Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866909299658194944 |
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| author | Sun, Yibin Gomes, Heitor Murilo Pfahringer, Bernhard Bifet, Albert |
| author_facet | Sun, Yibin Gomes, Heitor Murilo Pfahringer, Bernhard Bifet, Albert |
| contents | 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. We conduct extensive analyses and experiments on these datasets, covering preprocessing techniques, regression tasks, prediction intervals, concept drift detection, and anomaly detection. Our experiments demonstrate the datasets' utility and highlight the challenges and opportunities for future research in energy price forecasting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16187 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis Sun, Yibin Gomes, Heitor Murilo Pfahringer, Bernhard Bifet, Albert Machine Learning Artificial Intelligence 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. We conduct extensive analyses and experiments on these datasets, covering preprocessing techniques, regression tasks, prediction intervals, concept drift detection, and anomaly detection. Our experiments demonstrate the datasets' utility and highlight the challenges and opportunities for future research in energy price forecasting. |
| title | Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2408.16187 |