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Special Issue "Solar and Wind Energy Forecasting"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Energy Sources".

Deadline for manuscript submissions: closed (20 December 2018).

Special Issue Editors

Guest Editor
Prof. Dr. Sonia Leva

Research Group in Electrical Engineering, Dipartimento di Energia, Politecnico di Milano (Campus Bovisa), Piazza Leonardo da Vinci 32, Milano, Italy
Website | E-Mail
Phone: +390223993709
Interests: renewable energy sorces, storage systems, smart grid, PV modelling and forecasting, microgrids, electric vehicles
Guest Editor
Dr. Emanuele Ogliari

Department of Energy, Politecnico di Milano, via La masa, 34, 20156 Milano MI, Italy
Website | E-Mail
Interests: photovoltaics; PV power forecasting; machine learning

Special Issue Information

Dear Colleagues,

The journal Energies (ISSN 1996-1073, IF 2.262) is currently running a Special Issue entitled "Solar and Wind Energy Forecasting". Prof. Dr. Sonia Leva and Dr. Emanuele Ogliari (Politecnico di Milano, Milano, Italy) are serving as Guest Editors for this issue. We think you could make an excellent contribution based on your expertise.

The renewable-energy-based generation of electricity is currently experiencing rapid growth in electric grids. The intermittent input from the renewable energy sources (RES) as a consequence brings problems in balancing the energy supply and demand.

Thus, forecasting of RES power generation is vital to help the grid operators to better manage the electric balance between power demand and supply, and to improve the penetration of distributed renewable energy sources and, in stand-alone hybrid systems, for the optimum size of all its components and to improve the reliability of the isolated systems.

This Special Issue of Energies, “Solar and Wind Energy Forecasting”, is intended for disseminating new promising methods and techniques to forecast the output power and energy of intermittent renewable energy sources.

Prof. Dr. Sonia Leva
Dr. Emanuele Ogliari
Guest Editors

Manuscript Submission Information

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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 semimonthly 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 1800 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

  • RES integration
  • Forecasting techniques
  • Machine Learning
  • Computational Intelligence
  • Optimization
  • PV system
  • Wind system

Published Papers (26 papers)

