Novel statistical methods for opportunity detection in energy time series

能源时间序列中机会检测的新颖统计方法

• 批准号: 2614268
• 金额: --
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2614268
• 关键词: Novel; statistical; methods; opportunity; detection

This project seeks to develop novel statistical opportunity detection methods inspired by, and feeding back into, end user energy demand systems. Due to contemporary sensor and IoT technologies, it is now possible to monitor energy usage across a range of devices and locations in real time. This provides an opportunity to synthesise data from numerous sources with a view to identifying different usage patterns, and thereby possible energy saving strategies. To achieve this, we will seek to develop novel methods to characterise such energy series, building on advances in changepoint detection, as well as other methods for detecting anomalous structures in signals. Where feasible, we will also seek to make generic software implementing such methods available in the form of reproducible software.

该项目旨在开发新的统计机会检测方法的启发，并反馈到，最终用户的能源需求系统。由于现代传感器和物联网技术，现在可以真实的实时监控一系列设备和位置的能源使用情况。这提供了一个机会来综合来自众多来源的数据，以确定不同的使用模式，从而可能的节能策略。为了实现这一目标，我们将寻求开发新的方法来识别这样的能量序列，建立在变化点检测的进步，以及其他方法来检测信号中的异常结构。在可行的情况下，我们还将寻求以可复制软件的形式提供实现这些方法的通用软件。