1. A federated deep learning system for privacy-preserving solar radiation forecasting, the system comprising: a plurality of distributed energy nodes configured to locally train deep learning forecasting models using locally stored solar radiation and environmental datasets; a federated aggregation server configured to receive model updates from the distributed energy nodes and aggregate the model updates without accessing raw local datasets; a privacy-preserving module configured to secure the model updates prior to transmission; a communication module configured to exchange model parameters between the distributed energy nodes and the federated aggregation server; and a global model update module configured to generate and redistribute an updated global forecasting model to participating distributed energy nodes.

2. The system as claimed in claim 1, wherein the distributed energy nodes comprise solar farms, rooftop photovoltaic systems, renewable energy stations, smart buildings, or microgrids.

3. The system as claimed in claim 1, wherein the deep learning forecasting models comprise Long Short-Term Memory (LSTM) networks, Gated Recurrent Unit (GRU) networks, Transformer models, recurrent neural networks, convolutional neural networks, or hybrid deep learning architectures.

4. The system as claimed in claim 1, wherein the privacy-preserving module comprises differential privacy mechanisms configured to introduce perturbations into model parameters.

5. The system as claimed in claim 1, wherein the privacy-preserving module comprises encryption protocols configured to encrypt model updates prior to transmission.

6. The system as claimed in claim 1, wherein the federated aggregation server performs federated averaging or weighted aggregation for generating the global forecasting model.

7. The system as claimed in claim 1, wherein the communication module comprises a communication optimization engine configured to perform parameter compression, quantization, or gradient sparsification.

8. The system as claimed in claim 1, wherein the distributed energy nodes continuously retrain local forecasting models using updated local datasets for iterative collaborative learning.

9. The system as claimed in claim 1, wherein the federated aggregation server supports adaptive node participation based on computational resources or network conditions.

10. The system as claimed in claim 1, wherein the system is configured for smart grid management, renewable energy optimization, photovoltaic forecasting, or distributed energy management applications.

