Multivariate time series pattern discovery in a complex continuous industrial process

随着传感器和信息技术的进步，组织现在能够持续收集大量数据并将其存储在大型数据库中。这些数据的可用性使解决以前无法解决的问题和改进现有程序成为可能。值得一提的是，安大略省政府早在2010年就在安大略省所有家庭和小企业中引入了智能电表传感器。我们的工业合作伙伴Energent认为，这些传感器收集的数据包含有价值的信息，可以改善高峰时段的能源消耗，并将能源供应重新分配到全省不同地区。问题变成了如何提取这些有价值的信息。虽然大量实时数据的可用性是一个令人兴奋的技术出现，但我们面临着信息过载的挑战——试图理解大量数据以确定适当的行动方针。

With the advancement in sensor and information technology, organizations are now capable of collecting massive amounts of data continuously and storing them in large databases. The availability of these data makes it possible to solve previously unsolvable problems and to improve existing processes. In particular, the Ontario Government introduced smart metering sensors in all Ontario homes and small businesses back in 2010. Our industrial partner, Energent, believes that the data collected by these sensors contain valuable information that can improve energy consumption during peak hours and redistribute energy supplies to different regions across the province. The problem becomes how to extract this valuable information. While the availability of massive real-time data is an exciting technological advent, we are faced with the challenge of information overload - trying to make sense of the vast amount of data to determine the appropriate course of actions.