Assessing Solar: An Interactive Best Practices Guide to Solar Resource Assessment

Solar energy has become a key technology in the renewable energy transition. Particularly, the decrease in the cost of solar technologies has led to increasing investments and significant penetration into energy systems. This has, in turn, led to a greater need for reliable and accurate solar resource data. Fortunately, there has been a great effort in advancing the state of the art of solar resource assessment, including enhanced solar radiation models and more accurate solar resource datasets. While these research advances are published in great detail in scientific journals, a large gap is left in the path to widespread adoption by the scientific community and industry. The high complexity of these new methods and models often means that they are, in practice, only available to a small group of experts. To that end, Assessing Solar presents a practical guide to solar resource assessment using the Python programming language. The guide consists of several sections on different topics, which are organized by theme. Each section provides best practices and includes several examples on how to apply the methods to real-life applications, allowing users to understand the code and, with minimal effort, modify it to fit their own applications. The content is written in the Jupyter Notebook format, allowing for an interactive experience and seamless integration of computational content, figures, equations, text, references, and more. The guide is available at AssessingSolar.org.