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Open AccessArticle
Minute-Scale Forecasting of Wind Power—Results from the Collaborative Workshop of IEA Wind Task 32 and 36
Energies 2019, 12(4), 712; https://doi.org/10.3390/en12040712
Received: 14 December 2018 / Revised: 13 February 2019 / Accepted: 14 February 2019 / Published: 21 February 2019
Cited by 1 | PDF Full-text (1968 KB) | HTML Full-text | XML Full-text
Abstract
The demand for minute-scale forecasts of wind power is continuously increasing with the growing penetration of renewable energy into the power grid, as grid operators need to ensure grid stability in the presence of variable power generation. For this reason, IEA Wind Tasks [...] Read more.
The demand for minute-scale forecasts of wind power is continuously increasing with the growing penetration of renewable energy into the power grid, as grid operators need to ensure grid stability in the presence of variable power generation. For this reason, IEA Wind Tasks 32 and 36 together organized a workshop on “Very Short-Term Forecasting of Wind Power” in 2018 to discuss different approaches for the implementation of minute-scale forecasts into the power industry. IEA Wind is an international platform for the research community and industry. Task 32 tries to identify and mitigate barriers to the use of lidars in wind energy applications, while IEA Wind Task 36 focuses on improving the value of wind energy forecasts to the wind energy industry. The workshop identified three applications that need minute-scale forecasts: (1) wind turbine and wind farm control, (2) power grid balancing, (3) energy trading and ancillary services. The forecasting horizons for these applications range from around 1 s for turbine control to 60 min for energy market and grid control applications. The methods that can be applied to generate minute-scale forecasts rely on upstream data from remote sensing devices such as scanning lidars or radars, or are based on point measurements from met masts, turbines or profiling remote sensing devices. Upstream data needs to be propagated with advection models and point measurements can either be used in statistical time series models or assimilated into physical models. All methods have advantages but also shortcomings. The workshop’s main conclusions were that there is a need for further investigations into the minute-scale forecasting methods for different use cases, and a cross-disciplinary exchange of different method experts should be established. Additionally, more efforts should be directed towards enhancing quality and reliability of the input measurement data. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation
Energies 2019, 12(2), 329; https://doi.org/10.3390/en12020329
Received: 8 December 2018 / Revised: 15 January 2019 / Accepted: 16 January 2019 / Published: 21 January 2019
Cited by 1 | PDF Full-text (2889 KB) | HTML Full-text | XML Full-text
Abstract
Accurate predictions of wind speed and wind energy are essential in renewable energy planning and management. This study was carried out to test the accuracy of two different neuro fuzzy techniques (neuro fuzzy system with grid partition (NF-GP) and neuro fuzzy system with [...] Read more.
Accurate predictions of wind speed and wind energy are essential in renewable energy planning and management. This study was carried out to test the accuracy of two different neuro fuzzy techniques (neuro fuzzy system with grid partition (NF-GP) and neuro fuzzy system with substractive clustering (NF-SC)), and two heuristic regression methods (least square support vector regression (LSSVR) and M5 regression tree (M5RT)) in the prediction of hourly wind speed and wind power using a cross-validation method. Fourfold cross-validation was employed by dividing the data into four equal subsets. LSSVR’s performance was superior to that of the M5RT, NF-SC, and NF-GP models for all datasets in wind speed prediction. The overall average root-mean-square errors (RMSE) of the M5RT, NF-GP, and NF-SC models decreased by 11.71%, 1.68%, and 2.94%, respectively, using the LSSVR model. The applicability of the four different models was also investigated in the prediction of one-hour-ahead wind power. The results showed that NF-GP’s performance was superior to that of LSSVR, NF-SC, and M5RT. The overall average RMSEs of LSSVR, NF-SC, and M5RT decreased by 5.52%, 1.30%, and 15.6%, respectively, using NF-GP. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
Probabilistic Wind Power Forecasting Approach via Instance-Based Transfer Learning Embedded Gradient Boosting Decision Trees
Energies 2019, 12(1), 159; https://doi.org/10.3390/en12010159
Received: 3 December 2018 / Revised: 25 December 2018 / Accepted: 1 January 2019 / Published: 3 January 2019
Cited by 2 | PDF Full-text (9684 KB) | HTML Full-text | XML Full-text
Abstract
With the high wind penetration in the power system, accurate and reliable probabilistic wind power forecasting has become even more significant for the reliability of the power system. In this paper, an instance-based transfer learning method combined with gradient boosting decision trees (GBDT) [...] Read more.
With the high wind penetration in the power system, accurate and reliable probabilistic wind power forecasting has become even more significant for the reliability of the power system. In this paper, an instance-based transfer learning method combined with gradient boosting decision trees (GBDT) is proposed to develop a wind power quantile regression model. Based on the spatial cross-correlation characteristic of wind power generations in different zones, the proposed model utilizes wind power generations in correlated zones as the source problems of instance-based transfer learning. By incorporating the training data of source problems into the training process, the proposed model successfully reduces the prediction error of wind power generation in the target zone. To prevent negative transfer, this paper proposes a method that properly assigns weights to data from different source problems in the training process, whereby the weights of related source problems are increased, while those of unrelated ones are reduced. Case studies are developed based on the dataset from the Global Energy Forecasting Competition 2014 (GEFCom2014). The results confirm that the proposed model successfully improves the prediction accuracy compared to GBDT-based benchmark models, especially when the target problem has a small training set while resourceful source problems are available. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production
Energies 2019, 12(1), 100; https://doi.org/10.3390/en12010100
Received: 29 November 2018 / Revised: 19 December 2018 / Accepted: 25 December 2018 / Published: 29 December 2018
Cited by 3 | PDF Full-text (1317 KB) | HTML Full-text | XML Full-text
Abstract
Solar energy forecasting is an active research problem and a key issue to increase the competitiveness of solar power plants in the energy market. However, using meteorological, production, or irradiance data from the past is not enough to produce accurate forecasts. This article [...] Read more.
