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Special Issue "Wind Energy, Load and Price Forecasting towards Sustainability 2019"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 30 June 2019

Special Issue Editor

Guest Editor
Prof. Dr. João P. S. Catalão

University of Porto, Porto, Portugal
Website | E-Mail
Interests: power system operations and planning; hydrothermal scheduling and wind/price forecasting; power system economics and electricity markets; risk analysis, uncertainty, and stochastic programming; renewable energies and demand-side management

Special Issue Information

Dear Colleagues,

“Wind Energy, Load and Price Forecasting towards Sustainability 2019” is a continuation of the previous and successful Special Issue, “Wind Energy, Load and Price Forecasting towards Sustainability”.

We are inviting submissions to this Sustainability Special Issue on “Wind Energy, Load and Price Forecasting towards Sustainability 2019”.

This is the second Special Issue and focuses on wind energy forecasting, load forecasting and price forecasting. Wind energy is one of the fastest growing sources of electricity worldwide. However, the availability of wind energy is not known in advance, so its large-scale integration in the power supply poses serious challenges. In order to conveniently address those challenges, wind energy forecasting plays a major role. Load forecasting has also recently acquired renewed interest with the advent of the smart grid, especially due to the growing focus on demand side-management activities. Likewise, price forecasting is of major importance in the energy industry, both for companies and customers. Hence, in this Special Issue we invite submissions exploring cutting-edge research and recent advances in the fields of wind energy forecasting, load forecasting and price forecasting, towards increased overall sustainability of the energy system from supply to demand sides.

Prof. Dr. João P. S. Catalão
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. Sustainability 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 1700 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
  • Wind Energy
  • Load
  • Electricity Prices
  • Sustainability

Published Papers (6 papers)

