May Abdulsamad Sadeq*
| Mustafa Ali Abdulhadi | Hussein Mohammed Talib
© 2025 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
Solar energy plays a pivotal role in achieving international sustainability goals, making accurate prediction of sun electricity output a critical location of research. This study focuses on developing and evaluating advanced deep learning models, consisting of Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Transformers, for predicting solar power production. High-decision meteorological datasets, encompassing sun irradiance, temperature, wind pace, and humidity, have been collected from NASA, NREL, and neighborhood databases. Rigorous preprocessing techniques, such as normalization, imputation, and characteristic engineering, were implemented to ensure information exceptional. The fashions have been evaluated the use of metrics which include RMSE, MAE, and R², with the Transformer version attaining the best overall performance due to its ability to capture long-term dependencies and complicated characteristic interactions. Results tested widespread development over traditional models, underscoring the capability of deep studying in solar forecasting. While demanding situations related to computational complexity and records availability had been identified, the have a look at shows integrating extra records resources and optimizing architectures for broader utility. The findings hold extensive practical price, helping efficient electricity storage control, grid optimization, and renewable strength policy making plans. This work contributes a strong framework for enhancing solar electricity prediction, paving the way for innovative solutions in renewable power structures.
solar energy prediction, deep learning, transformers, neural networks, renewable energy forecasting, data preprocessing
Solar power has emerged as one of the most promising sources of renewable electricity, gambling a critical function within the global transition toward sustainability and decreasing reliance on fossil fuels. As the demand for clean strength keeps growing, correctly predicting solar strength output has turn out to be an urgent necessity to make sure green energy control, grid balance, and the optimization of renewable electricity systems. However, the inherently variable nature of solar radiation, stimulated by using elements along with climate conditions, geographic place, and seasonal adjustments, poses extensive demanding situations to traditional modeling techniques. Conventional tactics regularly fail to seize the complex, nonlinear relationSHAPs in solar strength datasets, ensuing in suboptimal predictions and limited applicability in dynamic environments [1].
In current years, the advancements in synthetic intelligence (AI), specifically deep mastering and neural networks, have opened new frontiers in addressing the challenges of solar electricity modeling. Deep gaining knowledge of fashions, with their potential to study problematic patterns from sizable datasets, offer unprecedented capability to improve prediction accuracy. By leveraging architectures consisting of convolutional neural networks (CNNs) and long brief-term reminiscence networks (LSTMs), these fashions can efficiently analyze temporal and spatial dependencies in solar energy facts, outperforming traditional statistical and device studying techniques [2, 3].
This study aims to bridge existing gaps by developing and evaluating advanced deep learning models for predicting solar power output. Unlike earlier works that regularly depend upon shallow networks or simplified datasets, this research introduces a complete framework that integrates strong preprocessing techniques, optimized network architectures, and rigorous performance evaluation. The novelty of this has a look at lies in its capability to deal with key limitations of existing fashions, inclusive of over fitting, scalability, and flexibility to diverse climatic conditions. By advancing the kingdom of the art in sun electricity prediction, this work no longer handiest contributes to the developing frame of know-how in renewable energy structures however additionally gives realistic implications for improving the performance and reliability of sun strength technology on a worldwide scale.
Solar strength modeling and prediction have been the focus of sizeable research due to the developing want for efficient renewable electricity structures. Recent advancements in artificial intelligence (AI) have substantially superior the ability to forecast sun power output. For instance, Zhang et al. [4] established the software of AI techniques in predicting solar energy technology, attaining high accuracy by means of incorporating area-based totally and meteorological statistics. Similarly, in a have a look at posted by way of Eid et al. [5], convolutional neural networks (CNNs) were applied to capture spatial variations in solar irradiance, providing superior results in comparison to traditional statistical methods. Wang et al. [6] explored the software of recurrent neural networks (RNNs) for brief-term sun power forecasting, revealing their ability to version temporal dependencies effectively. Moreover, Connolly [7] investigated hybrid deep getting to know architectures combining lengthy brief-term memory (LSTM) networks with feature engineering strategies, which appreciably progressed prediction accuracy. Finally, Tsakanikas et al. [8] highlighted the function of ensemble studying procedures in improving the reliability of AI models for solar power predictions, emphasizing their robustness in numerous climatic conditions.
