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Special Issue "Forecasting Methods and Measurements of Forecasting Errors for Renewable Energy Sources"

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

Deadline for manuscript submissions: closed (15 October 2015)

Special Issue Editors

Guest Editor
Prof. Dr. Guido Carpinelli

Department of Electrical Engineering and Information Technologies, University of Napoli Federico II, Naples, Italy
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Interests: smart grid; renewable energy; power quality
Guest Editor
Dr. Anna Rita Di Fazio

Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino (FR), Italy
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Interests: Smart Grid, Renewable Energy, Reliability

Special Issue Information

Dear Colleagues,

Modern electrical distribution systems are characterized by the simultaneous presence of different distributed resources that actively participate in the system operation and that can contribute to enhance the system efficiency and to reduce greenhouse gas emissions. However, the inclusion of distributed resources into the networks makes the planning and operation of the distribution systems more complex and new research contributions are essential to guarantee their correct behavior.

Among distributed resources, renewable energy systems, such as photovoltaic power plant and wind farms, are of particular interest, thanks to the technical, environmental, and economic benefits their uses involve. Unfortunately, uncertainties related to the intermittent nature of solar and wind energy have a negative impact on the efficient, reliable, and secure operation of electrical power systems. Then, accurate methods for forecasting wind and photovoltaic generation, as well as appropriate measures to quantify the goodness of the previsions, are mandatory for the development of electrical systems.

We invite the submission of original and unpublished contributions discussing forecasting methods and measurements of errors for renewable energy sources. Review papers will also be taken in consideration for publication. Papers involving cooperation among researchers from academia, industries, and governments will be welcome to foster interactions among stakeholders.

Prof. Dr. Guido Carpinelli
Dr. Anna Rita Di Fazio
Guest Editors

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 1600 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

  • Forecasting methods for renewable energy sources
  • Measurements of forecasting errors and their application
  • Deterministic and probabilistic approaches
  • Forecasting methods for Smart Grids
  • Application of forecasting to the decision-making processes
  • Forecasting and optimal operation of distribution networks with renewable energy sources
  • Actual applications of forecasting tools

Published Papers (8 papers)

