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Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis

  • Yibin Sun
  • , Heitor Murilo Gomes
  • , Bernhard Pfahringer
  • , Albert Bifet
  • University of Waikato
  • Victoria University of Wellington

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

1 Citation (Scopus)

Abstract

This paper introduces a group of novel datasets representing real-time time-series and streaming data of energy prices in New Zealand, sourced from the Electricity Market Information (EMI) website maintained by the New Zealand government. The datasets are intended to address the scarcity of proper datasets for streaming regression learning tasks. Our experiments demonstrate the datasets’ utility and highlight the challenges and opportunities for research in energy price forecasting.

Original languageEnglish
Title of host publicationPRICAI 2024
Subtitle of host publicationTrends in Artificial Intelligence - 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, Proceedings
EditorsRafik Hadfi, Takayuki Ito, Patricia Anthony, Alok Sharma, Quan Bai
PublisherSpringer Science and Business Media Deutschland GmbH
Pages91-97
Number of pages7
ISBN (Print)9789819601271
DOIs
Publication statusPublished - 1 Jan 2025
Event21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024 - Kyoto, Japan
Duration: 18 Nov 202424 Nov 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15285 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024
Country/TerritoryJapan
CityKyoto
Period18/11/2424/11/24

Keywords

  • Anomaly Detection
  • Data Streams
  • Datasets
  • Prediction Interval
  • Regression

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