The use of artificial neural networks and big data infrastructure for predictive analytics in solar energy

Abstract

Abstract Renewable energy appears to be the solution to both the continuously growing energy demand, and pollution from fossil-based fuels. Recent advances in big data means that crucial areas of the energy supply chain are of interest to the use of advanced analytics. Solar energy is one of the most important renewable energy sources; however, it is stochastic, leading to production volatility and making it difficult to dispatch. The European Commission provides the legal framework and guidelines for increasing the adoption of renewable technologies in the European Union (EU). Meanwhile, the research community must provide solutions for increasing the predictability of solar energy: successful integration depends on how well solar energy production is predicted. Working under the Cross-Industry Standard Process for Data Mining, using real word operational data, this research focuses on providing a foundation of the analytics capabilities needed for reducing, or even removing, the disadvantages of solar energy, demonstrating that a world-class predicative tool can be obtained. Using weather and production data from photovoltaic cells installed in Romania, as a case study, coupled with the powerful artificial neural networks (ANN) architecture, results in a benchmark prediction performance. Currently, there is no research addressing photovoltaic energy production prediction by integrating the impact of artificial intelligence and big data.