Back to Search
Start Over
Feasibility Analysis for Setting up a Solar Plant using IoT and Machine Learning
- Source :
- 2020 International Conference on Advances in Computing, Communication & Materials (ICACCM).
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- An ever growing world population means an ever increasing energy demand. Our non-renewable energy sources are depleting fast. Excessive use of fossil fuels has led towards environmental problems like global warming, Greenhouse effect. Many Countries around the world are still dependent on fossil fuels because renewable sources cannot cater huge energy demand. The world is exploring renewable energy sources to develop technologies in order to enhance energy production capability of these sources. Solar energy is one of the most promising renewable sources that are currently being used worldwide to contribute for meeting rising demands of electric power. Solar power is a conversion of sunlight into electricity using photovoltaic devices. Solar Energy Measurement is very essential in harvesting energy in solar radiation. It is necessary for feasibility analysis of an solar power plant in order to know the suitable location for installing it. Internet of Things provides a smart platform to manage these alternative energy resources since remote monitoring can be done easily and the data can be accessed anywhere in the world over the internet. This paper incorporates a smart system which displays all the parameters of a solar panel by using multiple sensors connected to an Arduino. Using, IoT the data is monitored continuously and then this data is used for predicting feasibility of a site for generation of solar power to determine the suitable site location from which the data is fetched. The proposed system offers great portability and can be very beneficial to choose sites for generation of solar power.
Details
- Database :
- OpenAIRE
- Journal :
- 2020 International Conference on Advances in Computing, Communication & Materials (ICACCM)
- Accession number :
- edsair.doi...........4d4dcadf247dab87976468ce5fefdae9