1. A Transformer-based deep learning system for accurate solar radiation prediction under dynamic atmospheric conditions, the system comprising: a data acquisition module configured to collect meteorological and atmospheric data from one or more environmental sensing sources; a preprocessing module operatively connected to the data acquisition module and configured to perform data cleaning, normalization, synchronization, and feature conditioning operations; a sequence formation module configured to generate temporal time-series input sequences from the processed atmospheric data; a Transformer-based deep learning engine configured to process the generated sequences using encoder-decoder architectures and positional encoding mechanisms; a multi-head attention mechanism configured to capture long-range temporal dependencies and feature interactions among atmospheric variables; a dynamic atmospheric adaptation module configured to modify prediction behaviour according to atmospheric variability; and an explainability module configured to generate attention-based interpretations associated with solar radiation prediction outputs.

2. The system as claimed in claim 1, wherein the meteorological and atmospheric data comprise solar irradiance, temperature, humidity, wind speed, atmospheric pressure, cloud density, aerosol concentration, ultraviolet radiation levels, rainfall information, and solar zenith angle.

3. The system as claimed in claim 1, wherein the preprocessing module is configured to perform missing-value imputation, outlier removal, interpolation, feature scaling, signal smoothing, and temporal alignment of atmospheric datasets.

4. The system as claimed in claim 1, wherein the sequence formation module is configured to generate fixed-length or variable-length temporal windows corresponding to hourly, daily, weekly, or seasonal atmospheric observations.

5. The system as claimed in claim 1, wherein the Transformer-based deep learning engine comprises input embedding layers, positional encoding layers, encoder layers, decoder layers, feed-forward neural networks, residual connections, and normalization units.

6. The system as claimed in claim 1, wherein the multi-head attention mechanism computes attention weights corresponding to atmospheric variables to identify influential environmental parameters affecting solar radiation prediction.

7. The system as claimed in claim 1, wherein the dynamic atmospheric adaptation module is configured to detect atmospheric disturbances including cloud movement, rainfall variation, aerosol fluctuation, temperature change, and seasonal transition, and adaptively modify forecasting parameters accordingly.

8. The system as claimed in claim 1, wherein the explainability module is configured to generate feature importance graphs, attention heatmaps, temporal contribution analyses, confidence indicators, and predictive uncertainty visualizations.

9. The system as claimed in claim 1, wherein the prediction outputs comprise hourly irradiance forecasts, daily solar radiation predictions, multi-horizon forecasting outputs, and renewable energy optimization indicators for photovoltaic and smart-grid applications.

10. A method for accurate solar radiation prediction under dynamic atmospheric conditions, the method comprising: collecting atmospheric and meteorological data from multiple environmental sources; preprocessing the collected data by performing cleaning, normalization, synchronization, and feature conditioning operations; generating temporal time-series sequences from the processed atmospheric data; processing the generated sequences using a Transformer-based deep learning architecture; applying multi-head attention mechanisms to capture temporal dependencies and atmospheric feature interactions; dynamically adapting forecasting behaviour according to changing atmospheric conditions; generating solar radiation prediction outputs; and producing attention-based explainability information corresponding to the generated prediction outputs.