Description:FIELD OF THE INVENTION
The present invention generally relates to the fields of federated learning, artificial intelligence, renewable energy forecasting, and distributed energy management systems. More particularly, the invention relates to a federated deep learning framework for privacy-preserving solar radiation forecasting across geographically distributed energy systems such as solar farms, rooftop photovoltaic installations, microgrids, smart buildings, and renewable energy infrastructures. The disclosed framework enables collaborative model training among multiple distributed energy nodes without sharing raw local datasets, thereby ensuring data privacy, communication efficiency, scalability, and forecasting accuracy.
BACKGROUND OF THE INVENTION
Solar radiation forecasting plays a critical role in modern renewable energy systems and smart grid infrastructures. Accurate forecasting of solar irradiance is essential for efficient power generation scheduling, load balancing, grid stability, energy trading, and distributed energy resource management. With the increasing deployment of solar photovoltaic systems in residential, commercial, and industrial sectors, the need for intelligent forecasting systems has become increasingly important.
Conventional solar forecasting techniques generally rely on centralized machine learning and deep learning architectures. In such systems, raw data collected from geographically distributed energy nodes is transmitted to a centralized cloud server for training predictive models. These datasets often include solar irradiance measurements, weather conditions, environmental parameters, energy generation profiles, and operational characteristics of renewable energy systems.
However, centralized forecasting systems suffer from several technical limitations and operational drawbacks. One major issue is the risk associated with data privacy and confidentiality. Raw operational data collected from solar farms, rooftop photovoltaic systems, and microgrids may contain sensitive information related to energy consumption patterns, infrastructure behavior, and environmental conditions. Transmitting such data to external servers increases the risk of data leakage, cyberattacks, unauthorized access, and privacy violations.
Another limitation of centralized architectures is poor scalability. As the number of distributed energy nodes increases, the amount of raw data transmitted to the central server also increases significantly. This results in excessive bandwidth consumption, increased communication overhead, network congestion, and higher computational requirements at the central server. Such systems become inefficient for large-scale smart grid environments involving thousands of distributed energy resources.
Existing forecasting approaches also lack efficient collaborative learning mechanisms that allow geographically distributed energy systems to jointly improve forecasting performance without exposing local datasets. Many current systems are unable to simultaneously ensure forecasting accuracy, communication efficiency, privacy preservation, and scalability.
Furthermore, existing methods generally do not incorporate advanced communication optimization techniques such as gradient sparsification, parameter quantization, or adaptive model aggregation. As a result, repeated transmission of large deep learning model parameters significantly increases operational costs and delays model convergence.
Therefore, there exists a need for an improved solar radiation forecasting framework capable of enabling collaborative distributed learning while preserving local data privacy, minimizing communication overhead, improving scalability, and supporting real-time adaptive forecasting across distributed renewable energy systems.
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 federated deep learning framework for privacy-preserving solar radiation forecasting across distributed energy systems. The disclosed framework enables multiple geographically distributed energy nodes to collaboratively train deep learning forecasting models without sharing raw local datasets, thereby ensuring privacy preservation, communication efficiency, scalability, and improved forecasting accuracy.
In one embodiment, the system comprises a plurality of distributed energy nodes interconnected through a federated learning coordinator or aggregation server. The distributed energy nodes may include solar farms, rooftop photovoltaic systems, renewable energy stations, microgrids, smart buildings, industrial renewable infrastructures, and distributed smart grid environments.
Each distributed energy node locally stores environmental and solar radiation datasets including solar irradiance measurements, atmospheric parameters, weather conditions, humidity, temperature, wind speed, cloud coverage, and photovoltaic generation profiles. Local datasets remain stored within the corresponding node and are not transmitted externally.
Each node includes a local training engine configured to independently train a deep learning forecasting model using locally available datasets. The forecasting models may include Long Short-Term Memory (LSTM) networks, Gated Recurrent Unit (GRU) networks, recurrent neural networks, Transformer-based architectures, convolutional neural networks, hybrid neural architectures, or combinations thereof.
After completion of local training, each node generates model updates including model weights, gradients, parameter differences, or compressed parameter representations. The generated updates are encrypted and privacy protected prior to transmission.
The system further comprises a federated aggregation server configured to receive encrypted model updates from participating distributed energy nodes. The aggregation server combines the received model updates using federated averaging, weighted aggregation, adaptive aggregation, or optimization-based aggregation techniques to generate a global forecasting model.
In another embodiment, the invention comprises a privacy-preserving module configured to secure collaborative learning operations. The privacy-preserving module includes differential privacy mechanisms, secure aggregation protocols, homomorphic encryption techniques, secure multiparty computation methods, and encrypted communication channels.