Solar energy forecasting is an active research problem and a key issue to increase the competitiveness of solar power plants in the energy market. However, using meteorological, production, or irradiance data from the past is not enough to produce accurate forecasts. This article aims to integrate a prediction algorithm (Smart Persistence), irradiance, and past production data, using a state-of-the-art machine learning technique (Random Forests). Three years of data from six solar PV modules at Faro (Portugal) are analyzed. A set of features that combines past data, predictions, averages, and variances is proposed for training and validation. The experimental results show that using Smart Persistence as a Machine Learning input greatly improves the accuracy of short-term forecasts, achieving an NRMSE of 0.25 on the best panels at short horizons and 0.33 on a 6 h horizon. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
An LM-BP Neural Network Approach to Estimate Monthly-Mean Daily Global Solar Radiation Using MODIS Atmospheric Products
Energies 2018, 11(12), 3510; https://doi.org/10.3390/en11123510
Received: 10 November 2018 / Revised: 3 December 2018 / Accepted: 11 December 2018 / Published: 16 December 2018
Cited by 2 | PDF Full-text (4730 KB) | HTML Full-text | XML Full-text
Abstract
Solar energy is one of the most widely used renewable energy sources in the world and its development and utilization are being integrated into people’s lives. Therefore, accurate solar radiation data are of great significance for site-selection of photovoltaic (PV) power generation, design [...] Read more.
Solar energy is one of the most widely used renewable energy sources in the world and its development and utilization are being integrated into people’s lives. Therefore, accurate solar radiation data are of great significance for site-selection of photovoltaic (PV) power generation, design of solar furnaces and energy-efficient buildings. Practically, it is challenging to get accurate solar radiation data because of scarce and uneven distribution of ground-based observation sites throughout the country. Many artificial neural network (ANN) estimation models are therefore developed to estimate solar radiation, but the existing ANN models are mostly based on conventional meteorological data; clouds, aerosols, and water vapor are rarely considered because of a lack of instrumental observations at the conventional meteorological stations. Based on clouds, aerosols, and precipitable water-vapor data from Moderate Resolution Imaging Spectroradiometer (MODIS), along with conventional meteorological data, back-propagation (BP) neural network method was developed in this work with Levenberg-Marquardt (LM) algorithm (referred to as LM-BP) to simulate monthly-mean daily global solar radiation (M-GSR). Comparisons were carried out among three M-GSR estimates, including the one presented in this study, the multiple linear regression (MLR) model, and remotely-sensed radiation products by Cloud and the Earth’s radiation energy system (CERES). The validation results indicate that the accuracy of the ANN model is better than that of the MLR model and CERES radiation products, with a root mean squared error (RMSE) of 1.34 MJ·m−2 (ANN), 2.46 MJ·m−2 (MLR), 2.11 MJ·m−2 (CERES), respectively. Finally, according to the established ANN-based method, the M-GSR of 36 conventional meteorological stations for 12 months was estimated in 2012 in the study area. Solar radiation data based on the LM-BP method of this study can provide some reference for the utilization of solar and heat energy. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
Multiple-Regression Method for Fast Estimation of Solar Irradiation and Photovoltaic Energy Potentials over Europe and Africa
Energies 2018, 11(12), 3477; https://doi.org/10.3390/en11123477
Received: 15 November 2018 / Revised: 3 December 2018 / Accepted: 10 December 2018 / Published: 13 December 2018
Cited by 1 | PDF Full-text (3360 KB) | HTML Full-text | XML Full-text
Abstract
In recent years, various online tools and databases have been developed to assess the potential energy output of photovoltaic (PV) installations in different geographical areas. However, these tools generally provide a spatial resolution of a few kilometers and, for a systematic analysis at [...] Read more.
In recent years, various online tools and databases have been developed to assess the potential energy output of photovoltaic (PV) installations in different geographical areas. However, these tools generally provide a spatial resolution of a few kilometers and, for a systematic analysis at large scale, they require continuous querying of their online databases. In this article, we present a methodology for fast estimation of the yearly sum of global solar irradiation and PV energy yield over large-scale territories. The proposed method relies on a multiple-regression model including only well-known geodata, such as latitude, altitude above sea level and average ambient temperature. Therefore, it is particularly suitable for a fast, preliminary, offline estimation of solar PV output and to analyze possible investments in new installations. Application of the method to a random set of 80 geographical locations throughout Europe and Africa yields a mean absolute percent error of 4.4% for the estimate of solar irradiation (13.6% maximum percent error) and of 4.3% for the prediction of photovoltaic electricity production (14.8% maximum percent error for free-standing installations; 15.4% for building-integrated ones), which are consistent with the general accuracy provided by the reference tools for this application. Besides photovoltaic potentials, the proposed method could also find application in a wider range of installation assessments, such as in solar thermal energy or desalination plants. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model
Energies 2018, 11(12), 3415; https://doi.org/10.3390/en11123415
Received: 12 November 2018 / Revised: 29 November 2018 / Accepted: 3 December 2018 / Published: 6 December 2018
Cited by 7 | PDF Full-text (3825 KB) | HTML Full-text | XML Full-text
Abstract
Accurate global solar radiation prediction is highly essential for related research on renewable energy sources. The cost implication and measurement expertise of global solar radiation emphasize that intelligence prediction models need to be applied. On the basis of long-term measured daily solar radiation [...] Read more.
Accurate global solar radiation prediction is highly essential for related research on renewable energy sources. The cost implication and measurement expertise of global solar radiation emphasize that intelligence prediction models need to be applied. On the basis of long-term measured daily solar radiation data, this study uses a novel regularized online sequential extreme learning machine, integrated with variable forgetting factor (FOS-ELM), to predict global solar radiation at Bur Dedougou, in the Burkina Faso region. Bayesian Information Criterion (BIC) is applied to build the seven input combinations based on speed (Wspeed), maximum and minimum temperature (Tmax and Tmin), maximum and minimum humidity (Hmax and Hmin), evaporation (Eo) and vapor pressure deficiency (VPD). For the difference input parameters magnitudes, seven models were developed and evaluated for the optimal input combination. Various statistical indicators were computed for the prediction accuracy examination. The experimental results of the applied FOS-ELM model demonstrated a reliable prediction accuracy against the classical extreme learning machine (ELM) model for daily global solar radiation simulation. In fact, compared to classical ELM, the FOS-ELM model reported an enhancement in the root mean square error (RMSE) and mean absolute error (MAE) by (68.8–79.8%). In summary, the results clearly confirm the effectiveness of the FOS-ELM model, owing to the fixed internal tuning parameters. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessEditor’s ChoiceArticle
