# Machine learning methods for GEFCom2017 probabilistic load forecasting

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/machine-learning-methods-for-gefcom2017-probabilistic-load-forecasting/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Slawek Smyl,Ning Hua |
| Citations | 29 |
| DOI | 10.1016/j.ijforecast.2019.02.002 |
| Fields | Computer Science,Decision Sciences,Engineering |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W2944327260 |
| Type | article |
| Year | 2019 |

## Paper authors

- [Ning Hua](https://scholariq.org/researchers/ning-hua/)

## Paper primary topic

- [Energy Load and Power Forecasting](https://scholariq.org/topics/energy-load-and-power-forecasting/)

## Paper topics

- [Energy Load and Power Forecasting](https://scholariq.org/topics/energy-load-and-power-forecasting/)
- [Image and Signal Denoising Methods](https://scholariq.org/topics/image-and-signal-denoising-methods/)
- [Grey System Theory Applications](https://scholariq.org/topics/grey-system-theory-applications/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.