Here we analyze 75 balancing authority areas (BAA) in the United States, covering the years 2000 through 2023. For each year and load zone, we have trained both a multi-linear regression model and the XGBoost machine learning model to predict electrical load obtained from independent system operators and from the U.S. Energy Information Agency using temperature, global horizontal irradiance and hour of the week, temperature and GHI are derived from the ERA5 reanalysis. The linear model parameters, and linearized output from the trained XGBoost model are used to diagnose the complexity of the forecast problem in each BAA, and to diagnose trends in the dependence of each BAA’s load on the various predictors, reflecting changes in end-use infrastructure in each BAA.