Data Analysis Approach for Large Data Volumes in a Connected Community

摘要: Recent advancements within smart neighborhoods where utilities are enabling automatic control of appliances such as heating, ventilation, and air conditioning (HVAC) water heater (WH) systems providing new opportunities to minimize energy costs through reduced peak load. This requires systematic collection, storage, management, in-memory processing large volumes streaming data for fast performance. In this paper, we propose a multi-tier layered IoT software framework that enables effective descriptive predictive analysis understanding live operation the neighborhood, fault identification, future further optimization load curves. We then demonstrate how achieve situational awareness connected neighborhood suite visualization components. Finally, discuss few analytic dashboards address questions reductions obtained due optimization, customer preference (do they override HVAC?, etc.). 1 1This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with U.S. Department Energy. The United States Government retains publisher, accepting article publication, acknowledges nonexclusive, paid-up, irrevocable, world-wide license publish or reproduce published form manuscript, allow others do so, purposes. Energy will provide public access these results federally sponsored research in accordance DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).