Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
2.9
Journals
Atmosphere
Special Issues
Machine Learning for Solar Radiation Estimation
share announcement

Machine Learning for Solar Radiation Estimation

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (5 February 2021) | Viewed by 11150

Special Issue Editor

Dr. Carlos Casanova-Mateo

E-Mail
Guest Editor
Department of Civil Engineering: Construction, Infrastructure and Transport, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: meteorology (observation and forecasting); energy; climate and environmental applications; remote sensing; soft-computing and machine learning algorithms; metaheuristics optimization techniques

Special Issue Information

Dear Colleagues,

The interest in solar radiation prediction has increased greatly in recent times as a direct consequence of the exponential grow in the use of renewable energies. In this regard, a large number of different techniques have been developed to predict solar (global, direct, and/or diffuse) radiation: empirical models, numerical weather models, satellite-based schemes, etc. Among all of them, machine learning techniques have proven their capacities as a reliable and cost-efficient alternative to the more traditional approaches, showing their high capacity for obtaining robust results in solar radiation estimation problems using different sets of input variables.
This Special Issue deals with machine learning methods in solar radiation prediction, at any time horizon and in any part of the world. Articles discussing novel machine learning-based predictive approaches, original works using innovative input data as predictive variables, new algorithms or revisited algorithms providing good solutions to difficult problems in solar radiation estimation are welcome.

Dr. Carlos Casanova-Mateo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Solar energy problems
  • Soft-computing and machine learning techniques
  • Climate change impact in energy systems
  • Energy systems management and development

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 2306 KiB  
Article
Solar Photovoltaic Forecasting of Power Output Using LSTM Networks
by Maria Konstantinou, Stefani Peratikou and Alexandros G. Charalambides
Atmosphere 2021, 12(1), 124; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12010124 - 18 Jan 2021
Cited by 50 | Viewed by 5437
Abstract
The penetration of renewable energies has increased during the last decades since it has become an effective solution to the world’s energy challenges. Among all renewable energy sources, photovoltaic (PV) technology is the most immediate way to convert solar radiation into electricity. Nevertheless, [...] Read more.
The penetration of renewable energies has increased during the last decades since it has become an effective solution to the world’s energy challenges. Among all renewable energy sources, photovoltaic (PV) technology is the most immediate way to convert solar radiation into electricity. Nevertheless, PV power output is affected by several factors, such as location, clouds, etc. As PV plants proliferate and represent significant contributors to grid electricity production, it becomes increasingly important to manage their inherent alterability. Therefore, solar PV forecasting is a pivotal factor to support reliable and cost-effective grid operation and control. In this paper, a stacked long short-term memory network, which is a significant component of the deep recurrent neural network, is considered for the prediction of PV power output for 1.5 h ahead. Historical data of PV power output from a PV plant in Nicosia, Cyprus, were used as input to the forecasting model. Once the model was defined and trained, the model performance was assessed qualitative (by graphical tools) and quantitative (by calculating the Root Mean Square Error (RMSE) and by applying the k-fold cross-validation method). The results showed that our model can predict well, since the RMSE gives a value of 0.11368, whereas when applying the k-fold cross-validation, the mean of the resulting RMSE values is 0.09394 with a standard deviation 0.01616. Full article
(This article belongs to the Special Issue Machine Learning for Solar Radiation Estimation)
►▼ Show Figures

Figure 1

19 pages, 7599 KiB  
Article
Assessment and Correction of Solar Radiation Measurements with Simple Neural Networks
by Jason Kelley
Atmosphere 2020, 11(11), 1160; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos11111160 - 27 Oct 2020
Cited by 1 | Viewed by 2154
Abstract
Solar radiation received at the Earth’s surface provides the energy driving all micro-meteorological phenomena. Local solar radiation measurements are used to estimate energy mediated processes such as evapotranspiration (ET); this information is important in managing natural resources. However, the technical requirements to reliably [...] Read more.
Solar radiation received at the Earth’s surface provides the energy driving all micro-meteorological phenomena. Local solar radiation measurements are used to estimate energy mediated processes such as evapotranspiration (ET); this information is important in managing natural resources. However, the technical requirements to reliably measure solar radiation limits more extensive adoption of data-driven management. High-quality radiation sensors are expensive, delicate, and require skill to maintain. In contrast, low-cost sensors are widely available, but may lack long-term reliability and intra-sensor repeatability. As weather stations measure solar radiation and other parameters simultaneously, machine learning can be used to integrate various types of environmental data, identify periods of erroneous measurements, and estimate corrected values. We demonstrate two case studies in which we use neural networks (NN) to augment direct radiation measurements with data from co-located sensors, and generate radiation estimates with comparable accuracy to the data typically available from agro-meteorology networks. NN models that incorporated radiometer data reproduced measured radiation with an R2 of 0.9–0.98, and RMSE less than 100 Wm−2, while models using only weather parameters obtained R2 less than 0.75 and RMSE greater than 140 Wm−2. These cases show that a simple NN implementation can complement standard procedures for estimating solar radiation, create opportunities to measure radiation at low-cost, and foster adoption of data-driven management. Full article
(This article belongs to the Special Issue Machine Learning for Solar Radiation Estimation)
►▼ Show Figures

Figure 1

20 pages, 6624 KiB  
Article
Estimating Surface Downward Longwave Radiation Using Machine Learning Methods
by Chunjie Feng, Xiaotong Zhang, Yu Wei, Weiyu Zhang, Ning Hou, Jiawen Xu, Kun Jia, Yunjun Yao, Xianhong Xie, Bo Jiang, Jie Cheng and Xiang Zhao
Atmosphere 2020, 11(11), 1147; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos11111147 - 22 Oct 2020
Cited by 14 | Viewed by 2631
Abstract
The downward longwave radiation (Ld, 4–100 μm) is a major component of research for the surface radiation energy budget and balance. In this study, we applied five machine learning methods, namely artificial neural network (ANN), support vector regression (SVR), gradient [...] Read more.
The downward longwave radiation (Ld, 4–100 μm) is a major component of research for the surface radiation energy budget and balance. In this study, we applied five machine learning methods, namely artificial neural network (ANN), support vector regression (SVR), gradient boosting regression tree (GBRT), random forest (RF), and multivariate adaptive regression spline (MARS), to estimate Ld using ground measurements collected from 27 Baseline Surface Radiation Network (BSRN) stations. Ld measurements in situ were used to validate the accuracy of Ld estimation models on daily and monthly time scales. A comparison of the results demonstrated that the estimates on the basis of the GBRT method had the highest accuracy, with an overall root-mean-square error (RMSE) of 17.50 W m−2 and an R value of 0.96 for the test dataset on a daily time scale. These values were 11.19 W m−2 and 0.98, respectively, on a monthly time scale. The effects of land cover and elevation were further studied to comprehensively evaluate the performance of each machine learning method. All machine learning methods achieved better results over the grass land cover type but relatively worse results over the tundra. GBRT, RF, and MARS methods were found to show good performance at both the high- and low-altitude sites. Full article
(This article belongs to the Special Issue Machine Learning for Solar Radiation Estimation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop