# Deep Photovoltaic Nowcasting

arXiv: https://arxiv.org/abs/1810.06327 · HTML: https://mlsanity.com/paper/1810.06327/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2018.

## Built on by (most cited first, 21 of 21)

- [Benchmarking of Deep Learning Irradiance Forecasting Models from Sky Images -- an in-depth Analysis](https://mlsanity.com/paper/2102.00721/) (2021)
- [Diffusion Models for High-Resolution Solar Forecasts](https://mlsanity.com/paper/2302.00170/) (2023)
- [ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy](https://mlsanity.com/paper/2104.12419/) (2021)
- [SKIPP'D: a SKy Images and Photovoltaic Power Generation Dataset for Short-term Solar Forecasting](https://mlsanity.com/paper/2207.00913/) (2022)
- [SPIN: Simplifying Polar Invariance for Neural networks Application to vision-based irradiance forecasting](https://mlsanity.com/paper/2111.14507/) (2021)
- [LightLLM: A Versatile Large Language Model for Predictive Light Sensing](https://mlsanity.com/paper/2411.15211/) (2024)
- [End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting](https://mlsanity.com/paper/2304.07151/) (2023)
- [A Moment in the Sun: Solar Nowcasting from Multispectral Satellite Data using Self-Supervised Learning](https://mlsanity.com/paper/2112.13974/) (2021)
- [Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data](https://mlsanity.com/paper/2403.01653/) (2024)
- [A review on physical and data-driven based nowcasting methods using sky images](https://mlsanity.com/paper/2105.02959/) (2021)
- [Sky-image-based solar forecasting using deep learning with multi-location data: training models locally, globally or via transfer learning?](https://mlsanity.com/paper/2211.02108/) (2022)
- [Open-Source Ground-based Sky Image Datasets for Very Short-term Solar Forecasting, Cloud Analysis and Modeling: A Comprehensive Survey](https://mlsanity.com/paper/2211.14709/) (2022)
- [SkyGPT: Probabilistic Short-term Solar Forecasting Using Synthetic Sky Videos from Physics-constrained VideoGPT](https://mlsanity.com/paper/2306.11682/) (2023)
- [Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction](https://mlsanity.com/paper/2411.10921/) (2024)
- [Solar Multimodal Transformer: Intraday Solar Irradiance Predictor using Public Cameras and Time Series](https://mlsanity.com/paper/2503.00250/) (2025)
- [Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions](https://mlsanity.com/paper/2206.03207/) (2022)
- [A Meta-Analysis of Solar Forecasting Based on Skill Score](https://mlsanity.com/paper/2208.10536/) (2022)
- [Computational Imaging for Long-Term Prediction of Solar Irradiance](https://mlsanity.com/paper/2409.12016/) (2024)
- [Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks](https://mlsanity.com/paper/2210.12310/) (2022)
- [Forecasting Solar Energy Using a Single Image](https://mlsanity.com/paper/2604.21982/) (2026)
- [Modeling spatio-temporal locality in multi-step forecasting of geo-referenced time series](https://mlsanity.com/paper/2608.25698/) (2026)

## Abstract (arXiv)

Predicting the short-term power output of a photovoltaic panel is an important task for the efficient management of smart grids. Short-term forecasting at the minute scale, also known as nowcasting, can benefit from sky images captured by regular cameras and installed close to the solar panel. However, estimating the weather conditions from these images---sun intensity, cloud appearance and movement, etc.---is a very challenging task that the community has yet to solve with traditional computer vision techniques. In this work, we propose to learn the relationship between sky appearance and the future photovoltaic power output using deep learning. We train several variants of convolutional neural networks which take historical photovoltaic power values and sky images as input and estimate photovoltaic power in a very short term future. In particular, we compare three different architectures based on: a multi-layer perceptron (MLP), a convolutional neural network (CNN), and a long short term memory (LSTM) module. We evaluate our approach quantitatively on a dataset of photovoltaic power values and corresponding images gathered in Kyoto, Japan. Our experiments reveal that the MLP network, already used similarly in previous work, achieves an RMSE skill score of 7% over the commonly-used persistence baseline on the 1-minute future photovoltaic power prediction task. Our CNN-based network improves upon this with a 12% skill score. In contrast, our LSTM-based model, which can learn the temporal dependencies in the data, achieves a 21% RMSE skill score, thus outperforming all other approaches.