Machine learning applications for the optimization of renewable energy systems
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Date
2023-10-10
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Saudi Digital Library
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.
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Keywords
Renewable energy, climate change forecasting, machine learning, wastewater treatment, direct normal irradiance, wind speed