Renewable Energy Forecasting Using Satellite Imaging and Neural Networks
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Solution Overview
Problem
Existing approaches for forecasting renewable energy generation, such as photovoltaic (PV) energy, face limitations in accuracy due to reliance on single weather parameters and lack of comprehensive system analysis, leading to suboptimal performance especially under varying weather conditions.
Innovation Solution
A holistic system and method that incorporates derate factors of PV panels, regional and site-specific weather variability, and cross-view imaging from ground-based and geo-stationary satellites, utilizing LASSO-Elastic Net regularizations and multilayer perceptron trained with particle swarm optimization for robust forecasting, including cloud shading profiles derived from convolutional-time-dependent neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources and complex modeling techniques are used, then forecasting accuracy is improved, but system complexity increases
Solution Approach 1:
The patent combines multiple data sources (satellite imagery, ground-based sensors, weather models) and integrates them through a unified machine learning framework that processes all inputs simultaneously to generate forecasts, thereby improving accuracy while managing complexity through integration rather than separate systems
Solution Approach 2:
The machine learning model serves multiple functions: it processes satellite data, sensor data, and weather model outputs; performs cloud detection, shading profile generation, and energy generation forecasting; and adapts to different time horizons (short-term and long-term predictions), reducing the need for separate specialized systems
2Reliability
If comprehensive weather parameters and system inputs are collected, then model robustness is improved, but data processing requirements increase
Solution Approach 1:
The system performs preliminary processing of data inputs before main analysis, including pre-processing satellite imagery to detect clouds and generate shading profiles, and pre-processing sensor data to extract relevant features, thereby reducing the complexity of subsequent processing stages
Solution Approach 2:
The machine learning model automatically selects and weights relevant features from the comprehensive input data, and self-adjusts to handle different weather conditions and data quality levels, reducing the need for manual data processing intervention
Data Source
AI summary
Systems and methods for forecasting renewable energy generation using holistic weather and system analysis are provided. Derate factors of energy sources can be considered, along with regional weather variability, site specific weather, and a cross-view of the sky from ground based and/or geo-stationary satellite imaging. The forecast can include short-term forecasting and long-term forecasting.