Summary of Real-time Energy Pricing in New Zealand: An Evolving Stream Analysis, by Yibin Sun et al.
Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis
by Yibin Sun, Heitor Murilo Gomes, Bernhard Pfahringer, Albert Bifet
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a set of novel datasets containing real-time time-series and streaming data on energy prices in New Zealand, sourced from the Electricity Market Information (EMI) website. The datasets aim to address the scarcity of suitable datasets for regression learning tasks with streaming data. The authors conduct extensive experiments, exploring preprocessing techniques, regression tasks, prediction intervals, concept drift detection, and anomaly detection on these datasets. Results demonstrate the datasets’ utility and highlight the challenges and opportunities for future research in energy price forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates new datasets that show real-time energy prices in New Zealand over time. The goal is to help with a problem of not having enough good data for learning tasks like this. The authors test different techniques on these datasets, including preparing the data, making predictions, finding patterns, and detecting unusual changes. The results show that these datasets can be useful and point out areas where more research is needed. |
Keywords
» Artificial intelligence » Anomaly detection » Regression » Time series
