Solar Powered Car Optimization Through Machine Learning

Abstract

The main goal of the project is to predict the solar power output of the solar panel depending on prevailing weather conditions using some Machine Learning algorithms for the purpose of optimizing and adapting the power consumption so as to strike a good balance between solar power supply and demand. For predicting the power output, the following parameters are measured with the help of an Arduino-based sensor unit -Temperature, Pressure, Light intensity and Relative humidity. Apart from them as well, cloud coverage, visibility, and dew point, and we add wind speed is also taken into consideration which we collect from the weather forecast that help a lot. In order in an attempt to train the Machine Learning algorithms, a dataset using the above parameters will be formed hopefully. As renewable energy is gaining a lot of ground rapidly, it becomes necessary for us interestingly to make solar systems reliable kind of. By predicting the power output of the panel we can plan a Necessary alternative accordingly as well, making the system more reliable indubitably. Solar energy prediction is significant in enhancing the competitiveness of solar power plants in the energy market indeed, and decreasing reliance on fossil fuels in socio-economic development largely. Our work is aiming to accurately predict the solar energy. IndexTerms–MachineLearning, SolarPower, Arduino Uno, PythonJupyterNotebook, Sensors.