The differential privacy mechanism introduces controlled perturbations or Gaussian noise into model parameters to prevent reconstruction of sensitive local information from shared model updates. Secure aggregation protocols ensure that the aggregation server cannot directly access individual node updates.
The invention further comprises a communication optimization module configured to reduce communication overhead associated with repeated federated learning operations. The communication optimization module performs parameter compression, quantization, gradient sparsification, removal of insignificant updates, adaptive synchronization, and selective parameter transmission to minimize bandwidth consumption and improve scalability.
During operation, distributed energy nodes continuously collect solar irradiance and meteorological data from local environments. Local forecasting models are independently trained using local datasets. Model updates are encrypted and transmitted to the federated aggregation server. The server aggregates the updates to generate an improved global forecasting model. The updated global model is redistributed to participating nodes for subsequent training iterations. The process repeats continuously to improve forecasting accuracy over time.
The proposed invention provides several technical advantages over conventional centralized forecasting systems. The framework preserves data privacy by eliminating raw data sharing, reduces communication overhead through optimized parameter exchange, improves scalability across geographically distributed energy nodes, and enhances forecasting accuracy through collaborative federated learning.
The invention is particularly suitable for smart grid management, renewable energy optimization, photovoltaic forecasting, microgrid control systems, intelligent energy management platforms, distributed energy infrastructures, and large-scale renewable energy forecasting environments.
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 federated deep learning framework for solar radiation forecasting that enables multiple distributed energy nodes to collaboratively train forecasting models without sharing raw local datasets.
Another object of the invention is to provide a privacy-preserving distributed forecasting architecture capable of protecting sensitive operational and environmental data associated with distributed energy systems.
Another object of the invention is to provide a federated aggregation mechanism configured to aggregate model updates from distributed nodes while preventing direct access to local training data.
Another object of the invention is to incorporate differential privacy techniques, encryption mechanisms, and secure aggregation protocols for preventing reconstruction or reverse engineering of local datasets.
Another object of the invention is to reduce communication overhead associated with federated learning through parameter compression, quantization, gradient sparsification, and adaptive communication optimization techniques.
Another object of the invention is to provide a scalable forecasting framework suitable for smart grids, solar farms, rooftop photovoltaic systems, renewable energy stations, and microgrid environments.
Another object of the invention is to improve solar radiation forecasting accuracy through collaborative deep learning using geographically distributed datasets.
Another object of the invention is to support continuous distributed learning and adaptive model updates for real-time renewable energy forecasting applications.
Another object of the invention is to provide a flexible forecasting framework compatible with multiple deep learning architectures including Long Short-Term Memory (LSTM) networks, Gated Recurrent Unit (GRU) networks, Transformer networks, convolutional neural networks, and hybrid deep learning models.
Another object of the invention is to improve energy optimization, grid stability, renewable energy scheduling, and intelligent power management within distributed energy infrastructures.
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 THE OVERALL FEDERATED DEEP LEARNING ARCHITECTURE COMPRISING DISTRIBUTED ENERGY NODES CONNECTED TO A FEDERATED AGGREGATION SERVER.
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 federated deep learning framework configured for privacy-preserving solar radiation forecasting across distributed energy systems.
In one embodiment, the system comprises a plurality of distributed energy nodes communicatively connected to a federated aggregation server through a communication network. The communication network may comprise wired communication networks, wireless communication networks, cloud infrastructures, edge computing environments, Internet of Things (IoT) communication systems, or combinations thereof.
The distributed energy nodes may include solar farms, rooftop photovoltaic systems, smart buildings, renewable energy stations, microgrids, distributed renewable infrastructures, or energy monitoring devices. Each node includes sensors configured to collect environmental and solar radiation information.
The collected data may include solar irradiance measurements, temperature, humidity, wind speed, atmospheric pressure, cloud density, rainfall, photovoltaic output power, energy consumption patterns, and environmental measurements associated with renewable energy systems.
Each distributed energy node comprises a preprocessing module configured to clean, normalize, transform, and organize the acquired datasets prior to local training. The preprocessing module may perform missing value imputation, outlier removal, noise filtering, temporal alignment, feature scaling, and data formatting operations.
The distributed energy node further comprises a local training engine configured to train a deep learning forecasting model using locally available datasets. The local training engine may implement LSTM networks, GRU networks, recurrent neural networks, Transformer-based neural architectures, convolutional neural networks, attention-based models, or hybrid deep learning architectures suitable for time-series forecasting.
