Predicting smart distance till empty for solar vehicle users

Our main idea is to predict that the energy consumption and optimization of solar car. The solar car contains photovoltaic cells (PV).It generates current based on heat generated on solar car. It analyses based on heat shows the power consumption and predicts that how much distance the solar car can travel. Each individual driver has having own driving patterns and characteristics. For example if a driver drives at a certain speed and applies a sudden break, our project predicts that how much distance he can travel at the speed and how much amount the electricity is wasted. There are three data characteristics affecting driver’s pattern. The factors that might affect the power consumption are weather condition, road characteristics, driving characteristics. With the above said characteristics, we can predict that how much driver can drive for a distance. The most determinants of energy efficiency found to be driving patterns, variations in driving, temperature. And the data inputted with the help of Tensor flow. The user will input the data through the tensor flow and the tensor flow will predict the outcome through the graph of a Numpy. And the numpy will store the output in the database.