### Recent Advances in Time Series Prediction for Solar Forecasting
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref. \& Year** & **Learning Algorithms** & **Output \& Horizon** & **Location** & **Results \& Limitations** \\ \hline \multirow{2}{*}{[36], 2024} & Multilevel Data Fusion and Neural & \multirow{2}{*}{Power generation, one-day ahead} & \multirow{2}{*}{Medium-complex terrain} & MAE = 79.7\% W. Does not address seasonal variability, relies heavily on extensive data cleaning, and does not incorporate variable decomposition methods. \\  & Analysis (MF-NBEA) & & & \\ \hline \multirow{2}{*}{[37], 2022} & Hybrid Model (Deep Neural & \multirow{2}{*}{Power generation, one-hour ahead} & \multirow{2}{*}{Medium-complex terrain} & Accuracy of 97\%. Does not provide predictions beyond a 2 horizon. \\  & Network and LSTM & & & 2 \\ \hline \multirow{2}{*}{[38], 2024} & Hybrid Model (Transformer & \multirow{2}{*}{Power generation, one-hour ahead} & \multirow{2}{*}{Medium-complex terrain} & MAE = 11.8\%. Divides the dataset by seasons but does not utilize variable decomposition methods. \\  & Model—PVTranseNet) & & & \\ \hline \multirow{2}{*}{[39], 2024} & Attention-Embedded Convolutional & \multirow{2}{*}{Global horizontal irradiance (GHI), eight-time steps ahead} & \multirow{2}{*}{Medium-high complex terrain} & RMSE = 62.57\% W/m2. Relics solely on images for predictions in complex terrain. Does not use variable decomposition, seasonal division, or resampling. Does not incorporate additional data sources beyond images. \\  & Neural Networks (ATT\_CNN) & & & \\ \hline \multirow{2}{*}{[40], 2024} & Bi-LSTM with Additive Attention & \multirow{2}{*}{GHI, one-day ahead} & RMSE = 22.9\%. Using high-frequency meteorological data, tested with limited data, does not separate by seasons, and does not decompose the irradiance variable. \\  & Mechanism and Dilated & & & \\ Convolutional Layers & & & & \\ \hline \multirow{2}{*}{[41], 2021} & Hybrid Model (Conv-LSTM-R) & \multirow{2}{*}{GHI, one-day ahead} & \multirow{2}{*}{Low-complex terrain} & MAE = 18.2\%. Divides data by seasons but does not use variable decomposition or resampling methods. \\  & & & & \\ \hline \multirow{2}{*}{[42], 2024} & Spectral Cloud Optical Property & \multirow{2}{*}{GHI, one-hour ahead} & \multirow{2}{*}{Medium-complex terrain} & RMSE = 44.7 W/m2. Utilizes image data exclusively, does not incorporate additional variables, and divides the dataset by seasons. However, it does not decompose the studied variables. \\  & Estimation (SCOPE) Method in & & & \\ \hline \multirow{2}{*}{[43], 2024} & \multirow{2}{*}{Artificial Neural Networks (ANN)} & Direct normal Irradiance (DNI), one-day ahead & \multirow{2}{*}{Medium-complex terrain} & MAE = 21.1\% W/m2. Utilizes high-resolution data but does not employ variable decomposition methods or resampling techniques. \\  & & & & \\ \hline \multirow{2}{*}{[44], 2024} & \multirow{2}{*}{Quantum Neural Network Model} & \multirow{2}{*}{GHI, three-hour ahead} & \multirow{2}{*}{Medium-complex terrain} & MAE = 77.55\% W/m2. The dataset was limited to GHI, DNI, and sky images, and does not employ more advanced hybrid methods. \\  & & & & \\ \hline \multirow{2}{*}{[45], 2024} & Transformer, Informer, and Autoformer & \multirow{2}{*}{Power generation, one-day ahead} & \multirow{2}{*}{Medium-high complex terrain} & MAE = 8.50\%. A novel model that does not incorporate signal decomposition or resampling methods but offers high computational efficiency. \\ \cline{1-1} \cline{5-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of key studies on AI techniques for solar forecasting.