# Scene Learning: Deep Convolutional Networks For Wind Power Prediction by Embedding Turbines into Grid Space

arXiv: https://arxiv.org/abs/1807.05666 · HTML: https://mlsanity.com/paper/1807.05666/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2018.

## Built on by (most cited first, 3 of 3)

- [Deep Learning for Energy Time-Series Analysis and Forecasting](https://mlsanity.com/paper/2306.09129/) (2023)
- [2DXformer: Dual Transformers for Wind Power Forecasting with Dual Exogenous Variables](https://mlsanity.com/paper/2505.01286/) (2025)
- [Windformer:Bi-Directional Long-Distance Spatio-Temporal Network For Wind Speed Prediction](https://mlsanity.com/paper/2311.14316/) (2023)

## Abstract (arXiv)

Wind power prediction is of vital importance in wind power utilization. There have been a lot of researches based on the time series of the wind power or speed, but In fact, these time series cannot express the temporal and spatial changes of wind, which fundamentally hinders the advance of wind power prediction. In this paper, a new kind of feature that can describe the process of temporal and spatial variation is proposed, namely, Spatio-Temporal Features. We first map the data collected at each moment from the wind turbine to the plane to form the state map, namely, the scene, according to the relative positions. The scene time series over a period of time is a multi-channel image, i.e. the Spatio-Temporal Features. Based on the Spatio-Temporal Features, the deep convolutional network is applied to predict the wind power, achieving a far better accuracy than the existing methods. Compared with the starge-of-the-art method, the mean-square error (MSE) in our method is reduced by 49.83%, and the average time cost for training models can be shortened by a factor of more than 150.