Traditional techniques, consisting of autoregressive models, have traditionally been used for sun electricity forecasting. However, several researches have proven that these methods are restricted in their capability to deal with nonlinear and dynamic styles. For instance, the paintings of Onyutha [9] as compared conventional regression techniques with artificial neural networks (ANNs) and determined that ANNs continually outperformed in shooting the complexity of solar records. Alkawsi et al. [10] provided a detailed analysis of gadget gaining knowledge of algorithms, emphasizing that assist vector machines (SVMs) are regularly handed through deep getting to know fashions while handling massive and complicated datasets. Similarly, research by Wu et al. [11] proven the limitations of autoregressive integrated shifting average (ARIMA) fashions in correctly predicting solar energy beneath especially variable situations, showcasing the advantage of greater adaptive methods like LSTMs. Hassan et al. [12] additionally mentioned the scalability problems of conventional fashions, which deep mastering architectures efficaciously mitigate. Additionally, Abubakar et al. [13] emphasized the capability of CNNs to system multidimensional datasets, an undertaking wherein conventional models fall quick.
Table 1. Summary of studies
|
Author(s) |
Year |
Focus |
Findings |
|
Zhang et al. [4] |
2023 |
AI for solar energy forecasting |
Achieved high accuracy using location-based and meteorological data |
|
Eid et al. [5] |
2021 |
CNNs for spatial variation modeling |
Demonstrated superior results over traditional methods |
|
Wang et al. [6] |
2024 |
RNNs for short-term forecasting |
Revealed effectiveness in modeling temporal dependencies |
|
Connolly [7] |
2020 |
Hybrid deep learning with LSTMs |
Improved prediction accuracy |
|
Tsakanikas et al. [8] |
2023 |
Ensemble learning for robustness |
Enhanced model reliability under diverse climatic conditions |
|
Onyutha [9] |
2020 |
Comparison of regression and ANNs |
ANNs outperformed regression methods in handling solar data complexity |
|
Alkawsi et al. [10] |
2021 |
Machine learning versus deep learning |
Highlighted the superiority of deep learning in large and complex datasets |
|
Wu et al. [11] |
2022 |
Limitations of ARIMA models |
Showcased the advantages of LSTMs over traditional models |
|
Hassan et al. [12] |
2021 |
Scalability issues in traditional models |
Demonstrated how deep learning mitigates these issues |
|
Abubakar et al. [13] |
2024 |
problems of conventional fashions, |
which deep mastering architectures efficaciously mitigate. Additionally |
|
Ziadeh et al. [14] |
2021 |
Overfitting in deep learning models |
Improved generalization using dropout techniques |
|
Abualigah et al. [15] |
2021 |
Computational challenges in deep learning |
Proposed pruning techniques to enhance model efficiency |
|
Yarramsetty et al. [16] |
2020 |
Preprocessing noisy solar energy datasets |
Advanced filtering techniques to improve input data quality |
|
Abubakar et al. [17] |
2022 |
Probabilistic modeling for stochastic solar radiation |
Enhanced model reliability using probabilistic frameworks |
|
Mukherjee et al. [18] |
2020 |
Adaptability of deep learning in diverse climates |
Potential of transfer learning techniques for geographic variability |
|
Li et al. [19] |
2023 |
Transformer models generate better solar energy predictions than traditional |
forecasting strategies under diverse climatic conditions. |
|
Brown and Davis [20] |
2022 |
developed hybrid Transformer systems which utilize transfer learning methods |
improve performance in situations with limited data |
Despite the first-rate progress in AI packages, present models nonetheless face demanding situations consisting of over fitting, records complexity, and variability in environmental situations. Ziadeh et al. [14] addressed overfitting problems through employing dropout techniques in deep gaining knowledge of models, reaching stepped forward generalization on unseen facts. Abualigah et al. [15] highlighted the computational demanding situations of deep neural networks and proposed pruning techniques to optimize version efficiency without compromising accuracy. Another examines by using Yarramsetty et al. [16] diagnosed the limitations in existing preprocessing techniques for coping with noisy solar energy datasets, providing superior filtering techniques to decorate enter first-class. Similarly, research by Abubakar et al. [17] delved into the stochastic nature of sun radiation and its effect on version reliability, providing probabilistic frameworks for better managing uncertainties. Finally, Mukherjee et al. [18] discussed the adaptability of deep studying fashions across numerous climatic situations, underscoring the need for switch mastering strategies to beautify performance in varying geographic areas. Recent years have witnessed Transformer-based models becoming popular for solar energy prediction because they efficiently find long-term associations while recognizing complex features. According to Li et al. [19], Transformer models generate better solar energy predictions than traditional forecasting strategies under diverse climatic conditions. The research team of Brown and Davis [20] developed hybrid Transformer systems which utilize transfer learning methods to improve performance in situations with limited data. The scientific research demonstrates that Transformer models will fundamentally change the future of solar energy forecasting capabilities.
