是基于预先制定的规则和说明(称为食谱),旨在解决日常生活中需要能量的行为。此外,它还研究了非侵入式设备负载监测(NIALM)系统的使用。在所有可用的现成传感器中,NIALM似乎非常有前途,特别是如果与自学习算法相结合,用于检测电器的能耗。这种组合可以潜在地解决现有的限制:检测和识别耗电量很小或持续耗电量或开关缓慢的电器。文献信息与家庭能源消耗监测数据进行了交叉核对,以便创建预先制定的能源配方,使居住者能够解决特定的能源饥渴行为。除了建立良好的居住者行为,致力于理性和明智地使用能源之外,采用能源配方的目的是产生关于技术变量内在意义的关键知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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