Federated Learning Photovoltaic Forecasting for Grid Integration
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Solution Overview
Problem
Conventional photovoltaic power forecasting methods are inadequate for accurately predicting power output, especially in regions with fluctuating weather conditions, and lack the ability to handle large-scale data integration due to data privacy and inefficiencies in conventional CHP plants, restricting renewable energy integration and grid operation reliability.
Innovation Solution
A federated learning-based regional photovoltaic power probabilistic forecasting method using a Bayesian LSTM neural network that incorporates uncertainty considerations while protecting data privacy, enabling localized data storage and globalized model optimization for enhanced forecasting accuracy and real-time dispatch decision support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional point forecast methods are used for photovoltaic power prediction, then the forecasting process is simple, but the accuracy is insufficient under fluctuating weather conditions and cannot reflect operation risks
Solution Approach 1:
The patent transforms the forecasting approach from deterministic point predictions to probabilistic predictions by introducing confidence intervals and probability distributions. This parameter change allows the model to output not only predicted values but also uncertainty measures, thereby improving forecasting accuracy under fluctuating weather conditions while maintaining model tractability through established statistical frameworks
Solution Approach 2:
The patent introduces an intermediary layer between raw weather data and power predictions by incorporating atmospheric transmission models and irradiance conversion models. These intermediary components transform complex weather parameters into meaningful solar irradiance estimates, which then feed into the power prediction model, thereby improving overall forecasting accuracy without requiring direct complex modeling of all weather factors
2Measurement precision
If data from different regions and institutions are aggregated for forecasting, then the forecasting coverage and accuracy improve, but data privacy and security issues arise
Solution Approach 1:
The patent segments the centralized data aggregation process into distributed federated learning nodes across different regions and institutions. Each node trains local models on its own data without sharing raw data, and only model parameters are exchanged with the central server. This segmentation enables multi-region forecasting accuracy improvement while preserving data privacy and security through cryptographic and architectural safeguards
Solution Approach 2:
The patent introduces a federated learning platform as an intermediary that coordinates model training across distributed nodes without requiring direct data sharing. The platform manages parameter aggregation, model updates, and security protocols, thereby enabling collaborative forecasting improvement while maintaining data isolation and security through the intermediary's controlled communication channels
3Adaptability or versatility
If conventional CHP plants operate in deterministic mode, then the operation control is simple, but the flexibility for renewable energy integration is limited
Solution Approach 1:
The patent transforms the static deterministic operation mode of CHP plants into a dynamic probabilistic operation mode that adapts to uncertain renewable energy inputs. The control system continuously updates probability distributions of power generation and heat demand based on real-time weather forecasts and actual measurements, enabling flexible adjustment of operating parameters while maintaining system stability through dynamic equilibrium maintenance
Solution Approach 2:
The patent changes the control parameters from fixed deterministic values to probabilistic distributions with confidence intervals. This allows the CHP plant to operate with flexible parameter ranges that adapt to renewable energy variability, improving integration flexibility while maintaining operational simplicity through standardized probabilistic control frameworks and automated decision-making algorithms
Data Source
AI summary
Disclosed is a federated learning-based regional photovoltaic power probability forecasting method, mainly comprising steps of: pinpointing all photovoltaic power stations in a region which participate in a federated learning framework for probability forecasting, collecting information within a period of time and corresponding photovoltaic power variables, and sampling the variables according to time sequence into a sample dataset; processing missing values and outliers in the sample dataset resulting from the step; splitting the sample data set of the photovoltaic power stations into a training set and a testing set according to a preset proportion; normalizing the training set and the testing set, respectively; creating a federated learning framework; building, by a central server, a global forecasting model based on forecast requirements, defining a training error function and a precision requirement, and distributing the network architecture and initialized parameters to all photovoltaic power stations.