Negative Price Forecasting in Australian Energy Markets using gradient-boosted Machines: Predictive and Probabilistic Analysis

Devinder Kaur, Gagangeet Singh Aujla, Md Apel Mahmud

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

2 Citations (Scopus)

Abstract

With the integration of distributed energy resources such as roof-top solar panels and wind turbines into the grid, power generation can surpass demand-generation and thus, giving rise to the negative pricing, especially in the summer months. In this regard, a scientific case study is conducted in this paper to analyse and predict the increasing instances of negative energy prices against demand-generation in Australian energy markets (AEMs) using real-time energy data from the Hornsdale power reserve, South Australia. A robust machine learning method, Light gradient boosting machine (LightGBM) is utilised to detect and predict negative prices at different quantiles to quantity the outliers in the pricing data. The implementation results demonstrate that predicting the prices at different quantiles can tackle outliers (negative prices) effectively with the help of extracted upper and lower bounds using quantile regression-based approach. The case study is further extended to learn the complex statistical relationships between different data features using Naive-Bayes Tree Augmented (NB-TAN) algorithm considering 'price' as the dependent feature against the independent features such as demand-generation, battery charging/discharging, and frequency control ancillary services.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)978-1-6654-5556-5, 978-1-6654-5555-8
ISBN (Print)978-1-6654-5554-1
DOIs
Publication statusPublished - 6 Dec 2023
Externally publishedYes
Event14th IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023 - Glasgow, United Kingdom
Duration: 31 Oct 20233 Nov 2023

Publication series

Name2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023 - Proceedings

Conference

Conference14th IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023
Country/TerritoryUnited Kingdom
CityGlasgow
Period31/10/233/11/23

Keywords

  • Australian energy markets
  • battery storage systems
  • light gradient-boosted machines
  • negative pricing
  • quantile regression
  • renewable energy generation

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