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Research

Open AccessArticle Solar Radiation Forecasting, Accounting for Daily Variability
Energies 2016, 9(3), 200; https://doi.org/10.3390/en9030200
Received: 30 October 2015 / Revised: 14 February 2016 / Accepted: 7 March 2016 / Published: 15 March 2016
Cited by 1 | PDF Full-text (4911 KB) | HTML Full-text | XML Full-text
Abstract
Radiation forecast accounting for daily and instantaneous variability was pursued by means of a new bi-parametric statistical model that builds on a model previously proposed by the same authors. The statistical model is developed with direct reference to the Liu-Jordan clear sky theoretical
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Radiation forecast accounting for daily and instantaneous variability was pursued by means of a new bi-parametric statistical model that builds on a model previously proposed by the same authors. The statistical model is developed with direct reference to the Liu-Jordan clear sky theoretical expression but is not bound by a specific clear sky model; it accounts separately for the mean daily variability and for the variation of solar irradiance during the day by means of two corrective parameters. This new proposal allows for a better understanding of the physical phenomena and improves the effectiveness of statistical characterization and subsequent simulation of the introduced parameters to generate a synthetic solar irradiance time series. Furthermore, the analysis of the experimental distributions of the two parameters’ data was developed, obtaining opportune fittings by means of parametric analytical distributions or mixtures of more than one distribution. Finally, the model was further improved toward the inclusion of weather prediction information in the solar irradiance forecasting stage, from the perspective of overcoming the limitations of purely statistical approaches and implementing a new tool in the frame of solar irradiance prediction accounting for weather predictions over different time horizons. Full article
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Open AccessArticle Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model
Energies 2016, 9(2), 109; https://doi.org/10.3390/en9020109
Received: 17 June 2015 / Revised: 20 January 2016 / Accepted: 22 January 2016 / Published: 17 February 2016
Cited by 32 | PDF Full-text (778 KB) | HTML Full-text | XML Full-text
Abstract
Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA). This method’s performance is well studied for a large number of prediction problems. The other is a multivariate model developed
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Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA). This method’s performance is well studied for a large number of prediction problems. The other is a multivariate model developed using a nonlinear autoregressive exogenous artificial neural network (NARX). This uses the variables: barometric pressure, air temperature, wind direction and solar radiation or relative humidity, as well as delayed wind speed. Both models were developed from two databases from two sites: an hourly average measurements database from La Mata, Oaxaca, Mexico, and a ten minute average measurements database from Metepec, Hidalgo, Mexico. The main objective was to compare the impact of the various meteorological variables on the performance of the multivariate model of wind speed prediction with respect to the high performance univariate linear model. The NARX model gave better results with improvements on the ARIMA model of between 5.5% and 10. 6% for the hourly database and of between 2.3% and 12.8% for the ten minute database for mean absolute error and mean squared error, respectively. Full article
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Open AccessArticle A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting
Energies 2016, 9(1), 55; https://doi.org/10.3390/en9010055
Received: 15 October 2015 / Revised: 28 December 2015 / Accepted: 11 January 2016 / Published: 19 January 2016
Cited by 10 | PDF Full-text (1522 KB) | HTML Full-text | XML Full-text
Abstract
We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed
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We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statistics such as mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), relative MBE (rMBE), mean percentage error (MPE) and relative RMSE (rRMSE). This work provides findings on how forecasts from individual inverters will improve the total solar power generation forecast of the PV system. Full article
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Open AccessArticle Enhanced Predictive Current Control of Three-Phase Grid-Tied Reversible Converters with Improved Switching Patterns
Energies 2016, 9(1), 41; https://doi.org/10.3390/en9010041
Received: 2 November 2015 / Revised: 1 December 2015 / Accepted: 30 December 2015 / Published: 13 January 2016
Cited by 4 | PDF Full-text (5022 KB) | HTML Full-text | XML Full-text
Abstract
A predictive current control strategy can realize flexible regulation of three-phase grid-tied converters based on system behaviour prediction and cost function minimization. However, when the predictive current control strategy with conventional switching patterns is adopted, the predicted duration time for voltage vectors turns
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A predictive current control strategy can realize flexible regulation of three-phase grid-tied converters based on system behaviour prediction and cost function minimization. However, when the predictive current control strategy with conventional switching patterns is adopted, the predicted duration time for voltage vectors turns out to be negative in some cases, especially under the conditions of bidirectional power flows and transient situations, leading to system performance deteriorations. This paper aims to clarify the real reason for this phenomenon under bidirectional power flows, i.e., rectifier mode and inverter mode, and, furthermore, seeks to propose effective solutions. A detailed analysis of instantaneous current variations under different conditions was conducted. An enhanced predictive current control strategy with improved switching patterns was then proposed. An experimental platform was built based on a commercial converter produced by Danfoss, and moreover, relative experiments were carried out, confirming the superiority of the proposed scheme. Full article
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Open AccessArticle Error Assessment of Solar Irradiance Forecasts and AC Power from Energy Conversion Model in Grid-Connected Photovoltaic Systems
Energies 2016, 9(1), 8; https://doi.org/10.3390/en9010008
Received: 15 October 2015 / Revised: 6 December 2015 / Accepted: 11 December 2015 / Published: 24 December 2015
Cited by 9 | PDF Full-text (8232 KB) | HTML Full-text | XML Full-text
Abstract
