NPV analysis & prediction of SRES with python tools

This paper highlights a Solar energy generation plant as Renewable energy source (SRES) on the criterion of cost involvement using net present value (NPV) method and its payback period. The Homer app is also used for analyzing a selected SRES system. The various big data analysis tools of python are discussed,which could be utilized effectively to analyze RES based on predicting the outcomes, like maximum generation capacity, optimum generation month, weather conditions and performance and many more. System integrity and SRES economic considerations are critically evaluated for an educational institution. The cost analysis using NPV method is included with the existing MAHADISCOM as one of the projects and SRES as another project. Both the projects are analyzed by assuming the lifespan of 11 years. The payback period of the SRES system is 3.3 years and hence after that, the energy will be available for free of cost through the mentioned system. This analysis is done for the site located in the state of Maharashtra, India, and can be used as a case study for engineering students for future perspectives. The tools used for data analysis of a RES project are found very useful for predictive analysis. In the present work Matplotlib and Seaborn tools of python are used for prediction of seasonal effect on generation of power. The new innovative method of big data analysis using python programming is applied to a case study and results are presented successfully.