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Special Issue "Solar Forecasting"

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: 15 January 2018

Special Issue Editor

Guest Editor
Prof. Dr. John Boland

Barbara Hardy Institute and School of Information Technology and Mathematical Sciences, University of South Australia, Australia
Website | E-Mail
Interests: climate change and risk analysis; heat transfer and energy efficient buildings; water harvesting; ecological footprint; sustainable diet

Special Issue Information

Dear Colleagues,

Recently, significant advances have been made in solar forecasting on time scales from sub minute right through to several days ahead, using techniques that are specifically suited to each particular time scale. As well as the contributions of individual researchers, two projects, for which the editor has been a member; Task 46 of the International Energy Agency on solar forecasting, and the Australian Solar Energy Forecasting System (ASEFS), have added significantly to the knowledge in the field. Up to now, the work has focused principally on the expected value of solar energy at some future time, given the history up to a specific time, in other words, a point forecast.
The most recent endeavours, though, are aimed at what is alternatively called a probabilistic, interval or density forecast, in other words, putting error bounds on the point forecast. This is for a single location but some researchers have also been adding a spatial dimension. In addition, for risk analysis purposes, the synthetic generation of possible trajectories, also called scenario generation, has attracted interest. It is these topics that are of interest for this Special Edition of the journal.

Prof. Dr. John Boland
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 papers will be 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. Energies 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 1500 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

  • probabilistic forecast
  • spatial-temporal forecasting
  • solar energy
  • synthetic series
  • scenario generation
  • stochastic processes

Published Papers (3 papers)

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Research

Open AccessArticle Probabilistic Solar Forecasting Using Quantile Regression Models
Energies 2017, 10(10), 1591; doi:10.3390/en10101591
Received: 11 September 2017 / Revised: 3 October 2017 / Accepted: 6 October 2017 / Published: 13 October 2017
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Abstract
In this work, we assess the performance of three probabilistic models for intra-day solar forecasting. More precisely, a linear quantile regression method is used to build three models for generating 1 h–6 h-ahead probabilistic forecasts. Our approach is applied to forecasting solar irradiance
[...] Read more.
In this work, we assess the performance of three probabilistic models for intra-day solar forecasting. More precisely, a linear quantile regression method is used to build three models for generating 1 h–6 h-ahead probabilistic forecasts. Our approach is applied to forecasting solar irradiance at a site experiencing highly variable sky conditions using the historical ground observations of solar irradiance as endogenous inputs and day-ahead forecasts as exogenous inputs. Day-ahead irradiance forecasts are obtained from the Integrated Forecast System (IFS), a Numerical Weather Prediction (NWP) model maintained by the European Center for Medium-Range Weather Forecast (ECMWF). Several metrics, mainly originated from the weather forecasting community, are used to evaluate the performance of the probabilistic forecasts. The results demonstrated that the NWP exogenous inputs improve the quality of the intra-day probabilistic forecasts. The analysis considered two locations with very dissimilar solar variability. Comparison between the two locations highlighted that the statistical performance of the probabilistic models depends on the local sky conditions. Full article
(This article belongs to the Special Issue Solar Forecasting)
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Open AccessFeature PaperArticle Predictive Models for Photovoltaic Electricity Production in Hot Weather Conditions
Energies 2017, 10(7), 971; doi:10.3390/en10070971
Received: 23 May 2017 / Revised: 3 July 2017 / Accepted: 6 July 2017 / Published: 11 July 2017
Cited by 1 | PDF Full-text (5426 KB) | HTML Full-text | XML Full-text
Abstract
The process of finding a correct forecast equation for photovoltaic electricity production from renewable sources is an important matter, since knowing the factors affecting the increase in the proportion of renewable energy production and reducing the cost of the product has economic and
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The process of finding a correct forecast equation for photovoltaic electricity production from renewable sources is an important matter, since knowing the factors affecting the increase in the proportion of renewable energy production and reducing the cost of the product has economic and scientific benefits. This paper proposes a mathematical model for forecasting energy production in photovoltaic (PV) panels based on a self-organizing feature map (SOFM) model. The proposed model is compared with other models, including the multi-layer perceptron (MLP) and support vector machine (SVM) models. Moreover, a mathematical model based on a polynomial function for fitting the desired output is proposed. Different practical measurement methods are used to validate the findings of the proposed neural and mathematical models such as mean square error (MSE), mean absolute error (MAE), correlation (R), and coefficient of determination (R2). The proposed SOFM model achieved a final MSE of 0.0007 in the training phase and 0.0005 in the cross-validation phase. In contrast, the SVM model resulted in a small MSE value equal to 0.0058, while the MLP model achieved a final MSE of 0.026 with a correlation coefficient of 0.9989, which indicates a strong relationship between input and output variables. The proposed SOFM model closely fits the desired results based on the R2 value, which is equal to 0.9555. Finally, the comparison results of MAE for the three models show that the SOFM model achieved a best result of 0.36156, whereas the SVM and MLP models yielded 4.53761 and 3.63927, respectively. A small MAE value indicates that the output of the SOFM model closely fits the actual results and predicts the desired output. Full article
(This article belongs to the Special Issue Solar Forecasting)
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Open AccessArticle A Hybrid Method for Generation of Typical Meteorological Years for Different Climates of China
Energies 2016, 9(12), 1094; doi:10.3390/en9121094
Received: 22 October 2016 / Revised: 7 December 2016 / Accepted: 17 December 2016 / Published: 21 December 2016
Cited by 2 | PDF Full-text (1555 KB) | HTML Full-text | XML Full-text
Abstract
Since a representative dataset of the climatological features of a location is important for calculations relating to many fields, such as solar energy system, agriculture, meteorology and architecture, there is a need to investigate the methodology for generating a typical meteorological year (TMY).
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Since a representative dataset of the climatological features of a location is important for calculations relating to many fields, such as solar energy system, agriculture, meteorology and architecture, there is a need to investigate the methodology for generating a typical meteorological year (TMY). In this paper, a hybrid method with mixed treatment of selected results from the Danish method, the Festa-Ratto method, and the modified typical meteorological year method is proposed to determine typical meteorological years for 35 locations in six different climatic zones of China (Tropical Zone, Subtropical Zone, Warm Temperate Zone, Mid Temperate Zone, Cold Temperate Zone and Tibetan Plateau Zone). Measured weather data (air dry-bulb temperature, air relative humidity, wind speed, pressure, sunshine duration and global solar radiation), which cover the period of 1994–2015, are obtained and applied in the process of forming TMY. The TMY data and typical solar radiation data are investigated and analyzed in this study. It is found that the results of the hybrid method have better performance in terms of the long-term average measured data during the year than the other investigated methods. Moreover, the Gaussian process regression (GPR) model is recommended to forecast the monthly mean solar radiation using the last 22 years (1994–2015) of measured data. Full article
(This article belongs to the Special Issue Solar Forecasting)
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