This case study presents the underlying motivation and representative insights from an electric cooperative net demand planning exercise that utilized a long-term (15-year) correlated climate simulation and machine learning-based energy resource modeling. The approach described here overcomes several challenges related to net demand planning and resource adequacy analysis, namely– 1) the creation of a large sample of properly jointly distributed weather, 2) the incorporation of climate change trends, 3) the creation of a typical hourly net demand path, and 4) the creation (and curation) of extreme–but realistic–weather and energy scenarios across a utility footprint.