During local training, the deep learning model learns forecasting patterns associated with solar radiation behavior, weather dynamics, environmental variations, and photovoltaic generation characteristics specific to the local environment.
After local model training, the node generates local model updates comprising neural network weights, gradients, parameter changes, compressed updates, or optimization parameters. The generated updates are processed by a privacy-preserving module prior to external transmission.
In one embodiment, the privacy-preserving module comprises an encryption engine configured to encrypt model updates using symmetric encryption, asymmetric encryption, homomorphic encryption, or secure cryptographic protocols.
The privacy-preserving module may further include differential privacy algorithms configured to inject controlled perturbations into model updates to prevent reconstruction of local datasets. Gaussian noise, Laplacian noise, randomized perturbation, or stochastic privacy-preserving mechanisms may be applied.
The encrypted and privacy-protected updates are transmitted to the federated aggregation server through secure communication channels.
The federated aggregation server comprises an aggregation engine configured to receive model updates from multiple distributed nodes and combine the updates to generate a global forecasting model.
The aggregation engine may implement federated averaging algorithms, weighted parameter aggregation, adaptive optimization techniques, gradient-based optimization, decentralized aggregation methods, or hierarchical aggregation strategies.
In one embodiment, weighted aggregation is performed according to node-specific parameters including local dataset size, data quality, computational capability, forecasting accuracy, or node reliability.
The generated global forecasting model is redistributed to participating distributed energy nodes for subsequent local training iterations. Continuous iterative learning improves forecasting accuracy while preserving local data privacy.
The system further includes a communication optimization module configured to minimize communication overhead during federated learning operations. The communication optimization module performs parameter quantization, gradient sparsification, update pruning, adaptive synchronization intervals, selective layer transmission, and model compression.
In another embodiment, insignificant model updates are removed prior to transmission to reduce bandwidth usage. Sparse gradient encoding and compressed parameter representation may also be utilized.
The invention further supports asynchronous federated learning in which distributed nodes participate independently based on local computational availability and network conditions.
The disclosed framework may additionally support edge computing architectures in which local training operations are performed at edge devices near the energy generation source, thereby reducing latency and improving real-time forecasting performance.
The system may also include an adaptive node selection mechanism configured to dynamically select participating nodes according to communication quality, computational resources, forecasting performance, or data relevance.
The disclosed invention provides substantial technical improvements over conventional centralized forecasting systems by preserving privacy, improving scalability, reducing communication overhead, supporting distributed collaborative learning, and enhancing renewable energy forecasting accuracy across geographically distributed energy infrastructures. , Claims:1. A federated deep learning system for privacy-preserving solar radiation forecasting, the system comprising:
a plurality of distributed energy nodes configured to locally train deep learning forecasting models using locally stored solar radiation and environmental datasets;
a federated aggregation server configured to receive model updates from the distributed energy nodes and aggregate the model updates without accessing raw local datasets;
a privacy-preserving module configured to secure the model updates prior to transmission;
a communication module configured to exchange model parameters between the distributed energy nodes and the federated aggregation server; and
a global model update module configured to generate and redistribute an updated global forecasting model to participating distributed energy nodes.
2. The system as claimed in claim 1, wherein the distributed energy nodes comprise solar farms, rooftop photovoltaic systems, renewable energy stations, smart buildings, or microgrids.
3. The system as claimed in claim 1, wherein the deep learning forecasting models comprise Long Short-Term Memory (LSTM) networks, Gated Recurrent Unit (GRU) networks, Transformer models, recurrent neural networks, convolutional neural networks, or hybrid deep learning architectures.
4. The system as claimed in claim 1, wherein the privacy-preserving module comprises differential privacy mechanisms configured to introduce perturbations into model parameters.
5. The system as claimed in claim 1, wherein the privacy-preserving module comprises encryption protocols configured to encrypt model updates prior to transmission.
6. The system as claimed in claim 1, wherein the federated aggregation server performs federated averaging or weighted aggregation for generating the global forecasting model.
7. The system as claimed in claim 1, wherein the communication module comprises a communication optimization engine configured to perform parameter compression, quantization, or gradient sparsification.
8. The system as claimed in claim 1, wherein the distributed energy nodes continuously retrain local forecasting models using updated local datasets for iterative collaborative learning.
9. The system as claimed in claim 1, wherein the federated aggregation server supports adaptive node participation based on computational resources or network conditions.
10. The system as claimed in claim 1, wherein the system is configured for smart grid management, renewable energy optimization, photovoltaic forecasting, or distributed energy management applications.