This study investigates the impact of spatiotemporal data dimensions on the precision of a wind forecasting model developed using an artificial neural network. Although previous studies have shown that incorporating spatial data can enhance the accuracy of wind forecasting models, few investigations have explored the extent of the improvement owing to different spatial scales in neural network-based predictive models. Additionally, there are limited studies on the optimal temporal length of the input data for these models. To address this gap, this study employs data with various spatiotemporal dimensions as inputs when forecasting wind using 3D-Convolutional Neural Networks (3D-CNN) and assesses their predictive performance. The results indicate that using spatial data of the surrounding area for 3D-CNN training can achieve better predictive performance than using only single-point information. Additionally, multi-time data had a more positive effect on the predictive performance than single-time data. To determine the reasons for this, correlation analyses were used to determine the impact of the spatial and temporal sizes of the training data on the prediction performance. The study found that as the autocorrelation coefficient (ACC) decreased, meaning that there was less similarity over time, the prediction performance decreased. Furthermore, the spatial standard deviation of the ACC also affects the prediction performance. A Pearson correlation coefficient (PCC) analysis was conducted to examine the effect of space on the prediction performance. Through the PCC analysis, we show that local geometric and seasonal wind conditions can influence the forecast capability of a predictive model.

翻译：本研究探讨了时空数据维度对基于人工神经网络的风力预测模型精度的影响。尽管先前研究表明融入空间数据可提升风力预测模型的准确性，但鲜有研究探究不同空间尺度对神经网络预测模型改进程度的贡献。此外，关于此类模型输入数据最优时间长度的研究也较为有限。为弥补这一研究空白，本研究在运用三维卷积神经网络（3D-CNN）进行风力预测时，采用不同时空维度的数据作为输入，并评估其预测性能。结果表明，利用周边区域空间数据进行3D-CNN训练可取得优于仅使用单点信息的预测性能。此外，多时次数据对预测性能的积极影响显著高于单时次数据。为厘清其原因，本研究通过相关性分析确定训练数据时空维度对预测性能的影响机制。研究发现：随着自相关系数（ACC）降低（即时间序列相似性减弱），预测性能相应下降；同时，ACC的空间标准差亦影响预测性能。采用皮尔逊相关系数（PCC）分析空间因素对预测性能的影响，结果表明局地几何特征与季节性风况可显著影响预测模型的预报能力。