Modeling Real Time Energy Utilization Patterns as a Scalable Sensing Systems Employing Social Media
Keywords:Social media networks, Smart meters, Weather based model, Big data, Machine Learning
The theory of this undertaking is that subjects, communicated through huge scope web-based media organizations, rough power usage occasions (e.g., utilizing high force utilization gadgets like a dryer) with high precision. Customarily, scientists have proposed the utilization of keen meters to demonstrate gadget specific power usage designs. In any case, these methods experience the ill effects of versatility and cost difficulties. To alleviate these difficulties, we propose an online media network-driven model that uses huge scope text based and geospatial information to estimated power usage designs, without the requirement for actual equipment frameworks (e.g., like keen meters), therefore giving a promptly versatile wellspring of information. The technique is approved by considering the issue of power use disaggregation, where energy utilization rates from a nine-month duration in San Diego, combined with 1.8 million tweets from a similar area and interval of time, are used to consequently decide exercises that require huge or modest quantities of power to achieve. The framework decides 200 points on which to identify power related occasions and finds 38 of these to be substantial descriptors of energy usage. At long last, the generalizability of our model is contrasted and a climate based model, given by the U.S. Branch of Energy.
How to Cite
Copyright (c) 2021 Priyanka Kote, M. Dhananjay
This work is licensed under a Creative Commons Attribution 4.0 International License.