How difficult is to “catch” those correlations and those grouped variations? And also how difficult is to correlate and combine in time not only profiles but metrics or KPIs (Key Performance Indicators) that may be correlated in the background? This trend analytics definition is called “Insights”.
Hence, a smart grid system is full of heterogeneous flowing variable data that may come from smart meters or other external or internal sources (weather, behaviors, building data, maintenance plans, RES, mobility etc). This data flow creates variable dynamic models that are continuously changing and create ad-hoc variations in between them; ie a critical peak in a building may be correlated with a local climate condition, or with a faulty HVAC device, or with a bad human energy behavior or even with a wrong energy efficiency maintenance/action plan.
Based on the above assumption, we need an efficient, fast and accurate methodology to drive decisions and observe the correct instant decision paths inside a variety of “continuous moving interconnected data”.
Real-time or daily updating of hourly energy or utility consumption data (water, gas, EVs, building efficiency, etc) allows users and investment players to evaluate customer energy performance that are otherwise difficult to observe in a time-variant philosophy.
You can read the full paper here