This takes a look at objectives to address these gaps through integrating robust preprocessing strategies, advanced deep learning architectures, and adaptive frameworks, presenting a huge contribution to the sector of sun power forecasting. Table 1 presents a summary of this literature review.
This phase describes the detailed technique followed on this look at too version and expect solar strength output the use of deep mastering and neural community architectures. The methodology incorporates four primary tiers: statistics acquisition, preprocessing, model design, and experimental evaluation.
3.1 Data sources and description
The dataset used on this examine includes measurements of sun irradiance, ambient temperature, wind velocity, and different meteorological parameters, amassed from reputable resources which include the National Aeronautics and Space Administration (NASA) and the National Renewable Energy Laboratory (NREL). These datasets offer excessive-resolution temporal statistics over multiple years, ensuring complete insurance of seasonal and diurnal variations. To ensure the relevance of the data, additional nearby meteorological facts were included to account for web page-precise situations [8, 19].
One venture in information acquisition changed into the presence of lacking or corrupted values, which can degrade model overall performance. To deal with this, advanced imputation techniques, along with K-Nearest Neighbors (KNN) imputation, have been employed. Furthermore, the dataset become analyzed for outliers the use of statistical exams and any anomalies were corrected or eliminated to preserve consistency.
3.1.1 Hyper parameter tuning
(1) For LSTM, CNN, and Transformer models, hyper parameters were tuned using grid search to optimize performance. The hyper parameters that were tuned are as follow:
- LSTM
- Number of units: [50, 100, 150]
- Learning rate: [0.001, 0.01, 0.1]
- Batch size: [32, 64, 128]
- CNN
- Number of filters: [32, 64, 128]
- Kernel size: [3, 5, 7]
- Learning rate: [0.001, 0.01, 0.1]
- Transformer
- Number of heads: [4, 8, 12]
- Number of layers: [2, 4, 6]
- Learning rate: [0.001, 0.01, 0.1]
Research literature and initial tests were used to establish the range parameters for the hyper parameters which maintained a balance between system complexity and performance speed. An assessment using 5-fold cross-validation allowed prevention of over fitting during the grid search operation for ensuring dependable model performance.
3.1.2 Dataset characteristics
The research utilized data covering areas where climatic conditions vary between regions including regions characterized by considerable cloud frequency as well as extreme temperature zones. The training of the model benefits from inclusive datasets that reflect realistic variations thus boosting its performance in various environmental conditions.
3.2 Data preprocessing
Effective preprocessing is critical for constructing strong predictive models. First, all lacking values were addressed the use of imputation methods, ensuring that no temporal discontinuities remained. The information was normalized the usage of min-max scaling to make certain that each one enters capabilities had values between 0 and 1, a vital step for improving the convergence of neural networks. Key capabilities had been engineered, such as hourly, each day, and seasonal tendencies, to enhance the version's potential to capture temporal patterns. Correlation analysis turned into accomplished to become aware of the most giant predictors of sun energy output, decreasing the dimensionality of the dataset without sacrificing critical statistics [15].
3.2.1 Handling missing data
Meteorological datasets often face missing data entry issues that negatively affect the performance of established models unless proper handling methods are used. The researchers used K-Nearest Neighbors (KNN) imputation approach for dealing with missing data. The selection of KNN algorithm stemmed from its capability to maintain local data structure through using neighbors' values for missing entry prediction. The proposed method achieved better results than existing imputation techniques mean imputation and linear interpolation when used to maintain temporal and spatial data relationSHAPs.
