A Visual Analytics Framework for Renewable Energy Profiling and Resource Planning

Abstract

Renewable energy growth is one of the focus areas globally against the backdrop of the global energy crisis and climate change. Energy planners are looking into clean, safe, affordable, and reliable energy generation sources for a net zero future. Countries are setting energy targets and policies prioritizing renewable energy, shifting the dependence on fossil fuels. The selection of renewable energy sources depends on the suitability of the region under consideration and requires analyzing relevant environmental datasets. In this work, we present a visual analytics framework that facilitates users to explore solar and wind energy datasets consisting of Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), Diffusive Horizontal Irradiance (DHI), and Wind Power (WP) spanning across a 40 year period. This framework provides a suite of interactive decision support tools to analyze spatiotemporal patterns, variability in the variables across space and time at different temporal resolutions, Typical Meteorological Year (TMY) data with varying percentiles, and provides the capability to interactively explore and evaluate potential solar and wind energy equipment installation locations and study different energy acquisition scenarios. This work is conducted in collaboration with domain experts involved in sustainable energy planning. Different use case scenarios are also explained in detail, along with domain experts feedback and future directions.