# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
### Research Objectives  
The primary objective of this paper is to present a novel integrated wind speed-power forecasting system (WSPFS) designed to enhance renewable energy forecasting for smart and zero-energy cities. Unlike previous studies, which often focus on isolated aspects of either wind speed or power prediction as illustrated in Table 1, this study introduces a multi-horizon forecasting approach that seamlessly integrates wind speed forecasting (WSF) outputs as inputs for wind power forecasting (WPF). This comprehensive system evaluates the performance of 12 AI algorithms, including individual and ensemble models, across diverse forecasting horizons ranging from 10 min to 36 h. The system also incorporates metrics to benchmark predictive accuracy, addressing short- and long-term forecasting needs.  
This research work does more than compare existing forecasting methods; it contributes to the state of the art by addressing gaps in the literature, particularly the underutilization of tree-based models such as scalable boosting machines and random forests. By leveraging these models and incorporating computational time and cost evaluations, the study validates the effectiveness of these methods and demonstrates their scalability and efficiency for real-world applications. Moreover, the integration of the carbon city index (CCI) and the carbon-city transformation curve (CCTC) within the smart wind energy deep decarbonization (SWEDD) framework sets this research apart, providing a pathway for cities like NEOM and the Suez Canal to transition toward zero-energy operations. These contributions underline the dual focus of this work on advancing forecasting methodologies and supporting global sustainability initiatives.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Reference** & **Main Objective** & **Year** & \multicolumn{4}{c}{**Aspects**} & \\ \hline  & & & **Wind Speed** & \begin{tabular}{c} **Wind** \\ **Speed-Power** \\ **Forecasting** \\ \end{tabular} & \begin{tabular}{c} **Smead Cities** \\ **Framework** \\ \end{tabular} & \
\begin{tabular}{c} **Computational** \\ **Time and Cost** \\ \end{tabular} &
\begin{tabular}{c} **Different Time** \\ **Horizons** \\ \end{tabular} \\ \hline  & Wind speed and & & & & & & & \\  & power forecasting & & & & & & \\  & models & & & & & & \\ \hline  & Short-term combined & & & & & & \\  & forecasting for wind & 2024 & & & & & & \\  & power & & & & & & \\ \hline  & Offshore wind & & & & & & & \\  & turbine power & & & & & & \\  & prediction model & & & & & & \\ \hline  & Enhanced wind & & & & & & \\  & speed prediction & 2024 & & & & & & \\  & techniques & & & & & & \\ \hline  & Ensemble learning & & & & & & & \\  & methods for wind & & & & & & \\  & turbine power & & & & & & \\  & forecasting & & & & & & \\ \hline  & Wind power & & & & & & \\  & forecasting system & 2024 & & & & & & \\  & using data & & & & & & \\  & augmentation & & & & & & \\ \hline  & Integrated wind & & & & & & \\  & speed–power & & & & & & \\  & prediction system & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of recent relative studies.