Estimating the Economic Impacts of Net Metering Schemes for Residential PV Systems with Profiling of Power Demand, Generation, and Market Prices
Energies 2018, 11(11), 3222; https://doi.org/10.3390/en11113222
Received: 29 October 2018 / Revised: 12 November 2018 / Accepted: 16 November 2018 / Published: 20 November 2018
Cited by 4 | PDF Full-text (3092 KB) | HTML Full-text | XML Full-text
Abstract
This article analyses the influence of supporting scheme variants on the profitability of a projected investment of residential photovoltaic systems. The focus of the paper lies in evaluating the feasibility for the power system of solar power generation technologies to achieve a balance [...] Read more.
This article analyses the influence of supporting scheme variants on the profitability of a projected investment of residential photovoltaic systems. The focus of the paper lies in evaluating the feasibility for the power system of solar power generation technologies to achieve a balance between energy generation and support costs in a more efficient way. The case study is based on a year-long time series of examples with an hourly resolution of electricity prices from the Nord Pool power market, in addition to the power demand and solar generation of Latvian prosumers. Electric energy generation and the consumption of big data from more than 100 clients were collected. Based on these data, we predict the processes for the next 25 years, and we estimate economic indicators using a detailed description of the net metering billing system and the Monte-Carlo method. A recommendation to change the current net system to a superior one, taking into account the market cost of energy, concludes the paper. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
Prediction of Wind Turbine-Grid Interaction Based on a Principal Component Analysis-Long Short Term Memory Model
Energies 2018, 11(11), 3221; https://doi.org/10.3390/en11113221
Received: 26 September 2018 / Revised: 11 November 2018 / Accepted: 16 November 2018 / Published: 20 November 2018
Cited by 3 | PDF Full-text (5393 KB) | HTML Full-text | XML Full-text
Abstract
The interaction between the gird and wind farms has significant impact on the power grid, therefore prediction of the interaction between gird and wind farms is of great significance. In this paper, a wind turbine-gird interaction prediction model based on long short term [...] Read more.
The interaction between the gird and wind farms has significant impact on the power grid, therefore prediction of the interaction between gird and wind farms is of great significance. In this paper, a wind turbine-gird interaction prediction model based on long short term memory (LSTM) network under the TensorFlow framework is presented. First, the multivariate time series was screened by principal component analysis (PCA) to reduce the data dimensionality. Secondly, the LSTM network is used to model the nonlinear relationship between the selected sequence of wind turbine network interactions and the actual output sequence of the wind farms, it is proved that it has higher accuracy and applicability by comparison with single LSTM model, Autoregressive Integrated Moving Average (ARIMA) model and Back Propagation Neural Network (BPNN) model, the Mean Absolute Percentage Error (MAPE) is 0.617%, 0.703%, 1.397% and 3.127%, respectively. Finally, the Prony algorithm was used to analyze the predicted data of the wind turbine-grid interactions. Based on the actual data, it is found that the oscillation frequencies of the predicted data from PCA-LSTM model are basically the same as the oscillation frequencies of the actual data, thus the feasibility of the model proposed for analyzing interaction between grid and wind turbines is verified. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessEditor’s ChoiceArticle
One-Hour Prediction of the Global Solar Irradiance from All-Sky Images Using Artificial Neural Networks
Energies 2018, 11(11), 2906; https://doi.org/10.3390/en11112906
Received: 28 September 2018 / Revised: 18 October 2018 / Accepted: 22 October 2018 / Published: 25 October 2018
Cited by 6 | PDF Full-text (3478 KB) | HTML Full-text | XML Full-text
Abstract
We present a method to predict the global horizontal irradiance (GHI) one hour ahead in one-minute resolution using Artificial Neural Networks (ANNs). A feed-forward neural network with Levenberg–Marquardt Backpropagation (LM-BP) was used and was trained with four years of data from all-sky images [...] Read more.
We present a method to predict the global horizontal irradiance (GHI) one hour ahead in one-minute resolution using Artificial Neural Networks (ANNs). A feed-forward neural network with Levenberg–Marquardt Backpropagation (LM-BP) was used and was trained with four years of data from all-sky images and measured global irradiance as input. The pictures were recorded by a hemispheric sky imager at the Institute of Meteorology and Climatology (IMuK) of the Leibniz Universität Hannover, Hannover, Germany (52.23° N, 09.42° E, and 50 m above sea level). The time series of the global horizontal irradiance was measured using a thermopile pyranometer at the same site. The new method was validated with a test dataset from the same source. The irradiance is predicted for the first 10–30 min very well; after this time, the length of which is dependent on the weather conditions, the agreement between predicted and observed irradiance is reasonable. Considering the limited range that the camera and the ANN can “see”, this is not surprising. When comparing the results to the persistence model, we observed that the forecast accuracy of the new model reduced both the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) of the one-hour prediction by approximately 40% compared to the reference persistence model under various weather conditions, which demonstrates the high capability of the algorithm, especially within the first minutes. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
Extreme Learning Machines for Solar Photovoltaic Power Predictions
Energies 2018, 11(10), 2725; https://doi.org/10.3390/en11102725
Received: 3 September 2018 / Revised: 7 October 2018 / Accepted: 9 October 2018 / Published: 11 October 2018
Cited by 3 | PDF Full-text (3278 KB) | HTML Full-text | XML Full-text
Abstract
The unpredictability of intermittent renewable energy (RE) sources (solar and wind) constitutes reliability challenges for utilities whose goal is to match electricity supply to consumer demands across centralized grid networks. Thus, balancing the variable and increasing power inputs from plants with intermittent energy [...] Read more.
