Hyperparameter optimized classification pipeline for handling unbalanced urban and rural energy consumption patterns. (15th March 2023)

• Record Type: Journal Article
• Authors: Kumar Panda, Deepak; Das, Saptarshi; Townley, Stuart
• Is Part Of: Expert systems with applications. Volume 214(2023)
• Publication Date: 2023-03-15
• Subjects: Residential energy consumption -- Unbalanced data classification -- ROC curve -- Genetic programming; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.119127
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Unbalanced residential energy consumption data classification problem is tackled. Urban vs rural energy consumption is classified in original and reduced feature space. Monte Carlo under-sampling is used to obtain distribution of classification performance metrics. Six family of classifiers were compared using 6 classification metrics for unbalanced data. Hyperparameter tuning and optimal classifier pipeline were obtained using the genetic programming optimizer. Abstract: 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 obtained 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, geometric 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.