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Code for paper "Continuous and Distribution-free Probabilistic Wind Power Forecasting: A Conditional Normalizing Flow Approach" https://arxiv.org/abs/2206.02433

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conditional-normalizing-flows deep-learning probabilistic-forecasting wind-power-forecasting

conditional-normalizing-flow-for-wind-power-forecasting's Introduction

Conditional-normalzing-flow-for-wind-power-forecasting

This repo is for paper "Continuous and Distribution-free Probabilistic Wind Power Forecasting: A Conditional Normalizing Flow Approach" (https://arxiv.org/abs/2206.02433).

Authors: Honglin Wen, Pierre Pinson, Jinghuan Ma, Jie Gu, Zhijian Jin

Dependency: pytorch, nflows

Email: [email protected]

Bibtex:

@misc{wen2022continuous, title={Continuous and Distribution-free Probabilistic Wind Power Forecasting: A Conditional Normalizing Flow Approach}, author={Honglin Wen and Pierre Pinson and Jinghuan Ma and Jie Gu and Zhijian Jin}, year={2022}, eprint={2206.02433}, archivePrefix={arXiv}, primaryClass={eess.SY} }

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scutzck033 hanlaoshi shahrzadnikbakhtian

conditional-normalizing-flow-for-wind-power-forecasting's Issues

Dataset

Dear Honglin,

I am a data science master's student at Oslomet University. Your paper titled "Continuous and Distribution-free Probabilistic Wind
Power Forecasting" is relevant to my research area. I am working on a normalizing flow algorithm to forecast wind speed, and it would be great if you helped me on this path. I need to reproduce your work by using my dataset. I am going to forecast the wind speed with the dataset contains forecasted and observed wind speed and the other metrological features. Is that possible to use your algorithm in this regard.
I appreciate your contribution.

Best regards,
Shahrzad

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