Description:FIELD OF THE INVENTION
The present invention generally relates to the fields of artificial intelligence, renewable energy forecasting, atmospheric data analytics, and deep learning systems. More particularly, the invention relates to a Transformer-based deep learning system configured for accurate prediction of solar radiation under dynamically changing atmospheric conditions using multi-head attention mechanisms, temporal sequence modelling, atmospheric adaptation modules, and explainable artificial intelligence techniques.
The invention further relates to intelligent forecasting architectures capable of capturing long-range temporal dependencies and complex interactions among meteorological variables including cloud density, humidity, temperature, wind speed, aerosol concentration, atmospheric pressure, solar zenith angle, and historical irradiance data. The disclosed system is particularly useful for smart-grid optimization, photovoltaic energy management, renewable energy scheduling, and intelligent energy forecasting applications.
BACKGROUND OF THE INVENTION
Solar radiation forecasting plays a significant role in the efficient utilization and management of renewable energy resources, particularly photovoltaic power generation systems. Accurate prediction of solar irradiance enables grid stability, energy scheduling, battery management, power dispatch optimization, and enhanced reliability of renewable energy infrastructures. However, solar radiation prediction remains a highly challenging task due to continuously changing atmospheric and environmental conditions.
Conventional prediction techniques are generally based on statistical methods and machine learning models such as linear regression, support vector machines, decision trees, random forests, and shallow neural networks. Although such methods may provide acceptable prediction performance under stable environmental conditions, they exhibit limited capability in modelling complex temporal relationships and nonlinear atmospheric interactions. In particular, traditional machine learning approaches fail to effectively capture long-range dependencies present in time-series atmospheric datasets.
Subsequently, recurrent neural network (RNN)-based models and long short-term memory (LSTM) architectures were introduced for sequential solar forecasting tasks. While these models improved temporal modelling capability, they still suffer from multiple limitations including vanishing gradient issues, inadequate handling of long-range temporal dependencies, computational inefficiency during long-sequence processing, and poor interpretability of prediction outcomes. Existing recurrent architectures further demonstrate reduced forecasting performance under rapidly varying atmospheric conditions such as sudden cloud movement, aerosol fluctuations, humidity changes, and seasonal variability.
Recent advancements in Transformer architectures have demonstrated superior capability in sequence modelling through attention mechanisms. However, current Transformer-based forecasting systems remain limited in their ability to dynamically adapt to atmospheric variability while simultaneously providing interpretable insights regarding influential environmental parameters. Existing systems also lack dedicated atmospheric adaptation modules capable of real-time adjustment of prediction weights under changing climatic conditions.
Therefore, there exists a need for an advanced deep learning system capable of accurately predicting solar radiation under dynamic atmospheric conditions while effectively modelling long-range temporal dependencies, feature interactions, and atmospheric variability. There is also a requirement for a forecasting system that provides explainable and interpretable prediction outputs through attention-based visualization mechanisms.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The present invention discloses a Transformer-based deep learning system configured for accurate solar radiation prediction under dynamic atmospheric conditions. The system comprises a data acquisition module configured to collect multi-source atmospheric and meteorological data from weather stations, satellite systems, environmental sensors, and historical irradiance repositories.
The collected data is provided to a preprocessing module configured to perform data cleaning, normalization, missing-value handling, feature scaling, and temporal synchronization. The processed atmospheric data is subsequently converted into sequential time-series representations using a sequence formation module.
The sequential atmospheric data is then supplied to a Transformer-based deep learning architecture comprising positional encoding layers, encoder blocks, decoder blocks, self-attention mechanisms, feed-forward neural networks, and multi-head attention modules. The multi-head attention mechanism enables the system to capture long-range temporal dependencies and complex interactions among atmospheric variables.
The invention further includes a dynamic atmospheric adaptation module configured to monitor rapidly changing weather conditions and adaptively modify prediction weights and attention distributions based on atmospheric variability indicators. Such adaptive behaviour significantly improves prediction robustness under unstable climatic conditions.
The system additionally comprises an explainability module configured to generate interpretable attention-based visualizations and feature importance representations. The explainability module enables identification of influential atmospheric variables and critical temporal intervals responsible for forecasting decisions.
