Wind speed prediction using multidimensional convolutional neural networks
This work addresses wind forecasting for economic and management sectors, but it is incremental as it builds on existing CNN methods with a novel multidimensional approach.
The paper tackles wind speed prediction by introducing a multidimensional convolutional neural network model that better characterizes spatio-temporal evolution from multiple data views, achieving improved accuracy compared to traditional CNN models on real-life weather datasets from Denmark and the Netherlands.
Accurate wind speed forecasting is of great importance for many economic, business and management sectors. This paper introduces a new model based on convolutional neural networks (CNNs) for wind speed prediction tasks. In particular, we show that compared to classical CNN-based models, the proposed model is able to better characterise the spatio-temporal evolution of the wind data by learning the underlying complex input-output relationships from multiple dimensions (views) of the input data. The proposed model exploits the spatio-temporal multivariate multidimensional historical weather data for learning new representations used for wind forecasting. We conduct experiments on two real-life weather datasets. The datasets are measurements from cities in Denmark and in the Netherlands. The proposed model is compared with traditional 2- and 3-dimensional CNN models, a 2D-CNN model with an attention layer and a 2D-CNN model equipped with upscaling and depthwise separable convolutions.