Hyperparameter optimized classification pipeline for handling unbalanced urban and rural energy consumption patterns

Energy consumer locations are required for framing effective energy policies. However, due to privacy concerns, it is becoming increasingly difficult to obtain the locational data of the consumers. Machine learning (ML) based classification strategies can be used to find the locational information of the consumers based on their historical energy consumption patterns. The ML methods in this paper are applied to the Residential Energy Consumption Survey 2009 dataset. In this dataset, the number of consumers in the urban area is higher than the rural area, thus making the classification problem unbalanced. The unbalanced classification problem has been solved in original and transformed or reduced feature space using Monte Carlo based under-sampling of the majority class datapoints. The hyperparameters for each classification algorithm family is represented as an optimized pipeline, obtained using the genetic programming (GP) optimizer. The classification performance metrics are then ob- tained for different algorithm families on the original and transformed feature spaces. Performance comparisons have been reported using univariate and bivariate distributions of the classification metrics viz. accuracy, geo- metric mean score (GMS), F1 score, precision, area under the curve (AUC) of receiver operator characteristics (ROC). The energy policy aspects for the urban and rural residential consumers based on the classification results have also been discussed.

Automated summary

This paper applies machine learning methods to obtain the locational information of energy consumers based on their historical energy consumption patterns. The author tackles the issue of unbalanced classification problem for the dataset and uses Monte Carlo based under-sampling and genetic programming optimizer to optimize and compare the classification algorithms. The classification performance metrics are evaluated and the energy policy implications for urban and rural consumers are discussed.