The unpredictability of intermittent renewable energy (RE) sources (solar and wind) constitutes reliability challenges for utilities whose goal is to match electricity supply to consumer demands across centralized grid networks. Thus, balancing the variable and increasing power inputs from plants with intermittent energy sources becomes a fundamental issue for transmission system operators. As a result, forecasting techniques have obtained paramount importance. This work aims at exploiting the simplicity, fast computational and good generalization capability of Extreme Learning Machines (ELMs) in providing accurate 24 h-ahead solar photovoltaic (PV) power production predictions. The ELM architecture is firstly optimized, e.g., in terms of number of hidden neurons, and number of historical solar radiations and ambient temperatures (embedding dimension) required for training the ELM model, then it is used online to predict the solar PV power productions. The investigated ELM model is applied to a real case study of 264 kWp solar PV system installed on the roof of the Faculty of Engineering at the Applied Science Private University (ASU), Amman, Jordan. Results showed the capability of the ELM model in providing predictions that are slightly more accurate with negligible computational efforts compared to a Back Propagation Artificial Neural Network (BP-ANN) model, which is currently adopted by the PV system owners for the prediction task. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
Outlier Events of Solar Forecasts for Regional Power Grid in Japan Using JMA Mesoscale Model
Energies 2018, 11(10), 2714; https://doi.org/10.3390/en11102714
Received: 7 September 2018 / Revised: 29 September 2018 / Accepted: 9 October 2018 / Published: 11 October 2018
PDF Full-text (11899 KB) | HTML Full-text | XML Full-text
Abstract
To realize the safety control of electric power systems under high penetration of photovoltaic power systems, accurate global horizontal irradiance (GHI) forecasts using numerical weather prediction models (NWP) are becoming increasingly important. The objective of this study is to understand meteorological characteristics pertaining [...] Read more.
To realize the safety control of electric power systems under high penetration of photovoltaic power systems, accurate global horizontal irradiance (GHI) forecasts using numerical weather prediction models (NWP) are becoming increasingly important. The objective of this study is to understand meteorological characteristics pertaining to large errors (i.e., outlier events) of GHI day-ahead forecasts obtained from the Japan Meteorological Agency, for nine electric power areas during four years from 2014 to 2017. Under outlier events in GHI day-ahead forecasts, several sea-level pressure (SLP) patterns were found in 80 events during the four years; (a) a western edge of anticyclone over the Pacific Ocean (frequency per 80 outlier events; 48.8%), (b) stationary fronts (20.0%), (c) a synoptic-scale cyclone (18.8%), and (d) typhoons (tropical cyclones) (8.8%) around the Japanese islands. In this study, the four case studies of the worst outlier events were performed. A remarkable SLP pattern was the case of the western edge of anticyclone over the Pacific Ocean around Japan. The comparison between regionally integrated GHI day-ahead forecast errors and cloudiness forecasts suggests that the issue of accuracy of cloud forecasts in high- and mid-levels troposphere in NWPs will remain in the future. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
PV System Design and Flight Efficiency Considerations for Fixed-Wing Radio-Controlled Aircraft—A Case Study
Energies 2018, 11(10), 2648; https://doi.org/10.3390/en11102648
Received: 28 August 2018 / Revised: 29 September 2018 / Accepted: 1 October 2018 / Published: 3 October 2018
Cited by 1 | PDF Full-text (1221 KB) | HTML Full-text | XML Full-text
Abstract
The list of photovoltaic (PV) applications grows longer every day with high consideration for system efficiency. For instance, in spite of many recent PV aircraft designs, aircraft propulsion was mainly reserved for nonelectric motors. Lately, the Solar Impulse flight across the world shows [...] Read more.
The list of photovoltaic (PV) applications grows longer every day with high consideration for system efficiency. For instance, in spite of many recent PV aircraft designs, aircraft propulsion was mainly reserved for nonelectric motors. Lately, the Solar Impulse flight across the world shows the possibilities of larger PV powered electric aircraft. In order to obtain this goal efficiency of flight, PV conversion, power converters and electric drives have to be maximized. These demands led to a 63.4 m wingspan. The purpose of this paper is to present that PV power could be used for improving the performance of fixed-wing radio-controlled aircrafts with smaller wingspans (1 m). In order to improve the performance of battery powered electric unmanned aerial vehicles (UAV), a model without PV cells (commercial Li-ion battery powered UAV) was compared with UAV powered both from battery and PV modules. This work shows details about Boost DC/DC converter and PV system design for small size fixed-wing electric UAVs, investigating the possibility of the application of PV powered drones, as well. Theoretical findings involving efficiency improvements have been confirmed by measurements combining the improvements in electrical engineering, microcontroller application and aerodynamics. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain
Energies 2018, 11(10), 2623; https://doi.org/10.3390/en11102623
Received: 2 September 2018 / Revised: 26 September 2018 / Accepted: 28 September 2018 / Published: 1 October 2018
Cited by 1 | PDF Full-text (7573 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Accurate forecasting of the produced and consumed electricity from wind farms is an essential aspect for wind power plant operators. In this context, our research addresses small-scale wind farms situated on hilly terrain, with the main purpose of overcoming the low accuracy limitations [...] Read more.
Accurate forecasting of the produced and consumed electricity from wind farms is an essential aspect for wind power plant operators. In this context, our research addresses small-scale wind farms situated on hilly terrain, with the main purpose of overcoming the low accuracy limitations arising from the wind deflection, caused by the quite complex hilly terrain. A specific aspect of our devised forecasting method consists of incorporating advantages of recurrent long short-term memory (LSTM) neural networks, benefiting from their long-term dependencies, learning capabilities, and the advantages of feed-forward function fitting neural networks (FITNETs) that have the ability to map between a dataset of numeric inputs and a set of numeric targets. Another specific element of our approach consists of improving forecasting accuracy by means of refining the accuracy of the weather data input parameters within the same weather forecast resolution area. The developed method has power plant operators as main beneficiaries, but it can also be successfully applied in order to assess the energy potential of hilly areas with deflected wind, being useful for potential investors who want to build this type of wind farms. The method can be compiled and incorporated in the development of a wide range of customized applications targeting electricity forecasting for small wind farms situated on hilly terrain with deflected wind. The experimental results, the implementation of the developed method in a real production environment, its validation, and the comparison between our proposed method and other ones from the literature, confirm that the developed forecasting method represents an accurate, useful, and viable tool that addresses a gap in the current state of knowledge regarding the necessity for an accurate forecasting method that is able to predict with a high degree of accuracy both the produced and consumed electricity for small wind power plants situated on quite complex hilly terrain with deflected wind. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessFeature PaperArticle