Availability of effective estimation of the power profiles of photovoltaic systems is essential for studying how to increase the share of intermittent renewable sources in the electricity mix of many countries. For this purpose, weather forecasts, together with historical data of the meteorological
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Availability of effective estimation of the power profiles of photovoltaic systems is essential for studying how to increase the share of intermittent renewable sources in the electricity mix of many countries. For this purpose, weather forecasts, together with historical data of the meteorological quantities, provide fundamental information. The weak point of the forecasts depends on variable sky conditions, when the clouds successively cover and uncover the solar disc. This causes remarkable positive and negative variations in the irradiance pattern measured at the photovoltaic (PV) site location. This paper starts from 1 to 3 days-ahead solar irradiance forecasts available during one year, with a few points for each day. These forecasts are interpolated to obtain more irradiance estimations per day. The estimated irradiance data are used to classify the sky conditions into clear, variable or cloudy. The results are compared with the outcomes of the same classification carried out with the irradiance measured in meteorological stations at two real PV sites. The occurrence of irradiance spikes in “broken cloud” conditions is identified and discussed. From the measured irradiance, the Alternating Current (AC) power injected into the grid at two PV sites is estimated by using a PV energy conversion model. The AC power errors resulting from the PV model with respect to on-site AC power measurements are shown and discussed. Full article
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Open AccessArticle A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search
Energies 2015, 8(11), 12388-12408; https://doi.org/10.3390/en81112317
Received: 29 August 2015 / Revised: 15 October 2015 / Accepted: 20 October 2015 / Published: 3 November 2015
Cited by 8 | PDF Full-text (2265 KB) | HTML Full-text | XML Full-text
Abstract
The uncertainty and regularity of wind power generation are caused by wind resources’ intermittent and randomness. Such volatility brings severe challenges to the wind power grid. The requirements for ultrashort-term and short-term wind power forecasting with high prediction accuracy of the model used,
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The uncertainty and regularity of wind power generation are caused by wind resources’ intermittent and randomness. Such volatility brings severe challenges to the wind power grid. The requirements for ultrashort-term and short-term wind power forecasting with high prediction accuracy of the model used, have great significance for reducing the phenomenon of abandoned wind power , optimizing the conventional power generation plan, adjusting the maintenance schedule and developing real-time monitoring systems. Therefore, accurate forecasting of wind power generation is important in electric load forecasting. The echo state network (ESN) is a new recurrent neural network composed of input, hidden layer and output layers. It can approximate well the nonlinear system and achieves great results in nonlinear chaotic time series forecasting. Besides, the ESN is simpler and less computationally demanding than the traditional neural network training, which provides more accurate training results. Aiming at addressing the disadvantages of standard ESN, this paper has made some improvements. Combined with the complementary advantages of particle swarm optimization and tabu search, the generalization of ESN is improved. To verify the validity and applicability of this method, case studies of multitime scale forecasting of wind power output are carried out to reconstruct the chaotic time series of the actual wind power generation data in a certain region to predict wind power generation. Meanwhile, the influence of seasonal factors on wind power is taken into consideration. Compared with the classical ESN and the conventional Back Propagation (BP) neural network, the results verify the superiority of the proposed method. Full article
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Open AccessArticle An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power
Energies 2015, 8(9), 10293-10314; https://doi.org/10.3390/en80910293
Received: 17 June 2015 / Revised: 3 August 2015 / Accepted: 11 September 2015 / Published: 21 September 2015
Cited by 10 | PDF Full-text (375 KB) | HTML Full-text | XML Full-text
Abstract
Currently, among renewable distributed generation systems, wind generators are receiving a great deal of interest due to the great economic, technological, and environmental incentives they involve. However, the uncertainties due to the intermittent nature of wind energy make it difficult to operate electrical
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Currently, among renewable distributed generation systems, wind generators are receiving a great deal of interest due to the great economic, technological, and environmental incentives they involve. However, the uncertainties due to the intermittent nature of wind energy make it difficult to operate electrical power systems optimally and make decisions that satisfy the needs of all the stakeholders of the electricity energy market. Thus, there is increasing interest determining how to forecast wind power production accurately. Most the methods that have been published in the relevant literature provided deterministic forecasts even though great interest has been focused recently on probabilistic forecast methods. In this paper, an advanced probabilistic method is proposed for short-term forecasting of wind power production. A mixture of two Weibull distributions was used as a probability function to model the uncertainties associated with wind speed. Then, a Bayesian inference approach with a particularly-effective, autoregressive, integrated, moving-average model was used to determine the parameters of the mixture Weibull distribution. Numerical applications also are presented to provide evidence of the forecasting performance of the Bayesian-based approach. Full article
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Open AccessArticle The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation
Energies 2015, 8(9), 9594-9619; https://doi.org/10.3390/en8099594
Received: 23 June 2015 / Revised: 7 August 2015 / Accepted: 20 August 2015 / Published: 3 September 2015
Cited by 19 | PDF Full-text (3839 KB) | HTML Full-text | XML Full-text
Abstract
A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (“WIRE”) with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting the power
[...] Read more.
A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (“WIRE”) with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field and identifying the main areas for improving accuracy in the future. Full article
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