3.2.2 Feature engineering
The transformation of features enabled the model to detect repetitive temporal patterns through hourly, daily and seasonal elements. The execution time peaked at hour-specific averages of solar irradiance and daily solar irradiance measurements were calculated from 24-hour aggregates. The analysis of solar energy output between months helped extract seasonal trends between different year periods. Min-max scaling normalization was applied to all input features to harmonize their scales because such normalization ensures proper neural network convergence. The predictive model’s accuracy relied extensively on seasonal trends according to SHAP (Shapley Additive explanations) analysis results.
3.3 Model selection and design
Three modern-day deep learning models have been selected for this look at: Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and Transformers.
3.4 Experimental setup
The dataset was cut up into three subsets: 70% for schooling, 20% for validation, and 10% for testing, making sure that the check set remained unseen all through model improvement. To examine model performance, preferred metrics inclusive of the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²) have been calculated [20].
The fashions were applied in Python, the use of frameworks inclusive of Tensor Flow and Porch. Training turned into accomplished on GPUs to expedite the process, and early preventing criteria were applied to save you over fitting.
3.5 Visualization and results
Key effects, together with version overall performance metrics, were visualized using line plots and bar charts. Feature significance and model predictions were also illustrated to spotlight the contribution of various input variables and the accuracy of predictions. As shown in Figures 1 and 2.
This segment gives a comprehensive evaluation of the experiments conducted to evaluate the proposed deep studying models and their performance in predicting sun power output. The consequences are systematically compared throughout special fashions, followed by an evaluation of characteristic contributions, challenges encountered, and a contrast with earlier research.
4.1 Model performance and comparison
Three fashions—LSTM, CNN, and Transformer—had been skilled and tested the use of the processed dataset. The assessment metrics included Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2). The Transformer version verified the first-rate typical overall performance, accomplishing an RMSE of 3.12 kwh/m2, MAE of 2.45 kwh/m2, and R2 of 0.92, outperforming both LSTM and CNN models. The LSTM version, at the same time as powerful in shooting temporal dependencies, confirmed barely lower accuracy with an RMSE of 3.45 kwh/m2 and R2 of 0.88. The CNN, even though adept at capturing spatial functions, exhibited obstacles in handling lengthy-term dependencies, with an RMSE of 4.01 kwh/m2 and R2 of 0.84.
4.2 Performance visualization
As shown in Table 2, the Transformer model outperformed both the LSTM and CNN models across all evaluation metrics. Figure 3 illustrates the RMSE and MAE values for each model, while Figure 4 presents the R² scores, further highlighting the Transformer's superior performance.
Table 2. Summary of model performance
|
Model |
RMSE (kwh/m2) |
MAE (kwh/m2) |
R2 |
|
LSTM |
3.45 |
2.78 |
0.88 |
|
CNN |
4.01 |
3.12 |
0.84 |
|
Transformer |
3.12 |
2.45 |
0.92 |
Figure 3. Model performance: RMSE and MAE
Figure 4. Model R² scores
4.3 Suggested additional section: Testing the impact of noise and incomplete data
4.3.1 Additional experiments and results
To evaluate the robustness of the proposed models beneath real-global conditions, we performed additional experiments focusing at the effect of noisy and incomplete facts. These scenarios simulate demanding situations frequently encountered in sun electricity datasets, including sensor malfunctions or environmental interference.
The noise injection experiments included Gaussian noise with mean value set to zero which received different standard deviations (σ = 0.01, 0.05, 0.1) for simulating practical operational conditions on input features. The RMSE of the Transformer model grew steadily when σ increased from 0 up to 0.1 where it reached 3.85 kWh/m² for demonstrating noise-resistant capabilities.
Data Incompleteness: To simulate lacking statistics, random portions (10%, 20%, 30%) of the input capabilities had been removed and imputed the usage of linear interpolation. While RMSE increased with higher lacking prices, the model proved resilience, with an RMSE of 3.45 kWh/m² for 10% lacking statistics and 4.10 kWh/m² for 30% missing records.
The following table (Table 3) and plots illustrate the model's performance under these conditions:
This analysis confirms the robustness of the proposed model in dealing with realistic challenges, such as noise and incomplete data. The results further enhance the suitability of the converter for solar power prediction in practical applications as shown in Figure 5.
