# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
## 2 Artificial Intelligence for Wind Speed-Power Forecasting  
Artificial intelligence has significantly enhanced the accuracy of wind speed predictions using large datasets from wind stations. One notable approach is the Convolutional Support Vector Machine (CSVM), which combines Support Vector Machines (SVMs) with Convolutional Neural Networks (CNNs) [44; 45]. This CSVM model has demonstrated the effectiveness of wind speed prediction by integrating these advanced analytical techniques. Additionally, deep learning techniques such as Recurrent Neural Networks (RNNs) have been successfully utilized. Combining wavelet transforms with updated Long Short-Term Memory (LSTM) networks has further enhanced the accuracy of prediction, showcasing the potential of integrating advanced signal processing with deep learning methodologies [46; 47]. Moreover, developing a unified framework that merges CNNs with LSTMs has facilitated the joint learning of temporal and spatial correlations in wind speed forecasting, thereby improving overall model performance [48]. Recent advancements in wind speed and power prediction models have increasingly focused on leveraging transformer architectures due to their superior ability to handle sequential data and capture long-term dependencies. [PERSON] et al. proposed a transformer-based deep neural network with wavelet transform, demonstrating significant improvements in forecasting wind speed and energy, when compared to traditional models such as LSTM [49]. Similarly, [PERSON] et al. explored spatiotemporal wind speed forecasting using graph networks in conjunction with novel transformer architectures, which allowed for more accurate multi-step predictions over varying future intervals [50]. In another study, [PERSON] et al. introduced a hybrid wind speed forecasting model that combines ICEEMDAN and transformer networks with a novel loss function, effectively addressing the challenges posed by seasonal and stochastic wind variations [51]. Further extending these concepts, [PERSON] et al. developed a model that integrates both high- and low-frequency components using Transformer and BiGRU-Attention mechanisms, achieving robust power prediction performance across different time scales [52]. These studies collectively highlight the growing efficacy of transformer-based models in enhancing the accuracy and reliability of wind speed and power forecasting. Recent literature has increasingly focused on developing novel deep-learning approaches for time series forecasting in the context of wind speed and power prediction. [PERSON] et al. proposed a novel wind power forecasting system that integrates time series refining with a nonlinear multi-objective optimized deep learning framework and linear error correction, demonstrating significant improvements in predictive accuracy for monthly wind speed data [53]. Additionally, [PERSON] et al. developed a deep learning-based multistep-ahead forecasting model that focuses on discrete methods, which offers a direct approach to predicting wind speed and power generation [54]. Further advancements were made by [PERSON] et al., who introduced a bio-inspired optimization algorithm tailored specifically for wind power engineering applications in time series forecasting, showcasing the potential of bio-inspired methods to enhance deep learning models for energy forecasting [55]. These contributions underscore the significant role that advanced deeplearning techniques and novel algorithmic strategies play in improving the accuracy and reliability of wind speed and power forecasts. Many related works investigated the combination of ML with Numerical Weather Prediction (NWP) to offer promising accuracy with several limitations [56]. One significant challenge lies in the inherent differences between the two approaches: NWP models are physics-based and rely on complex atmospheric equations, which require extensive computational resources and can suffer from coarse spatial and temporal resolution [34].