###### Abstract  
In recent years, the adverse effects of climate change have increased rapidly worldwide, driving countries to transition to clean energy sources such as solar and wind. However, these energies face challenges such as cloud cover, precipitation, wind speed, and temperature, which introduce variability and intermittency in power generation, making integration into the interconnected grid difficult. To achieve this, we present a novel hybrid deep learning model, CEEMDAN-CNN-ATT-LSTM, for short- and medium-term solar irradiance prediction. The model utilizes complete empirical ensemble modal decomposition with adaptive noise (CEEMDAN) to extract intrinsic seasonal patterns in solar irradiance. In addition, it employs a hybrid encoder-decoder framework that combines convolutional neural networks (CNN) to capture spatial relationships between variables, an attention mechanism (ATT) to identify long-term patterns, and a long short-term memory (LSTM) network to capture short-term dependencies in time series data. This model has been validated using meteorological data in a more than 2400 masi region characterized by complex climatic conditions south of Ecuador. It was able to predict irradiance at 1, 6, and 12 h horizons, with a mean absolute error (MAE) of 99.89 W/m\({}^{2}\) in winter and 110.13 W/m\({}^{2}\) in summer, outperforming the reference methods of this study. These results demonstrate that our model represents progress in contributing to the scientific community in the field of solar energy in environments with high climatic variability and its applicability in real scenarios. solar irradiance forecasting; long short-term memory; convolution neural network; CEEMDAN; multi-head attention; complex terrain 20251527520251527520251527520251527520251527520251527520251527520251527520251527520251527520251527520251520251527520251520251527520251520251520251520251520251520251520251  
result, the number of solar power plants is increasing considerably [4]. It is, therefore, becoming critical for utilities to have reliable and robust models that ensure the efficient operation of the power grid. PV power generation is directly dependent on solar irradiance. Although predicting irradiance might appear to be an easy task, in reality, factors such as cloud cover, precipitation, and wind speed have a significant impact [5]. These factors introduce instability, randomness, and uncertainty into irradiance, resulting in solar power generation volatility. This variability makes it difficult to fully rely on PVs and presents a considerable challenge to the effective integration of solar generation plants into the power grid [6]. This challenge is even more complex in areas where the climate is volatile [7]. Different solar forecasting methods have been developed and experimented with over time. According to [8], solar energy prediction techniques are divided into five groups: physical methods, persistence methods, statistical methods, machine learning models, and hybrid models. Persistence models are commonly used to compare forecasted results and evaluate model performance. According to [PERSON] et al. [9], energy prediction models based on the persistence method are straightforward but limited. Physical methods, on the other hand, use meteorological variables defined by mathematical equations to model weather patterns, requiring significant computational resources [10]. In this context, [PERSON] and [PERSON] [11] proposed a unified model of irradiance patterns (UMRP) to simulate sky irradiance under various conditions. However, physical models often fail to achieve reasonable accuracy under unstable weather conditions [12]. Statistical methods rely on mathematical relationships between arbitrary and non-arbitrary variables. Examples of this method are the autoregressive integrated moving average (ARIMA) model [13], exponential smoothing [14], and multiple linear regression (MLR) [15], which efficiently model relationships between irradiance and meteorological data.