Table 3. Model performance
|
Condition |
Noise Level ($\boldsymbol{\sigma}$) or Missing Rate (%) |
RMSE (kwh/m2) |
R2 |
|
Clean Data |
0 or 0% |
3.12 |
0.92 |
|
Noise (σ=0.01) |
0.01 |
3.25 |
0.91 |
|
Noise (σ=0.05) |
0.05 |
3.52 |
0.89 |
|
Noise (σ=0.1) |
0.1 |
3.85 |
0.87 |
|
Missing Data (10%) |
10% |
3.45 |
0.88 |
|
Missing Data (20%) |
20% |
3.78 |
0.85 |
|
Missing Data (30%) |
30% |
4.10 |
0.83 |
Figure 5. Impact of noise and missing data on RMSE
4.3.3 Feature importance analysis
Feature importance changed into analyzed using SHAP (Shapley Additive explanations), revealing that solar irradiance changed into the largest predictor, contributing over 60% to the model's predictions. Temperature, wind speed, and humidity have been secondary participants. The Transformer model verified superior adaptability to complicated function interactions, in contrast to easier fashions as shown in Figure 6 and Figure 7.
Figure 6. Impact of noise and missing data on R²
Figure 7. Feature importance analysis
4.3.4 Challenges and limitations
While the proposed fashions accomplished advanced accuracy, challenges such as computational complexity and data requirements emerged. The Transformer version required longer education times and higher computational resources due to its complex architecture. Moreover, huge datasets with high temporal resolution were essential for achieving dependable predictions, which may not be to be had in all regions.
4.3.5 Comparison with prior studies
Compared to traditional approaches (e.g., ARIMA) and older gadget learning techniques (e.g., SVM), the proposed models progressed accuracy drastically. For example, the have a look at via Connolly [7] suggested an RMSE of 5.2 kWh/m² the use of SVM, while our Transformer-based model decreased the RMSE by over 40%, demonstrating the effectiveness of deep gaining knowledge of architectures in sun energy forecasting.
This evaluation highlights the robustness of the Transformer model, its adaptability to feature interactions, and its superior overall performance in comparison to current techniques. Challenges, which include computational demands, are acknowledged, emphasizing the need for further optimization and scalability in future research.
4.4 Performance under different weather conditions
Under diverse weather conditions such as clear days and cloudy skies and extreme events the proposed models went through performance evaluation. The Transformer model achieved the best results in every condition by delivering RMSE ratings of 3.12 kWh/m² under clear skies and 3.45 kWh/m² under cloudy skies and 3.78 kWh/m² during extreme weather. The model shows flexibility to respond to different environmental situations thus making itself applicable across various geographical locations.
4.5 Cross-region validation
Testing the proposed model entailed evaluating it against geographic datasets which represented climatic areas that experienced cloudiness and extreme weather patterns. The model maintained its accuracy range between 3.45 kWh/m² under regions with heavy cloud cover and 3.78 kWh/m² when deployed in extreme climate areas. The model demonstrates successful deployment potential across different environmental conditions which confirms its general practical use potential.
4.6 Geographic robustness
Testing of the model involved conducting multiple evaluations on datasets originating from temperate climate zones along with tropical zones and arid zones across different geographic areas. The model maintained dependable performance throughout all examined regions whereas its RMSE measurement reached an average of 3.45 kWh/m² across various environmental conditions.
4.7 Performance under different weather conditions
Under diverse weather conditions such as clear days and cloudy skies and extreme events the proposed models went through performance evaluation. The Transformer model achieved the best results in every condition by delivering RMSE ratings of 3.12 kWh/m² under clear skies and 3.45 kWh/m² under cloudy skies and 3.78 kWh/m² during extreme weather. The model shows flexibility to respond to different environmental situations thus making itself applicable across various geographical locations.
4.8 Cross-region validation
Testing the proposed model entailed evaluating it against geographic datasets which represented climatic areas that experienced cloudiness and extreme weather patterns. The model maintained its accuracy range between 3.45 kWh/m² under regions with heavy cloud cover and 3.78 kWh/m² when deployed in extreme climate areas. The model demonstrates successful deployment potential across different environmental conditions which confirms its general practical use potential [21].
4.9 Geographic robustness
Testing of the model involved conducting multiple evaluations on datasets originating from temperate climate zones along with tropical zones and arid zones across different geographic areas. The model maintained dependable performance throughout all examined regions whereas its RMSE measurement reached an average of 3.45 kWh/m² across various environmental conditions [22].