The disclosed system generates highly accurate solar radiation predictions suitable for renewable energy forecasting, photovoltaic optimization, energy scheduling, and intelligent smart-grid applications.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
OBJECTS OF THE INVENTION
The primary object of the present invention is to provide a Transformer-based deep learning system for accurate solar radiation prediction under dynamically changing atmospheric conditions.
Another object of the invention is to provide a deep learning architecture configured to utilize multi-head attention mechanisms for capturing long-range temporal dependencies within meteorological time-series data.
Another object of the invention is to provide a system capable of modelling complex feature interactions among atmospheric variables including temperature, humidity, wind speed, cloud density, aerosol concentration, and historical irradiance values.
Another object of the invention is to provide a dynamic atmospheric adaptation module configured to automatically adjust prediction behaviour in response to rapidly changing atmospheric conditions.
Another object of the invention is to provide an explainability module configured to generate interpretable attention-based insights indicating influential atmospheric parameters and critical temporal intervals contributing to prediction outcomes.
Another object of the invention is to improve renewable energy forecasting reliability, photovoltaic power management efficiency, smart-grid optimization, and energy dispatch planning.
A further object of the invention is to provide a scalable and computationally efficient forecasting framework suitable for integration with smart energy infrastructures and Internet-of-Things (IoT)-enabled renewable energy systems.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: ILLUSTRATES AN ARCHITECTURAL FLOW DIAGRAM OF THE TRANSFORMER-BASED DEEP LEARNING SYSTEM FOR ACCURATE SOLAR RADIATION PREDICTION UNDER DYNAMIC ATMOSPHERIC CONDITIONS.
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The present invention relates to a Transformer-based deep learning system for accurate solar radiation prediction under dynamically changing atmospheric conditions. The invention integrates advanced deep learning architectures, atmospheric data analytics, attention-based sequence modelling, and explainable artificial intelligence mechanisms to improve prediction accuracy and reliability in renewable energy forecasting systems. The disclosed system is particularly useful for photovoltaic power generation forecasting, smart-grid optimization, energy scheduling, renewable resource management, and intelligent environmental monitoring applications.
In an embodiment of the present invention, the system comprises a data acquisition module configured to collect atmospheric and meteorological information from multiple heterogeneous sources. The data sources may include weather stations, atmospheric monitoring sensors, satellite observation systems, photovoltaic monitoring systems, environmental sensing devices, and historical irradiance repositories. The collected data may comprise solar irradiance measurements, ambient temperature, relative humidity, wind speed, atmospheric pressure, aerosol concentration, ultraviolet radiation levels, cloud density, precipitation information, solar zenith angle, seasonal indicators, and historical radiation profiles. The system may continuously acquire real-time atmospheric data or periodically retrieve historical datasets for long-term forecasting analysis.
In another embodiment, the acquired atmospheric information is transmitted to a preprocessing module configured to enhance data quality and ensure compatibility with the deep learning framework. The preprocessing module performs multiple data conditioning operations including noise reduction, outlier elimination, missing-value imputation, feature scaling, normalization, temporal synchronization, interpolation, and smoothing operations. Such preprocessing operations improve the consistency and reliability of atmospheric datasets prior to predictive analysis. The preprocessing module may further perform feature engineering operations to derive secondary atmospheric parameters associated with radiation prediction, including humidity gradients, atmospheric stability indicators, cloud movement trends, and irradiance fluctuation patterns.
In a further embodiment, the processed atmospheric information is supplied to a sequence formation module configured to generate temporal time-series representations suitable for Transformer-based analysis. The sequence formation module organizes atmospheric observations into fixed-length or variable-length temporal windows corresponding to hourly, daily, weekly, or seasonal intervals. Each sequence may include multidimensional atmospheric features arranged according to chronological order to preserve temporal continuity. The sequence generation process enables the system to capture evolving atmospheric dynamics associated with solar radiation behaviour over time.
The core component of the invention comprises a Transformer-based deep learning architecture configured to process sequential atmospheric data. In one embodiment, the Transformer architecture includes input embedding layers, positional encoding units, encoder layers, decoder layers, self-attention mechanisms, feed-forward neural networks, residual connections, and normalization layers. The positional encoding mechanism enables the architecture to represent temporal order information associated with atmospheric observations, thereby overcoming limitations associated with conventional recurrent neural networks and long short-term memory models.
