Solar-Powered Vehicle Energy Generation Forecasting

Abstract

Solar-powered vehicles are anticipated to have a significant impact on transportation in the near future, owing to developments in solar energy harvesting technologies. Nevertheless, there are significant obstacles that must be overcome in order to make solar-powered vehicles a feasible and viable option. One such problem is the requirement for precise energy generation forecasting. Accurately forecasting the energy production for each trip is crucial to guarantee the continuous functioning of a solar-powered vehicle. Regrettably, current energy generation forecasting techniques are predominantly tailored for fixed solar panels and are inadequate for predicting energy generation in mobile, solar-powered vehicles. This paper proposes a method for predicting the energy produced by solar-powered cars while they are moving, together with a computerized user interface for delivering pertinent information. The process encompassed the identification of all variables influencing solar energy generation and the acquisition of up-to-the-minute data via application programming interfaces (APIs). This approach has the potential for worldwide use, as it takes into account route, topography, and weather data. Its accuracy was evaluated across several routes, days, hours, speeds, and precision levels in a specific area in Sri Lanka, using local route, terrain, and weather data. The findings demonstrated that implementing the procedure on a global scale yields a notable level of precision at a reduced computational timeframe, whereas implementing it locally produces comparable accuracy but necessitates a relatively longer computational duration.