Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods

Fan, Lin; Zhang, Yao; Wang, Jianxue

doi:10.1016/j.ijforecast.2021.11.002

Cited by 61 publications

(17 citation statements)

References 133 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They could be used for power output of a solar panel prediction based on highdimensional data, e.g., satellite images or weather radar data, and for drying applications, prediction of the final moisture content and drying time of the dried product based on highdimensional data, e.g., thermal images. Convolutional neural networks (CNNs), computer vision (CV), long short-term memory (LSTM), SVM, and k-nearest neighbor (kNN) are used for intra-hour solar forecasting [67]. In addition, machine learning has been used for decision making in the solar energy field [68].…”

Section: The Applications Of Machine Learning For Solar Energy Issuesmentioning

confidence: 99%

Review of Energy-Related Machine Learning Applications in Drying Processes

Đaković,

Kljajić,

Milivojević

et al. 2023

Energies

View full text Add to dashboard Cite

Drying processes are among the most energy-intensive industrial processes. There is a need for development of the efficient methods needed for estimating, measuring, and reducing energy use. Different machine learning algorithms might provide some of the answers to these issues in a faster and less costly way, without the need for time-consuming and expensive experiments done at different scales of the dryers. The aim of this paper was to provide a comprehensive overview of machine learning applications for addressing energy-related challenges by exploration of different energy types and energy reduction opportunities. Also, the analysis of the applied algorithms, their specific applications and a critical evaluation of the obtained results are provided. The paper is based on the necessity of the improvements in energy use needed for drying related on the existing data. The overview of the ways for such achievements, and a general classification of machine learning algorithm are the background of the paper. The methods used are the machine learning techniques employed in different energy-related issues for drying processes. The paper focuses on the applications of artificial neural networks and other machine learning algorithms and models for different energy-related issues, including different energy types applications, challenges associated with energy consumption, and opportunities for energy reduction. Not only the applied algorithms, but also their specific applications, and the statistical analysis of the obtained results are also overviewed. Finally, a critical evaluation of the findings highlighting the potentials of machine learning algorithms in addressing energy-related challenges (such as estimation of energy consumption, opportunities for energy reduction, and use of different energy sources) is provided. The presented analysis underscored the effectiveness of machine learning applications for these purposes.

show abstract

Section: The Applications Of Machine Learning For Solar Energy Issuesmentioning

confidence: 99%

Review of Energy-Related Machine Learning Applications in Drying Processes

Đaković,

Kljajić,

Milivojević

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

“…Several types of neural networks can realize semantic segmentation; herein, the PSPNet network semantic segmentation algorithm (Figure 2) [11] was used for classification. In the PSPNet network, the netscope space pyramid pool structure was adopted, as shown in Figure 3.…”

Section: Cloud Edge Contour Extraction and Feature Point Recognitionmentioning

confidence: 99%

“…However, in the field of photovoltaic power generation, it has always been challenging to accurately predict the weather type and cloud movement [10,11]. However, in the field of photovoltaic power generation, it has always been challenging to accurately predict the weather type and cloud movement [10,11].…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation Algorithm-Based Calculation of Cloud Shadow Trajectory and Cloud Speed

2022

View full text Add to dashboard Cite

Cloud covering is an important factor affecting solar radiation and causes fluctuations in solar energy production. Therefore, real-time recognition and the prediction of cloud covering and the adjustment of the angle of photovoltaic panels to improve power generation are important research areas in the field of photovoltaic power generation. In this study, several methods, namely, the principle of depth camera measurement distance, semantic segmentation algorithm, and long- and short-term memory (LSTM) network were combined for cloud observation. The semantic segmentation algorithm was applied to identify and extract the cloud contour lines, determine the feature points, and calculate the cloud heights and geographic locations of the cloud shadows. The LSTM algorithm was used to predict the trajectory and speed of the cloud movement, achieve accurate and real-time detection, and track the clouds and the sun. Based on the results of these methods, the shadow area of the cloud on the ground was calculated. The recursive neural LSTM network was also used to predict the track and moving speed of the clouds according to the cloud centroid data of the cloud images at different times. The findings of this study can provide insights to establish a low-cost intelligent monitoring predicting system for cloud covering and power generation.

show abstract

“…The applications of these systems are wide, as there are several sectors in which the study of cloud cover is of vital importance. For example, they can be used from educational tools to sources of valuable data in fields such as meteorology, astronomy, forecasting systems in renewable energy plants [1], cloud state detection for airports, tourism, research, etc. This document presents a system that is considered low cost, as well as efficient and reliable, with high resolution image and that has, as an added value, a backup system that supplies daily curves of generated solar energy, which facilitates the detection of the sky state on a given day by observing this curve.…”

Section: Introductionmentioning

confidence: 99%

Continuous sky digitalization using images from an all-sky camera

González¹,

Santana-Suárez²,

Piñán-Roescher³

et al. 2024

RE&PQJ

View full text Add to dashboard Cite

The designed networked system captures and stores high and medium resolution sky images every 2 seconds. The IP camera employed is low-cost, omnidirectional, and its images are accessible from any point with Internet connection, both in real time and to a database, thanks to the configuration of a VPN network. The images obtained by the camera can be utilised for a variety of purposes but are of particular interest for those applications where a large volume of separate images is required for the characteristics of the sky, as the system also provides an innovative method of measuring solar energy, which provides an unambiguous view of the cloud state on any given day.

show abstract

Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods

Cited by 61 publications

References 133 publications

Review of Energy-Related Machine Learning Applications in Drying Processes

Review of Energy-Related Machine Learning Applications in Drying Processes

Semantic Segmentation Algorithm-Based Calculation of Cloud Shadow Trajectory and Cloud Speed

Continuous sky digitalization using images from an all-sky camera

Contact Info

Product

Resources

About