Machine learning applications for the optimization of renewable energy systems

Abstract

This thesis aims to establish the sectors and applications of renewable energy. The applications of sustainable energy have grown every year in different uses. Three separate studies stand out as a source of creativity. Together, they point the way to a better and more accountable future. Each study explores an important aspect of energy optimization and environmental stewardship and offers remarkable insights and responses. This thesis begins with climate simulation, concentrating on projected solar irradiation and wind patterns in the following decades. These estimations emphasize preemptive energy management. This study compares Saudi Arabia's metropolitan solar power systems to wind and fossil fuel infrastructure. Current and future climates are analyzed. The company's recent solar energy strategy shows its ability to produce sustainable energy and reduce climate change. Wastewater is the topic of the second part of this thesis. Wastewater treatment is a priority due to rising water needs and environmental concerns. Here, renewable energy sources and electrochemical technologies are combined to improve wastewater treatment efficiency and save operational costs. The study uses machine learning predictive models and extraction of high-value by-products from the treatment process blends technology and ecology, enabling sustainable water management. The third study project, which explores the complicated field of predictive modeling, is essential to prioritize the accurate projection of energy output given the rising importance of renewable energy. This research paper thoroughly examines the effectiveness of statistical and machine-learning approaches in predicting renewable energy generation. The findings demonstrate the prevalence of machine learning techniques, which bring a sense of innovation and efficacy to the realm of renewable energy fields.