TY - GEN
T1 - Negative Price Forecasting in Australian Energy Markets using gradient-boosted Machines
T2 - 14th IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023
AU - Kaur, Devinder
AU - Aujla, Gagangeet Singh
AU - Mahmud, Md Apel
PY - 2023/12/6
Y1 - 2023/12/6
N2 - 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.
AB - 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.
KW - Australian energy markets
KW - battery storage systems
KW - light gradient-boosted machines
KW - negative pricing
KW - quantile regression
KW - renewable energy generation
UR - http://www.scopus.com/inward/record.url?scp=85180788786&partnerID=8YFLogxK
U2 - 10.1109/SmartGridComm57358.2023.10333879
DO - 10.1109/SmartGridComm57358.2023.10333879
M3 - Conference contribution
AN - SCOPUS:85180788786
SN - 978-1-6654-5554-1
T3 - 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023 - Proceedings
BT - 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers
Y2 - 31 October 2023 through 3 November 2023
ER -