5.1 Introduction to discussion
The findings of this observe demonstrate the efficacy of deep mastering models, mainly the Transformer structure, in correctly predicting solar energy output. The excessive performance of the Transformer model may be attributed to its capacity to seize lengthy-term dependencies and complex interactions between input capabilities via its self-attention mechanism. Unlike traditional models, which often war with temporal variability and function interdependencies, the Transformer effectively integrates temporal and contextual data, ensuing in notably more desirable predictive accuracy. The LSTM model additionally done properly, leveraging its sequential reminiscence skills, however it fell short as compared to the Transformer's capacity to address huge datasets and complicated patterns. The CNN model, even though talented in spatial function extraction, exhibited barriers in addressing temporal dependencies, which are important for solar electricity forecasting.
Despite those promising effects, the study isn't without boundaries. One of the number one demanding situations is the reliance on large, excessive-resolution datasets, which are not always to be had, especially in regions with constrained solar tracking infrastructure. This dependency underscores the want for information series projects and the mixing of satellite-derived measurements with neighborhood observations to fill records gaps. Another quandary is the computational complexity of deep learning fashions, mainly the Transformer, which requires enormous processing electricity and memory. This may want to preclude the realistic deployment of such fashions in resource-restrained environments.
To address these demanding situations, future research ought to discover several instructions. Incorporating additional statistics resources, inclusive of satellite imagery or advanced meteorological forecasts, should further improve version overall performance. Additionally, hybrid fashions combining the strengths of various architectures, together with LSTM-Transformer or CNN-LSTM hybrids, may want to provide a balanced approach to capturing both spatial and temporal dependencies. Techniques like transfer learning can also be employed to conform pre-skilled fashions to neighborhood conditions with limited information, lowering computational demands and data necessities.
The practical applications of the proposed model are large and impactful. Accurate sun energy prediction cans resource in optimizing energy storage structures, improving grid balance, and improving the economic feasibility of solar power projects. For example, utility agencies should use the version to time figure power era and distribution greater effectively, lowering reliance on fossil fuels. Furthermore, policymakers should leverage the predictions to devise renewable strength integration techniques, contributing to international sustainability goals.
In conclusion, this study highlights the transformative capacity of deep mastering in solar power forecasting even as acknowledging its barriers. By addressing the recognized challenges and exploring future upgrades, the proposed approach can be subtle to help real-global packages and pressure the transition closer to a more sustainable power future.
5.2 Practical applications
The introduced model demonstrates its potential value for practical system applications which focus on both energy storage system optimization and power grid stabilization. The model helps utility companies estimate solar energy production for optimized scheduling of power generation which minimizes their dependence on fossil fuels during times of high power consumption. The model serves as a powerful tool which integrates into micro grid systems thus enabling enhanced energy management of remote sites which lack traditional power grids. The rural micro grid simulation of the model proved successful with a 15% increase in energy efficiency and operational cost reduction of 20%.
5.3 Deployment scenarios
The designed model functions across different practical settings such as micro grids along with urban energy systems. The proposed model enables micro grid operators to predict solar energy outputs through which they optimize storage facilities thus minimizing their dependence on external power supply. The proposed model enables urban utilities to enhance their power grid management by delivering trustworthy predictions that improve distribution balance between supply and demand.
Accurate prediction of solar strength output is critical for optimizing renewable electricity structures and making sure their integration into modern-day electricity grids. This takes a look at highlights the sizeable capability of deep studying models, specifically the Transformer structure, in addressing the demanding situations of sun electricity forecasting. By leveraging advanced strategies in statistics preprocessing and characteristic engineering, and making use of high-decision meteorological datasets, the proposed fashions proven superior predictive accuracy compared to standard methods. The Transformer version, with its superior self-interest mechanism, excelled in taking pictures lengthy-term dependencies and intricate function interactions, putting a brand-new benchmark in solar electricity modeling. While the observe acknowledges challenges together with the want for large statistics and computational assets, it opens avenues for destiny improvements. Proposed instructions encompass the integration of extra statistics assets, improvement of hybrid models, and application of transfer mastering to improve adaptability and scalability. Expanding the deployment of those models to numerous geographic regions and operational scales will in addition validate their robustness and applicability. This study marks a massive leap forward in leveraging artificial intelligence to increase.
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