Prediction of PIRP Values from Sky Images Using Deep Learning Techniques

Abstract

Introduction: The intermittency of solar energy necessitates accurate short-term forecasting for effective energy grid management. Pyranometer Irradiance Recorded Point (PIRP) values — representing instantaneous solar irradiance measurements (in W/m²) — can be predicted using deep learning models. This study introduces a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture to forecast PIRP values from sequential sky images, historical PIRP data, and cyclical time-based features, providing a scalable and low-cost alternative to conventional sensor-dependent methods.

Objectives: The objectives of this research are to develop a hybrid CNN-LSTM model that leverages spatial features from sky images and temporal dependencies from historical PIRP values, to enhance forecasting accuracy by integrating cyclical time features, and to demonstrate a scalable and cost-effective forecasting approach for smart grid operations, solar farms, and urban energy planning.

Methods: The

model utilizes a TimeDistributed CNN to extract spatial features from sequences of sky images and LSTM layers to capture temporal dependencies. Timestamp features are encoded using sine and cosine transformations. An attention mechanism is integrated to focus on the most informative parts of the sequence. The curated dataset, comprising 13,818 PIRP records and over 39,000 images, was preprocessed through timestamp alignment, filtering, and feature scaling. Training employed Adam optimization and Huber loss, with extensive evaluation through MAE, RMSE, Filtered MAPE, and R² Score metrics.

Results: The proposed model achieved a Mean Absolute Error (MAE) of 14.46, a Root Mean Squared Error (RMSE) of 30.03, a Filtered Mean Absolute Percentage Error (MAPE) of 9.85%, and an R² Score of 0.9737. It significantly outperformed baseline models such as CNN-only, LSTM-only, and Linear Regression, validating its effectiveness in short-term PIRP forecasting. Comparative evaluations confirmed the superiority of the hybrid CNN-LSTM approach with cyclical time feature integration and attention mechanism.

Conclusions: The hybrid CNN-LSTM model demonstrates robust performance by accurately capturing spatio-temporal dependencies inherent in PIRP values. Its ability to predict 15-minute-ahead irradiance values with high accuracy highlights its potential for real-world deployment in renewable energy forecasting. Future work will focus on expanding datasets across diverse geographical regions, integrating additional meteorological parameters, and exploring transformer-based architectures for further enhancements.