Forecasting energy poverty using different machine learning techniques for Missouri

Energy poverty in Missouri was analyzed using the four quadrant approach using both state and county level data sets for two separate definitions of the grid. Predictions used machine learning techniques including decision trees, random forest, extreme gradient boosting and support vector machines. It was determined that the extreme gradient boosting performed the best when compared to all the other models after the hyperparameters were tuned. The F1 scores for the county level data sets were higher than for the state levels thus indicating greater predictability for the National Oceanic and Atmospheric Administration (NOAA) climatological regional runs. For the county level data, the F1 score was the highest for region 1, which coincided with one of the highest expenditure risk values whilst regions 2–4 were the lowest scoring area. In grid 2, the largest class distribution changed from grid 1’s expenditure risk to the no risk category. This grid had more variability in terms of the double risk class when compared to grid 1 and, as such, its predictability in terms of its F1 scores was reduced. There were similarities in the ranking of the prediction scores for the regions for both grids as regions 1 and 6 incurred the largest F1 values. Thus energy poverty can be classified and predicted for Missouri, which in turn may aid policy makers via quantitative regional risk analysis. This data-driven informed policy making can lead to the development and implementation of laws and social programs to help ameliorate energy poverty.