Convolutional Neural Network for High-Resolution Cloud Motion Prediction from Hemispheric Sky Images

Crisosto, Cristian; Luiz, Eduardo Weide; Seckmeyer, Günther

doi:10.3390/en14030753

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…Deep learning, particularly convolutional neural networks (CNNs), has significantly enhanced the learning capabilities of intelligent algorithms [ 31 ]. CNNs, a subclass of Artificial Neural Networks (ANNs), are primarily employed for image analysis and have the ability to learn directly from data.…”

Section: Methodsmentioning

confidence: 99%

Sky Image Classification Based on Transfer Learning Approaches

Hernández-López,

Travieso-González,

Ajali-Hernández

2024

Sensors

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Cloudy conditions at a local scale pose a significant challenge for forecasting renewable energy generation through photovoltaic panels. Consequently, having real-time knowledge of sky conditions becomes highly valuable. This information could inform decision-making processes in system operations, such as determining whether conditions are favorable for activating a standalone system requiring a minimum level of radiation or whether sky conditions might lead to higher energy consumption than generation during adverse cloudy conditions. This research leveraged convolutional neural networks (CNNs) and transfer learning (TL) classification techniques, testing various architectures from the EfficientNet family and two ResNet models for classifying sky images. Cross-validation methods were applied across different experiments, where the most favorable outcome was achieved with the EfficientNetV2-B1 and EfficientNetV2-B2 models boasting a mean Accuracy of 98.09%. This study underscores the efficacy of the architectures employed for sky image classification, while also highlighting the models yielding the best results.

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Section: Methodsmentioning

confidence: 99%

Sky Image Classification Based on Transfer Learning Approaches

Hernández-López,

Travieso-González,

Ajali-Hernández

2024

Sensors

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“…CNNs represent a class of Deep Learning models specifically tailored to process visual data such as images and videos [26]. In the field of solar forecasting for photovoltaic systems, CNNs are used to process weather data in the form of images, including satellite imagery and radar data [27].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Short-Term Photovoltaic Power Plant Output Forecasting Using Sky Images and Deep Learning

et al. 2023

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With the steady increase in the use of renewable energy sources in the energy sector, new challenges arise, especially the unpredictability of these energy sources. This uncertainty complicates the management, planning, and development of energy systems. An effective solution to these challenges is short-term forecasting of the output of photovoltaic power plants. In this paper, a novel method for short-term production prediction was explored which involves continuous photography of the sky above the photovoltaic power plant. By analyzing a series of sky images, patterns can be identified to help predict future photovoltaic power generation. A hybrid model that integrates both a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) for short-term production forecasting was developed and tested. This model effectively detects spatial and temporal patterns from images and power output data, displaying considerable prediction accuracy. In particular, a 74% correlation was found between the model’s predictions and actual future production values, demonstrating the model’s efficiency. The results of this paper suggest that the hybrid CNN-LSTM model offers an improvement in prediction accuracy and practicality compared to traditional forecasting methods. This paper highlights the potential of Deep Learning in improving renewable energy practices, particularly in power prediction, contributing to the overall sustainability of power systems.

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“…Due to the RNN structure included in the model, its parallelism is poor and the training time is long. Crisosto et al [22] have also conducted cloud image movement prediction research. This method uses the pretrained VGG16 model for feature extraction, and then compares the similarity between the generated image and the real value.…”

Section: Introductionmentioning

confidence: 99%

CloudpredNet: An Ultra-Short-Term Movement Prediction Model for Ground-Based Cloud Image

Wei,

Zhu,

Guo

et al. 2023

IEEE Access

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Ground-based cloud images can provide information on weather and cloud conditions, which are important for cloud monitoring and PV power generation forecasting. Prediction of short-time cloud movement from images is a major means of intra-hourly irradiation forecasting for solar power generation and is also important for precipitation forecasting. However, there is a lack of advanced and complete methods for cloud movement prediction from ground-based cloud images, and traditional techniques based on image processing and motion vector calculations have difficulty in predicting cloud morphological changes, which makes accurate prediction of cloud motion (especially nonlinear motion) very challenging. Therefore, this paper proposes CloudpredNet, a ground-based cloud ultra-short-term movement prediction model based on an "encoder-generator" architecture. This paper also proposes a loss function dedicated to the time series prediction of ground-based cloud images and combines the attention mechanism to train the model. The model is validated on a publicly available dataset, and it is demonstrated that it has good performance in all metrics of cloud image generation for the next 10 minutes. Spatio-temporal prediction, ultra-short-term prediction, Transformer, ground-based cloud images INDEX TERMS

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Convolutional Neural Network for High-Resolution Cloud Motion Prediction from Hemispheric Sky Images

Cited by 5 publications

References 31 publications

Sky Image Classification Based on Transfer Learning Approaches

Sky Image Classification Based on Transfer Learning Approaches

Short-Term Photovoltaic Power Plant Output Forecasting Using Sky Images and Deep Learning

CloudpredNet: An Ultra-Short-Term Movement Prediction Model for Ground-Based Cloud Image

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