In an embodiment, the encoder layers of the Transformer architecture analyze historical atmospheric patterns and extract contextual relationships among meteorological variables. The encoder may comprise multiple stacked attention layers configured to progressively learn higher-order temporal dependencies and nonlinear atmospheric interactions. The decoder layers utilize encoded contextual information to generate solar radiation predictions corresponding to future forecasting intervals. The Transformer architecture enables parallel sequence processing, thereby improving computational efficiency and scalability when compared to recurrent forecasting architectures.
In another embodiment, the Transformer architecture incorporates a multi-head attention mechanism configured to capture complex feature interactions and long-range temporal dependencies present within atmospheric datasets. The multi-head attention mechanism simultaneously processes multiple contextual representations associated with different atmospheric variables and temporal intervals. Each attention head independently focuses on specific atmospheric relationships including temperature fluctuations, humidity variations, aerosol concentration effects, cloud movement patterns, and historical irradiance dependencies. The attention outputs generated by multiple heads are subsequently aggregated and supplied to feed-forward processing layers for prediction generation.
The multi-head attention mechanism further computes attention weights corresponding to influential atmospheric parameters and critical temporal regions contributing to forecasting outcomes. Such attention weights enable the system to identify important environmental features affecting solar radiation behaviour under dynamic weather conditions. The attention mechanism thereby improves the capability of the forecasting model to capture nonlinear atmospheric interactions and temporal variability that conventional machine learning approaches fail to adequately represent.
In a further embodiment, the invention includes a dynamic atmospheric adaptation module configured to continuously monitor environmental variability and adapt forecasting behaviour according to changing atmospheric conditions. The adaptation module may detect atmospheric disturbances including sudden cloud formation, aerosol fluctuations, precipitation events, seasonal transitions, wind-speed anomalies, and rapid temperature changes. Upon detection of atmospheric variability, the adaptation module dynamically adjusts attention distributions, prediction confidence parameters, temporal weighting coefficients, and feature importance representations to improve forecasting robustness and reliability.
The atmospheric adaptation module may additionally incorporate adaptive learning algorithms configured to update model parameters according to evolving environmental conditions. Such adaptive functionality enables the disclosed system to maintain prediction accuracy under unstable or rapidly changing climatic environments. The adaptation process further minimizes forecasting errors associated with transient atmospheric disturbances that commonly affect photovoltaic energy generation systems.
In another embodiment, the invention comprises an explainability module configured to generate interpretable prediction insights using attention-based visualization mechanisms. The explainability module may produce feature importance graphs, attention heatmaps, temporal contribution analyses, atmospheric relevance indicators, confidence measures, and predictive uncertainty visualizations. The explainability module enables system operators, researchers, and energy managers to identify atmospheric variables and temporal intervals that significantly influence solar radiation prediction outcomes.
The explainability mechanism further improves transparency and trustworthiness of deep learning-based renewable energy forecasting systems by enabling interpretation of attention distributions generated within the Transformer architecture. Such interpretable outputs facilitate informed decision-making in smart-grid management, photovoltaic scheduling, battery optimization, and energy dispatch planning applications.
In one embodiment, the final prediction module generates solar radiation forecasts corresponding to future temporal intervals. The outputs may include hourly irradiance predictions, daily solar radiation forecasts, multi-horizon energy forecasts, confidence intervals, uncertainty measures, and atmospheric reliability indicators. The prediction results may be integrated with renewable energy infrastructures including photovoltaic systems, battery storage units, intelligent grid controllers, energy scheduling platforms, and distributed energy management systems.
In operation, the system initially acquires atmospheric and meteorological information from multiple environmental sources. The collected data is subsequently preprocessed to remove inconsistencies and improve dataset quality. The processed information is then transformed into sequential time-series representations and supplied to the Transformer-based deep learning architecture. The Transformer engine applies positional encoding and multi-head attention mechanisms to extract contextual atmospheric relationships and temporal dependencies. The dynamic atmospheric adaptation module continuously adjusts forecasting behaviour according to real-time environmental variability. The explainability module simultaneously generates interpretable attention-based insights associated with prediction outcomes. Finally, the system outputs accurate solar radiation forecasts suitable for renewable energy optimization and intelligent energy management applications.
