# Deep Learning for Inferring the Surface Solar Irradiance from Sky Imagery

## TL;DR

This paper introduces a deep learning approach using sky imagery and clustering features to accurately classify sky conditions and estimate solar irradiance, aiding photovoltaic energy prediction.

## Contribution

It presents a novel method combining clustering and deep neural networks for precise estimation of solar irradiance from sky images.

## Key findings

- Classification accuracy of 99.7% for clear/cloudy sky detection
- Irradiance estimation accuracy of 95% using deep neural networks
- Effective feature extraction with mini-batch k-means clustering

## Abstract

We present a novel approach to perform ground-based estimation and prediction of the surface solar irradiance with the view to predicting photovoltaic energy production. We propose the use of mini-batch k-means clustering to extract features, referred to as per cluster number of pixels (PCNP), from sky images taken by a low-cost fish eye camera. These features are first used to classify the sky as clear or cloudy using a single hidden layer neural network; the classification accuracy achieves 99.7%. If the sky is classified as cloudy, we propose to use a deep neural network having as input features the PCNP to predict intra-hour variability of the solar irradiance. Toward this objective, in this paper, we focus on estimating the deep neural network model relating the PCNP features and the solar irradiance, which is an important step before performing the prediction task. The proposed deep learning-based estimation approach is shown to have an accuracy of 95%.