A Statistical Modeling Methodology for Long-Term Wind Generation and Power Ramp Simulations in New Generation Locations
Energies 2018, 11(9), 2442; https://doi.org/10.3390/en11092442
Received: 23 August 2018 / Revised: 10 September 2018 / Accepted: 11 September 2018 / Published: 14 September 2018
Cited by 3 | PDF Full-text (2829 KB) | HTML Full-text | XML Full-text
Abstract
In future power systems, a large share of the energy will be generated with wind power plants (WPPs) and other renewable energy sources. With the increasing wind power penetration, the variability of the net generation in the system increases. Consequently, it is imperative [...] Read more.
In future power systems, a large share of the energy will be generated with wind power plants (WPPs) and other renewable energy sources. With the increasing wind power penetration, the variability of the net generation in the system increases. Consequently, it is imperative to be able to assess and model the behavior of the WPP generation in detail. This paper presents an improved methodology for the detailed statistical modeling of wind power generation from multiple new WPPs without measurement data. A vector autoregressive based methodology, which can be applied to long-term Monte Carlo simulations of existing and new WPPs, is proposed. The proposed model improves the performance of the existing methodology and can more accurately analyze the temporal correlation structure of aggregated wind generation at the system level. This enables the model to assess the impact of new WPPs on the wind power ramp rates in a power system. To evaluate the performance of the proposed methodology, it is verified against hourly wind speed measurements from six locations in Finland and the aggregated wind power generation from Finland in 2015. Furthermore, a case study analyzing the impact of the geographical distribution of WPPs on wind power ramps is included. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
Primary Frequency Controller with Prediction-Based Droop Coefficient for Wind-Storage Systems under Spot Market Rules
Energies 2018, 11(9), 2340; https://doi.org/10.3390/en11092340
Received: 26 July 2018 / Revised: 15 August 2018 / Accepted: 22 August 2018 / Published: 5 September 2018
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Abstract
Increasing penetration levels of asynchronous wind turbine generators (WTG) reduce the ability of the power system to maintain adequate frequency responses. WTG with the installation of battery energy storage systems (BESS) as wind-storage systems (WSS), not only reduce the intermittency but also provide [...] Read more.
Increasing penetration levels of asynchronous wind turbine generators (WTG) reduce the ability of the power system to maintain adequate frequency responses. WTG with the installation of battery energy storage systems (BESS) as wind-storage systems (WSS), not only reduce the intermittency but also provide a frequency response. Meanwhile, many studies indicate that using the dynamic droop coefficient of WSS in primary frequency control (PFC) based on the prediction values, is an effective way to enable the performance of WSS similar to conventional synchronous generators. This paper proposes a PFC for WSS with a prediction-based droop coefficient (PDC) according to the re-bid process under real-time spot market rules. Specifically, WSS update the values of the reference power and droop coefficient discretely at every bidding interval using near-term wind power and frequency prediction, which enables WSS to be more dispatchable in the view of transmission system operators (TSOs). Also, the accurate prediction method in the proposed PDC-PFC achieves the optimal arrangement of power from WTG and BESS in PFC. Finally, promising simulation results for a hybrid power system show the efficacy of the proposed PDC-PFC for WSS under different operating conditions. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting
Energies 2018, 11(8), 2163; https://doi.org/10.3390/en11082163
Received: 19 July 2018 / Revised: 8 August 2018 / Accepted: 15 August 2018 / Published: 18 August 2018
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Abstract
Photovoltaic power has great volatility and intermittency due to environmental factors. Forecasting photovoltaic power is of great significance to ensure the safe and economical operation of distribution network. This paper proposes a novel approach to forecast short-term photovoltaic power based on a gated [...] Read more.
Photovoltaic power has great volatility and intermittency due to environmental factors. Forecasting photovoltaic power is of great significance to ensure the safe and economical operation of distribution network. This paper proposes a novel approach to forecast short-term photovoltaic power based on a gated recurrent unit (GRU) network. Firstly, the Pearson coefficient is used to extract the main features that affect photovoltaic power output at the next moment, and qualitatively analyze the relationship between the historical photovoltaic power and the future photovoltaic power output. Secondly, the K-means method is utilized to divide training sets into several groups based on the similarities of each feature, and then GRU network training is applied to each group. The output of each GRU network is averaged to obtain the photovoltaic power output at the next moment. The case study shows that the proposed approach can effectively consider the influence of features and historical photovoltaic power on the future photovoltaic power output, and has higher accuracy than the traditional methods. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
An Extreme Scenario Method for Robust Transmission Expansion Planning with Wind Power Uncertainty
Energies 2018, 11(8), 2116; https://doi.org/10.3390/en11082116
Received: 16 July 2018 / Revised: 8 August 2018 / Accepted: 11 August 2018 / Published: 14 August 2018
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Abstract
The rapid incorporation of wind power resources in electrical power networks has significantly increased the volatility of transmission systems due to the inherent uncertainty associated with wind power. This paper addresses this issue by proposing a transmission network expansion planning (TEP) model that [...] Read more.
