Analytical study of the consumption of photovoltaic plants by means of Python programming

Abstract

Solar PV technology continues to evolve and is expected to become one of the leading sources of green energy in the future. To achieve this, it is crucial to analyse the current performance of photovoltaic (PV) plants and identify potential improvements. DNV's Solar Technology & Analytics department conducts analytical studies on both operational solar plants and projects in development, extracting key performance indicators (KPIs) and predictive insights. However, these studies are stored separately in individual reports, limiting the ability to perform large-scale comparative analysis and benchmarking. This Bachelor’s Degree Project aims to develop a centralised database to compile and compare the performance of solar plants across different regions, including Europe, the Middle East, Africa and America. Using Python and Pandas, the system will automate data integration from multiple projects, enabling a more efficient analysis of KPIs, degradation rates, and operational challenges. With this tool, the company DNV and its clients will be able to enhance decision-making, detect anomalies, and optimise PV plant performance. The final goal is to establish a data-driven methodology to support the development of future solar projects.