Identifying price index classes for electricity consumers via dynamic gradient boosting

Vanh Khuyen Nguyen*, Wei Emma Zhang, Quan Z. Sheng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

1 Citation (Scopus)

Abstract

Electricity retailers buy electricity at spot prices and resell energy to their customers at fixed retail prices. However, the electricity market is complex with highly volatile spot prices, and high price events might happen during peak time periods when energy demand significantly increases, leading to the decision of the retail price a challenging task. Understanding consumer price index, a price indicator that is associated with electricity consumption of customers helps energy retailers make critical decisions on pricing strategy. In this work, we apply dynamic gradient boosting model, namely CatBoost, to classify customers into different groups according to their price indices. To benchmark our results, we compare the performance of CatBoost with other baselines, including Random Forest, AdaBoost, XGBoost, and LightGBM. Our experimental results proved that CatBoost outperformed other algorithms due to its effective overfitting detector and categorical encoding techniques. Besides, the area under the curve of the Receiver Operating Characteristics (ROC), often known as AUC, is used as a standard measure metric to evaluate and compare between classifiers. Hence, CatBoost gained the lowest difference score of 0.02 between train AUC and test AUC scores that successfully competed other models.

Original languageEnglish
Title of host publicationWeb Information Systems Engineering – WISE 2018
Subtitle of host publication19th International Conference, 2018, Proceedings, Part I
EditorsHakim Hacid, Wojciech Cellary, Hua Wang, Hye-Young Paik, Rui Zhou
Place of PublicationCham
PublisherSpringer-VDI-Verlag GmbH & Co. KG
Pages472-486
Number of pages15
ISBN (Electronic)9783030029227
ISBN (Print)9783030029241
DOIs
Publication statusPublished - 1 Jan 2018
Event19th International Conference on Web Information Systems Engineering, WISE 2018 - Dubai, United Arab Emirates
Duration: 12 Nov 201815 Nov 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11234 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Web Information Systems Engineering, WISE 2018
CountryUnited Arab Emirates
CityDubai
Period12/11/1815/11/18

Keywords

  • CatBoost
  • Classification learning
  • Gradient boosting model

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