Complex terrains and wind power: enhancing forecasting accuracy through CNNs and DeepSHAP analysis.

Accurate prediction of wind power generation in regions characterised by complex terrain is a critical gap in renewable energy research. To address this challenge, the present study articulates a novel methodological framework using Convolutional Neural Networks (CNNs) to improve wind power forecasting in such geographically diverse areas. The core research question is to investigate the extent to which terrain complexity affects forecast accuracy. To this end, DeepSHAP -- an advanced interpretability technique -- is used to dissect the CNN model and identify the most significant features of the weather forecast grid that have the greatest impact on forecast accuracy. Our results show a clear correlation between certain topographical features and forecast accuracy, demonstrating that complex terrain features are an important part of the forecasting process. The study's findings support the hypothesis that a detailed understanding of terrain features, facilitated by model interpretability, is essential for improving wind energy forecasts. Consequently, this research addresses an important gap by clarifying the influence of complex terrain on wind energy forecasting and provides a strategic pathway for more efficient use of wind resources, thereby supporting the wider adoption of wind energy asa sustainable energy source, even in regions with complex terrain. [ABSTRACT FROM AUTHOR]