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（也称为非感官负载监测）是信号处理和模式识别技术的组合，以估算从总能量消耗信号中估算单个设备的能量消耗。它是通过识别汇总信号中的区分特征并将其分解为组成部分来实现的。将能源消耗数据降低到设备水平的分解很大程度上被确定为通过公民意识提高能源效率的机会，能源需求预测工具和对电器的智能自动控制工具，仅举几例。********** ***该研究计划旨在推进能量分解领域，以有效地确定现实生活中各个电力的功耗。这将通过建立申请人团队的最新发展，以霍尔效应当前传感器设备和弱监督的机器学习算法来实现。具体而言，我们将1）设计新的能量分类算法，用于使用低精确电流传感器的弱标记数据，2）分析分类算法对传感器信息的准确性和数量的敏感性，3）设计下一代的敏感性超低功率大厅效应电流传感器和4）设计具有最佳数量低精确传感器的商业和工业应用的新能量分解算法。这些进步将提高现有技术的准确性，可伸缩性和适应性。学习和微电子学。该研究结果也可能是新合作的起点，因为该领域最近的重要投资证实了能源分类的兴趣。*******自然资源加拿大确定了“加拿大建筑”行业有责任负责任地利用我们的能源资源，并将呼吁采取行动作为一种机制，可以增强和丰富我们的经济。”通过开发新型技术，以更有效地监控能源消耗，并最终为智能建筑提供前沿的能源管理技术，这是朝着该方向迈出的重要一步。***