The disclosed invention provides several technical advantages over conventional forecasting systems. The Transformer-based architecture effectively captures long-range temporal dependencies without suffering from vanishing gradient limitations associated with recurrent neural networks. The multi-head attention mechanism improves representation of complex atmospheric feature interactions and dynamic environmental variability. The atmospheric adaptation module enhances prediction robustness under unstable weather conditions, while the explainability module improves transparency and interpretability of forecasting decisions. Consequently, the invention provides enhanced prediction accuracy, improved renewable energy reliability, reduced forecasting errors, and superior scalability for deployment within modern smart-grid infrastructures.
Although the invention has been described with reference to exemplary embodiments, various modifications, substitutions, and equivalent arrangements may be implemented without departing from the scope and spirit of the invention. Accordingly, the scope of the present invention should not be limited solely to the embodiments disclosed herein but should be interpreted in accordance with the appended claims. , Claims:1. A Transformer-based deep learning system for accurate solar radiation prediction under dynamic atmospheric conditions, the system comprising:
a data acquisition module configured to collect meteorological and atmospheric data from one or more environmental sensing sources;
a preprocessing module operatively connected to the data acquisition module and configured to perform data cleaning, normalization, synchronization, and feature conditioning operations;
a sequence formation module configured to generate temporal time-series input sequences from the processed atmospheric data;
a Transformer-based deep learning engine configured to process the generated sequences using encoder-decoder architectures and positional encoding mechanisms;
a multi-head attention mechanism configured to capture long-range temporal dependencies and feature interactions among atmospheric variables;
a dynamic atmospheric adaptation module configured to modify prediction behaviour according to atmospheric variability; and
an explainability module configured to generate attention-based interpretations associated with solar radiation prediction outputs.
2. The system as claimed in claim 1, wherein the meteorological and atmospheric data comprise solar irradiance, temperature, humidity, wind speed, atmospheric pressure, cloud density, aerosol concentration, ultraviolet radiation levels, rainfall information, and solar zenith angle.
3. The system as claimed in claim 1, wherein the preprocessing module is configured to perform missing-value imputation, outlier removal, interpolation, feature scaling, signal smoothing, and temporal alignment of atmospheric datasets.
4. The system as claimed in claim 1, wherein the sequence formation module is configured to generate fixed-length or variable-length temporal windows corresponding to hourly, daily, weekly, or seasonal atmospheric observations.
5. The system as claimed in claim 1, wherein the Transformer-based deep learning engine comprises input embedding layers, positional encoding layers, encoder layers, decoder layers, feed-forward neural networks, residual connections, and normalization units.
6. The system as claimed in claim 1, wherein the multi-head attention mechanism computes attention weights corresponding to atmospheric variables to identify influential environmental parameters affecting solar radiation prediction.
7. The system as claimed in claim 1, wherein the dynamic atmospheric adaptation module is configured to detect atmospheric disturbances including cloud movement, rainfall variation, aerosol fluctuation, temperature change, and seasonal transition, and adaptively modify forecasting parameters accordingly.
8. The system as claimed in claim 1, wherein the explainability module is configured to generate feature importance graphs, attention heatmaps, temporal contribution analyses, confidence indicators, and predictive uncertainty visualizations.
9. The system as claimed in claim 1, wherein the prediction outputs comprise hourly irradiance forecasts, daily solar radiation predictions, multi-horizon forecasting outputs, and renewable energy optimization indicators for photovoltaic and smart-grid applications.
10. A method for accurate solar radiation prediction under dynamic atmospheric conditions, the method comprising:
collecting atmospheric and meteorological data from multiple environmental sources;
preprocessing the collected data by performing cleaning, normalization, synchronization, and feature conditioning operations;
generating temporal time-series sequences from the processed atmospheric data;
processing the generated sequences using a Transformer-based deep learning architecture;
applying multi-head attention mechanisms to capture temporal dependencies and atmospheric feature interactions;
dynamically adapting forecasting behaviour according to changing atmospheric conditions;
generating solar radiation prediction outputs; and
producing attention-based explainability information corresponding to the generated prediction outputs.