Machine Learning and Deep Learning Models for Short Term Electricity Price Forecasting in Australia's National Electricity Market
School of Engineering, Computer & Mathematical Sciences, Auckland University of Technology (AUT)（奥克兰技术大学工程、计算机与数学科学学院）；Department of Thermal Energy and Power Engineering, Yantai University（烟台大学热能与动力工程系）；Department of Building Environment and Equipment, School of Civil Engineering, Hefei University of Technology（合肥工业大学土木工程学院建筑环境与设备系）；Green Gold Energy Pty Ltd
Wei Lu, Jay Wang, Dingli Duan, Ding Mao, Caiyi Song, John Huang
Short term electricity price forecast is essential in competitive power markets, yet electricity price series exhibit high volatility, irregularity, and non-stationarity. This phenomenon is pronounced in the South Australian region of the National Electricity Market, where high renewable penetration drives price volatility and frequent negative price intervals, while structural changes such as the transition to five-minute settlement further complicate forecast. To address these challenges, this study develops a unified benchmark framework. Under identical data preprocessing, feature engineering with lag features, rolling statistics, cyclic temporal encodings, and so on, and an 85% to 15% chronological train test split, six algorithms are systematically compared, including AWMLSTM, CatBoost, GBRT, LSTM, LightGBM, and SVR. The results show that for price prediction, tree-based models, especially GBRT with an R squared value of 0.88, generally outperform LSTM and SVR. However, all models achieve a mean absolute percentage error above 90%, and more than 65% of GBRT predictions have relative errors above 10%, which highlights the inherent difficulty of price forecast. For demand prediction, all models perform substantially better than in price prediction. AWMLSTM and GBRT achieve an R2 value of 0.96 with mean absolute percentage error below 32%, and GBRT has 74.37% of samples within 5% error, while LSTM and SVR perform less accurately in both tasks. Future improvements should focus on hybrid models such as tree plus transformers, data augmentation for extreme events, and error correction to better capture price spikes.
Hybrid Kolmogorov-Arnold Network and XGBoost Framework for Week-Ahead Price Forecasting in Australia's National Electricity Market
Department of Data Science and AI, Faculty of IT, Monash University, Australia（数据科学与人工智能系，IT学院，墨尔本大学，澳大利亚）；School of Electrical Engineering and Computer Science, University of Queensland, Australia（电气工程与计算机科学学院，昆士兰大学，澳大利亚）；Monash Energy Institute, Monash University, Australia（墨尔本能源研究所，墨尔本大学，澳大利亚）
Beyond Weather Correlation: A Comparative Study of Static and Temporal Neural Architectures for Fine-Grained Residential Energy Consumption Forecasting in Melbourne, Australia
School of Computing and Information Technology（计算与信息科技学院）
Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning
University of New South Wales（新南威尔士大学）；CSIRO（联邦科学与工业研究组织）
Short-term electricity load forecasting with multi-frequency reconstruction diffusion
School of Computer Science and Engineering, Macau University of Science and Technology（计算机科学与工程学院，澳门科技大学）；Macau University of Science and Technology Zhuhai MUST Science and Technology Research Institute（澳门科技大学珠海 MUST 科技研究院）；School of Computer Science, University of Nottingham Ningbo China（Nottingham 中国宁波大学计算机学院）；Department of Engineering Science, Macau University of Science and Technology（工程科学系，澳门科技大学）
