POWER VORTEX - Smart Energy Forecasting and Management with IoT and Machine Learning
Power Vortex is an innovative smart power consumption management system designed to replace traditional switchboards with intelligent alternatives. This advanced solution connects various devices, accurately measures their power consumption, and sends the data to a centralized server for analysis. Through a user-friendly mobile app, equipped with cutting-edge machine learning algorithms like LSTM, RNN, and analytics, consumers gain access to real-time energy usage insights, personalized usage reports, and actionable recommendations. In this project, we present the comprehensive design, development, and testing of Power Vortex, highlighting its pivotal role in effectively managing power consumption. Beyond traditional energy management systems, our device harnesses the power of machine learning to predict and provide valuable recommendations to users based on their energy use patterns, preferences, and external factors. These personalized recommendations empower users to make informed decisions, optimize their energy consumption, and reduce costs. Power Vortex demonstrates tangible benefits in terms of both financial savings and environmental impact. By integrating a smart switchboard to connect and monitor devices, it delivers precise and real-time energy consumption data to users. This data-driven approach not only fosters efficient resource utilization, but also supports the larger goal of environmental sustainability. Power Vortex is poised to revolutionize energy management practices, offering a more sustainable future by enabling individuals and organizations to reduce their carbon footprint and create a positive impact on the environment.
Authors
- Soman Shibu
- A. Ashwin
- Adithya Anil
- S. Gopal
- Deepthi K Moorthy
Publication Details
- Published
- 2025-02-07
- DOI
- https://doi.org/10.1109/etis64005.2025.10961294
- Citations
- 1
- Primary Topic
- Energy Load and Power Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 0.73