Publication Type : Conference Proceedings
Publisher : Procedia Computer Science,
Source : Procedia Computer Science, Volume 165, p.90-96 (2019)
Url : https://www.sciencedirect.com/science/article/pii/S1877050920300831
Keywords : Data mining, Decision Tree, energy management system, Machine learning, Solar power management
Campus : Bengaluru
School : School of Engineering
Department : Electrical and Electronics
Year : 2019
Abstract : Electricity from solar energy is the new trend in electricity production companies. Many plants have been set up to harness energy from the sun. Since solar energy can be harnessed from open-air, starting the same from home is the best way. Most commonly solar energy is harnessed from a rooftop-mounted solar panel but is connected only for a few selective loads. All the other loads when in need of electricity cannot be utilized from solar energy. Since this is the format for electricity usage, the maximum usage of this is not done. This framework is proposed to maximize the usage of solar power by connecting both solar and grid to the same node and employing a unique switching strategy using decision tree machine learning algorithm in python environment. The hardware setup has been done for the same by using data acquisition techniques, collecting real-time data consisting of demand and solar power which is updated into the database in the local setup server. This real-time data is exported and prediction for the switching configuration is done and sent to an Arduino board to predict the source that has to be given to the particular demand in real-time.
Cite this Research Publication : M. Gautam, Raviteja, S., and Mahalakshmi, R., “Household Energy Management Model to Maximize Solar Power Utilization Using Machine Learning”, Procedia Computer Science, vol. 165. pp. 90-96, 2019.