2024 ASEE Annual Conference & Exposition

The advancement of Data Analysis technologies with visualization has gained significant ground in the industries and they are also gaining ground in higher education curriculum. This research will focus on the application of these techniques to the energy industry – in particular, solar renewable energy generation in all the States in the United States (US). One strong opposing action against the progression of climate change is the use of renewable energy.
The objective of the research is to develop a case study on renewable solar energy and its impact on abating or preventing CO2 emissions to help reduce the severe impacts of climate change in the United States and demonstrate how it supports the 3Es of sustainability at the same time. This research paper will specifically explore the past production of solar energy in all the states in the US, and with the use of data analysis tools will predict the production to the year 2030. The reduction of CO2 emissions with the use of renewable solar energy is in direct support of the three elements of sustainability, namely the 3Es: Environment, Economics, and Equity (or social justice). This research will quantify the past benefits already realized in all these three areas for solar energy, and project them up to 2030.
Cluster analysis technique will be applied to solar generation across all US States to identify group(s) at distinct levels of production. This can help States to follow the leading State(s) policy and process to increase their solar generation and thus help manage climate change. Solar energy generation challenges and recycling issues of solar equipment will also be addressed.
This case study approach will fill a gap that currently exists in engineering education when it comes to exploring renewable energy and its sustainability benefits with modern data analysis tools. This research will use publicly available data sources (e.g., NREL, EIA), ubiquitous Excel, and open-source data analytics and prediction tool Orange (for K-Means clustering analysis), so this type of case study approach could be taught and engage engineering students without any barrier. For highly effective visualization, the use of Tableau is also demonstrated.