Enhancing solar energy systems: The role of computational and automation techniques in achieving net zero by 2050
Year: 2024
Type: article
Abstract: This review aims to show how computational and automation can be applied to optimize the solar power system toward net-zero emissions in 2050. It emphasizes the power of data analytics, machine learning and automated systems for optimizing business models and measuring the performance of solar technology. The role of solar power in decarbonizing our energy needs and lowering carbon emissions is discussed along the global climate agenda. It also discusses novel uses such as self-teaching robots for solar panel inspection and automated manufacturing to increase the efficiency and sustainability of solar power generation. Additionally, it addresses the need for sophisticated research tools such as computational fluid dynamics (CFD) and finite element analysis (FEA) to create new technologies in the solar market and ultimately meet the ambitious renewable energy targets of 2050.
Author: Parankush Koul
Institution: Illinois Institute of Technology
Language: English
Subfield: Energy Engineering and Power Technology
Field: Energy
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4403572134