The rapid incorporation of wind power resources in electrical power networks has significantly increased the volatility of transmission systems due to the inherent uncertainty associated with wind power. This paper addresses this issue by proposing a transmission network expansion planning (TEP) model that integrates wind power resources, and that seeks to minimize the sum of investment costs and operation costs while accounting for the costs associated with the pollution emissions of generator infrastructure. Auxiliary relaxation variables are introduced to transform the established model into a mixed integer linear programming problem. Furthermore, the novel concept of extreme wind power scenarios is defined, theoretically justified, and then employed to establish a two-stage robust TEP method. The decision-making variables of prospective transmission lines are determined in the first stage, so as to ensure that the operating variables in the second stage can adapt to wind power fluctuations. A Benders’ decomposition algorithm is developed to solve the proposed two-stage model. Finally, extensive numerical studies are conducted with Garver’s 6-bus system, a modified IEEE RTS79 system and IEEE 118-bus system, and the computational results demonstrate the effectiveness and practicability of the proposed method. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach
Energies 2018, 11(8), 1958; https://doi.org/10.3390/en11081958
Received: 23 June 2018 / Revised: 19 July 2018 / Accepted: 25 July 2018 / Published: 27 July 2018
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Abstract
Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and stability of power system. In [...] Read more.
Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and stability of power system. In this study, we propose an ensemble model for probabilistic wind speed forecasting. It consists of wavelet threshold denoising (WTD), recurrent neural network (RNN) and adaptive neuro fuzzy inference system (ANFIS). Firstly, WTD smooths the wind speed series in order to better capture its variation trend. Secondly, RNNs with different architectures are trained on the denoising datasets, operating as sub-models for point wind speed forecasting. Thirdly, ANFIS is innovatively established as the top layer of the entire ensemble model to compute the final point prediction result, in order to take full advantages of a limited number of deep-learning-based sub-models. Lastly, variances are obtained from sub-models and then prediction intervals of probabilistic forecasting can be calculated, where the variances inventively consist of modeling and forecasting uncertainties. The proposed ensemble model is established and verified on less than one-hour-ahead ultra-short-term wind speed forecasting. We compare it with other soft computing models. The results indicate the feasibility and superiority of the proposed model in both point and probabilistic wind speed forecasting. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting
Energies 2018, 11(7), 1763; https://doi.org/10.3390/en11071763
Received: 11 May 2018 / Revised: 25 June 2018 / Accepted: 28 June 2018 / Published: 4 July 2018
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Abstract
The inclusion of photo-voltaic generation in the distribution grid poses technical difficulties related to the variability of the solar source and determines the need for Probabilistic Forecasting procedures (PF). This work describes a new approach for PF based on quantile regression using the [...] Read more.
The inclusion of photo-voltaic generation in the distribution grid poses technical difficulties related to the variability of the solar source and determines the need for Probabilistic Forecasting procedures (PF). This work describes a new approach for PF based on quantile regression using the Gradient-Boosted Regression Trees (GBRT) method fed by numerical weather forecasts of the European Centre for Medium Range Weather Forecast (ECMWF) Integrated Forecasting System (IFS) and Ensemble Prediction System (EPS). The proposed methodology is compared with the forecasts obtained with Quantile Regression using only IFS forecasts (QR), with the uncalibrated EPS forecasts and with the EPS forecasts calibrated with a Variance Deficit (VD) procedure. The proposed methodology produces forecasts with a temporal resolution equal to or better than the meteorological forecast (1 h for the IFS and 3 h for EPS) and, in the case examined, is able to provide higher performances than those obtained with the other methods over a forecast horizon of up to 72 h. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
A Novel Decomposition-Optimization Model for Short-Term Wind Speed Forecasting
Energies 2018, 11(7), 1752; https://doi.org/10.3390/en11071752
Received: 1 June 2018 / Revised: 26 June 2018 / Accepted: 27 June 2018 / Published: 4 July 2018
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Abstract
Due to inherent randomness and fluctuation of wind speeds, it is very challenging to develop an effective and practical model to achieve accurate wind speed forecasting, especially over large forecasting horizons. This paper presents a new decomposition-optimization model created by integrating Variational Mode [...] Read more.
Due to inherent randomness and fluctuation of wind speeds, it is very challenging to develop an effective and practical model to achieve accurate wind speed forecasting, especially over large forecasting horizons. This paper presents a new decomposition-optimization model created by integrating Variational Mode Decomposition (VMD), Backtracking Search Algorithm (BSA), and Regularized Extreme Learning Machine (RELM) to enhance forecasting accuracy. The observed wind speed time series is firstly decomposed by VMD into several relative stable subsequences. Then, an emerging optimization algorithm, BSA, is utilized to search the optimal parameters of the RELM. Subsequently, the well-trained RELM is constructed to do multi-step (1-, 2-, 4-, and 6-step) wind speed forecasting. Experiments have been executed with the proposed method as well as several benchmark models using several datasets from a widely-studied wind farm, Sotavento Galicia in Spain. Additionally, the effects of decomposition and optimization methods on the final forecasting results are analyzed quantitatively, whereby the importance of decomposition technique is emphasized. Results reveal that the proposed VMD-BSA-RELM model achieves significantly better performance than its rivals both on single- and multi-step forecasting with at least 50% average improvement, which indicates it is a powerful tool for short-term wind speed forecasting. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
Research and Application of a Hybrid Wind Energy Forecasting System Based on Data Processing and an Optimized Extreme Learning Machine
Energies 2018, 11(7), 1712; https://doi.org/10.3390/en11071712
Received: 15 April 2018 / Revised: 29 May 2018 / Accepted: 20 June 2018 / Published: 1 July 2018
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Abstract
Accurate wind speed forecasting plays a significant role for grid operators and the use of wind energy, which helps meet increasing energy needs and improve the energy structure. However, choosing an accurate forecasting system is a challenging task. Many studies have been carried [...] Read more.
