Advanced devices and algorithms for energy disaggregation in buildings

用于建筑物能量分解的先进设备和算法

• 批准号: RGPIN-2017-06469
• 负责人: Gagnon, Ghyslain
• 金额: $ 1.75万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=651358
• 关键词: Advanced; devices; algorithms; energy; disaggregation

Energy disaggregation (also referred as nonintrusive load monitoring) is a combination of signal processing and pattern recognition techniques to estimate the energy consumption of individual appliances from the total energy consumption signal. It is achieved by identifying discriminating features in the aggregated signal and decomposing it into its constituent parts. Disaggregation of the energy consumption data down to the level of appliances has been largely identified as an opportunity for enhanced energy efficiency through citizen awareness, energy demand prediction tools for utilities and smart automatic control of appliances, just to name a few.*******This research program seeks to advance the field of energy disaggregation to efficiently determine the power consumption of individual electrical loads in real-life scenarios. This will be achieved by building on the applicant's team latest developments in Hall-effect current sensor devices and weakly-supervised machine learning algorithms. Specifically, we will 1) design new energy disaggregation algorithms for residential applications using weakly labeled data from low-precision current sensors, 2) analyze the sensitivity of the disaggregation algorithms to the accuracy and quantity of sensor information, 3) design the next generation of ultra-low-power Hall-effect current sensors and 4) design new energy disaggregation algorithms for commercial and industrial applications with an optimal number of low-precision sensors. These advances will increase the accuracy, the scalability and the adaptability of existing techniques.*******This research program will train four HQP in domains which are in high demand in industry and academia, gaining important skills in signal processing, machine learning and microelectronics. The research results could also be the starting point of new collaborations, as energy disaggregation is gaining interest from industry and utilities, as confirmed by recent important investments in this field.*******Natural Resources Canada established that "the Canadian buildings sector has a duty to use our energy resources responsibly and take up the call to action as a mechanism that will strengthen and enrich our economy for future generations." This research program is an important step in that direction, through the development of novel technologies to monitor energy consumption more efficiently, and eventually, enabling leading-edge energy management technologies for smart buildings.***

能量分解(也称为非侵入性负载监测)是信号处理和模式识别技术的组合，用于根据总能耗信号估计单个电器的能耗。它是通过识别聚集信号中的区分特征并将其分解成其组成部分来实现的。能源消耗数据分解到家用电器的级别在很大程度上被认为是通过公民意识、公用事业的能源需求预测工具和家用电器的智能自动控制来提高能效的机会。*本研究计划旨在推动能源分解领域的发展，以有效地确定现实生活场景中单个电力负荷的电力消耗。这将通过建立在申请者团队在霍尔效应电流传感器设备和弱监督机器学习算法方面的最新发展来实现。具体来说，我们将1)使用低精度电流传感器的弱标记数据设计新的用于住宅应用的能量分解算法，2)分析分解算法对传感器信息的准确性和信息量的敏感性，3)设计下一代超低功耗霍尔效应电流传感器，4)设计新的低精度传感器数目最优的商业和工业应用的能量分解算法。这些进展将提高现有技术的准确性、可扩展性和适应性。*本研究计划将在工业界和学术界需求较高的领域培训四名HQP，获得信号处理、机器学习和微电子学方面的重要技能。这些研究结果也可能成为新合作的起点，因为行业和公用事业对能源分解的兴趣越来越大，这一点最近在该领域的重要投资中得到了证实。*加拿大自然资源公司确立，“加拿大建筑业有责任负责任地使用我们的能源资源，并将行动呼吁作为一种机制，为子孙后代加强和丰富我们的经济。”这项研究计划是朝着这个方向迈出的重要一步，通过开发新技术来更有效地监控能源消耗，并最终实现智能建筑的尖端能源管理技术。*