Accurate wind speed forecasting plays a significant role for grid operators and the use of wind energy, which helps meet increasing energy needs and improve the energy structure. However, choosing an accurate forecasting system is a challenging task. Many studies have been carried out in recent years, but unfortunately, these studies ignore the importance of data preprocessing and the influence of numerous missing values, leading to poor forecasting performance. In this paper, a hybrid forecasting system based on data preprocessing and an Extreme Learning Machine optimized by the cuckoo algorithm is proposed, which can overcome the limitations of the single ELM model. In the system, the standard genetic algorithm is added to reduce the dimensions of the input and utilize the time series model for error correction by focusing on the optimized extreme learning machine model. And according to screened results, the 5% fractile and 95% fractile are applied to compose the upper and lower bounds of the confidence interval, respectively. The assessment results indicate that the hybrid system successfully overcomes some limitations of the single Extreme Learning Machine model and traditional BP and Mycielski models and can be an effective tool compared to traditional forecasting models. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
Forecasting the Long-Term Wind Data via Measure-Correlate-Predict (MCP) Methods
Energies 2018, 11(6), 1541; https://doi.org/10.3390/en11061541
Received: 4 May 2018 / Revised: 31 May 2018 / Accepted: 11 June 2018 / Published: 13 June 2018
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Abstract
The current study aims to forecast and analyze wind data such as wind speed at a test site called “Urumsill” on Deokjeok Island, South Korea. The measured wind data available at the aforementioned test site are only for two years (2015 and 2016), [...] Read more.
The current study aims to forecast and analyze wind data such as wind speed at a test site called “Urumsill” on Deokjeok Island, South Korea. The measured wind data available at the aforementioned test site are only for two years (2015 and 2016), making it impossible to analyze the long-term wind characteristics. In order to overcome this problem, two measure-correlate-predict (MCP) techniques were adopted using long-term wind data (2000–2016), measured by a meteorological mast (met-mast) installed at a distance of 3 km from the test site. The wind data measured at the test site in 2016 were selected as training data to build the MCP models, whereas wind data of 2015 were used to test the accuracy of MCP models (test data). The wind data at both sites were measured at a height of 10 m and showed a good agreement for the year 2016 (training period). Using the comparison results of the year 2016, wind speed predictions were made for the rest of the years (2000–2016) at the test site. The forecasted values of wind speed had maximum relative error in the range of ±0.8 m/s for the test year of 2105. The predicted wind data values were further analyzed by estimating the mean wind speed, the Weibull shape, and the scale parameters, on a seasonal and an annual basis, in order to understand the wind behavior in the region. The accuracy and presence of possible errors in the forecasted wind data are discussed and presented. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessArticle
Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed
Energies 2018, 11(6), 1487; https://doi.org/10.3390/en11061487
Received: 29 April 2018 / Revised: 1 June 2018 / Accepted: 4 June 2018 / Published: 7 June 2018
Cited by 6 | PDF Full-text (1008 KB) | HTML Full-text | XML Full-text
Abstract
An accurate forecast of the exploitable energy from Renewable Energy Sources is extremely important for the stability issues of the electric grid and the reliability of the bidding markets. This paper presents a comparison among different forecasting methods of the photovoltaic output power [...] Read more.
An accurate forecast of the exploitable energy from Renewable Energy Sources is extremely important for the stability issues of the electric grid and the reliability of the bidding markets. This paper presents a comparison among different forecasting methods of the photovoltaic output power introducing a new method that mixes some peculiarities of the others: the Physical Hybrid Artificial Neural Network and the five parameters model estimated by the Social Network Optimization. In particular, the day-ahead forecasts evaluated against real data measured for two years in an existing photovoltaic plant located in Milan, Italy, are compared by means both new and the most common error indicators. Results reported in this work show the best forecasting capability of the new “mixed method” which scored the best forecast skill and Enveloped Mean Absolute Error on a yearly basis (47% and 24.67%, respectively). Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessBrief Report
Wind Turbine Wake Characterization for Improvement of the Ainslie Eddy Viscosity Wake Model
Energies 2018, 11(10), 2823; https://doi.org/10.3390/en11102823
Received: 3 July 2018 / Revised: 13 September 2018 / Accepted: 11 October 2018 / Published: 19 October 2018
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Abstract
This paper presents a modified version of the Ainslie eddy viscosity wake model and its accuracy by comparing it with selected exiting wake models and wind tunnel test results. The wind tunnel test was performed using a 1.9 m rotor diameter wind turbine [...] Read more.
This paper presents a modified version of the Ainslie eddy viscosity wake model and its accuracy by comparing it with selected exiting wake models and wind tunnel test results. The wind tunnel test was performed using a 1.9 m rotor diameter wind turbine model operating at a tip speed ratio similar to that of modern megawatt wind turbines. The control algorithms for blade pitch and generator torque used for below and above rated wind speed regions similar to those for multi-MW wind turbines were applied to the scaled wind turbine model. In order to characterize the influence of the wind turbine operating conditions on the wake, the wind turbine model was tested in both below and above rated wind speed regions at which the thrust coefficients of the rotor varied. The correction of the Ainslie eddy viscosity wake model was made by modifying the empirical equation of the original model using the wind tunnel test results with the Nelder-Mead simplex method for function minimization. The wake prediction accuracy of the modified wake model in terms of wind speed deficit was found to be improved by up to 6% compared to that of the original model. Comparisons with other existing wake models are also made in detail. Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Open AccessErratum
Erratum: Rogada, J.R.; et al. Comparative Modeling of a Parabolic Trough Collectors Solar Power Plant with MARS Models. Energies 2018, 11, 37
Energies 2018, 11(7), 1856; https://doi.org/10.3390/en11071856
Received: 2 July 2018 / Accepted: 3 July 2018 / Published: 16 July 2018
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Abstract
Due to the difference in date between the sending and the publication of the article [...] Full article
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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