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Research

Open AccessArticle Short-Term Wind Speed Forecasting Based on Hybrid Variational Mode Decomposition and Least Squares Support Vector Machine Optimized by Bat Algorithm Model
Sustainability 2019, 11(3), 652; https://doi.org/10.3390/su11030652
Received: 24 December 2018 / Revised: 21 January 2019 / Accepted: 23 January 2019 / Published: 26 January 2019
Cited by 1 | PDF Full-text (3572 KB) | HTML Full-text | XML Full-text
Abstract
With the integration of wind energy into electricity grids, wind speed forecasting plays an important role in energy generation planning, power grid integration and turbine maintenance scheduling. This study proposes a hybrid wind speed forecasting model to enhance prediction performance. Variational mode decomposition [...] Read more.
With the integration of wind energy into electricity grids, wind speed forecasting plays an important role in energy generation planning, power grid integration and turbine maintenance scheduling. This study proposes a hybrid wind speed forecasting model to enhance prediction performance. Variational mode decomposition (VMD) was applied to decompose the original wind speed series into different sub-series with various frequencies. A least squares support vector machine (LSSVM) model with the pertinent parameters being optimized by a bat algorithm (BA) was established to forecast those sub-series extracted from VMD. The ultimate forecast of wind speed can be obtained by accumulating the prediction values of each sub-series. The results show that: (a) VMD-BA-LSSVM displays better capacity for the prediction of ultra short-term (15 min) and short-term (1 h) wind speed forecasting; (b) the proposed forecasting model was compared with wavelet decomposition (WD) and ensemble empirical mode decomposition (EEMD), and the results indicate that VMD has stronger decomposition ability than WD and EEMD, thus, significant improvements in forecasting accuracy were obtained with the proposed forecasting models compared with other forecasting methods. Full article
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability 2019)
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Open AccessArticle A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network
Sustainability 2019, 11(2), 526; https://doi.org/10.3390/su11020526
Received: 14 December 2018 / Revised: 8 January 2019 / Accepted: 17 January 2019 / Published: 19 January 2019
Cited by 1 | PDF Full-text (4643 KB) | HTML Full-text | XML Full-text
Abstract
Given the rapid development and wide application of wind energy, reliable and stable wind speed forecasting is of great significance in keeping the stability and security of wind power systems. However, accurate wind speed forecasting remains a great challenge due to its inherent [...] Read more.
Given the rapid development and wide application of wind energy, reliable and stable wind speed forecasting is of great significance in keeping the stability and security of wind power systems. However, accurate wind speed forecasting remains a great challenge due to its inherent randomness and intermittency. Most previous researches merely devote to improving the forecasting accuracy or stability while ignoring the equal significance of improving the two aspects in application. Therefore, this paper proposes a novel hybrid forecasting system containing the modules of a modified data preprocessing, multi-objective optimization, forecasting, and evaluation to achieve the wind speed forecasting with high precision and stability. The modified data preprocessing method can obtain a smoother input by decomposing and reconstructing the original wind speed series in the module of data preprocessing. Further, echo state network optimized by a multi-objective optimization algorithm is developed as a predictor in the forecasting module. Finally, eight datasets with different features are used to validate the performance of the proposed system using the evaluation module. The mean absolute percentage errors of the proposed system are 3.1490%, 3.0051%, 3.0618%, and 2.6180% in spring, summer, autumn, and winter, respectively. Moreover, the interval prediction is complemented to quantitatively characterize the uncertainty as developing intervals, and the mean average width is below 0.2 at the 95% confidence level. The results demonstrate the proposed forecasting system outperforms other comparative models considered from the forecasting accuracy and stability, which has great potential in the application of wind power systems. Full article
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability 2019)
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Open AccessArticle Hybrid Forecasting Model for Short-Term Electricity Market Prices with Renewable Integration
Sustainability 2019, 11(1), 57; https://doi.org/10.3390/su11010057
Received: 21 November 2018 / Revised: 17 December 2018 / Accepted: 18 December 2018 / Published: 22 December 2018
PDF Full-text (3344 KB) | HTML Full-text | XML Full-text
Abstract
In recent years, there have been notable commitments and obligations by the electricity sector for more sustainable generation and delivery processes to reduce the environmental footprint. However, there is still a long way to go to achieve necessary sustainability goals while ensuring standards [...] Read more.
In recent years, there have been notable commitments and obligations by the electricity sector for more sustainable generation and delivery processes to reduce the environmental footprint. However, there is still a long way to go to achieve necessary sustainability goals while ensuring standards of robustness and the quality of power grids. One of the main challenges hindering this progress are uncertainties and stochasticity associated with the electricity sector and especially renewable generation. In this paradigm shift, forecasting tools are indispensable, and their utilization can significantly improve system operation and minimize costs associated with all related activities. Thus, forecasting tools have an essential key role in all decision-making stages. In this work, a hybrid probabilistic forecasting model (HPFM) was developed for short-term electricity market prices (EMP) combining wavelet transforms (WT), hybrid particle swarm optimization (DEEPSO), adaptive neuro-fuzzy inference system (ANFIS), and Monte Carlo simulation (MCS). The proposed hybrid probabilistic forecasting model (HPFM) was tested and validated with real data from the Spanish and Pennsylvania-New Jersey-Maryland (PJM) markets. The proposed model exhibited favorable results and performance in comparison with previously published work considering electricity market prices (EMP) data, which is notable. Full article
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability 2019)
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Open AccessArticle Artificial Combined Model Based on Hybrid Nonlinear Neural Network Models and Statistics Linear Models—Research and Application for Wind Speed Forecasting
Sustainability 2018, 10(12), 4601; https://doi.org/10.3390/su10124601
Received: 14 November 2018 / Revised: 29 November 2018 / Accepted: 1 December 2018 / Published: 5 December 2018
Cited by 2 | PDF Full-text (4055 KB) | HTML Full-text | XML Full-text
Abstract
The use of wind power is rapidly increasing as an important part of power systems, but because of the intermittent and random nature of wind speed, system operators and researchers urgently need to find more reliable methods to forecast wind speed. Through research, [...] Read more.
The use of wind power is rapidly increasing as an important part of power systems, but because of the intermittent and random nature of wind speed, system operators and researchers urgently need to find more reliable methods to forecast wind speed. Through research, it is found that the time series of wind speed demonstrate not only linear features but also nonlinear features. Hence, a combined forecasting model based on an improved cuckoo search algorithm optimizes weight, and several single models—linear model, hybrid nonlinear neural network, and fuzzy forecasting model—are developed in this paper to provide more trend change for time series of wind speed forecasting besides improving the forecasting accuracy. Furthermore, the effectiveness of the proposed model is proved by wind speed data from four wind farm sites and the results are more reliable and accurate than comparison models. Full article
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability 2019)
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Open AccessArticle An Investigation of Wind Direction and Speed in a Featured Wind Farm Using Joint Probability Distribution Methods
Sustainability 2018, 10(12), 4338; https://doi.org/10.3390/su10124338
Received: 20 August 2018 / Revised: 14 October 2018 / Accepted: 15 October 2018 / Published: 22 November 2018
PDF Full-text (5178 KB) | HTML Full-text | XML Full-text
Abstract
Wind direction and speed are both crucial factors for wind farm layout; however, the relationship between the two factors has not been well addressed. To optimize wind farm layout, this study aims to statistically explore wind speed characteristics under different wind directions and [...] Read more.
Wind direction and speed are both crucial factors for wind farm layout; however, the relationship between the two factors has not been well addressed. To optimize wind farm layout, this study aims to statistically explore wind speed characteristics under different wind directions and wind direction characteristics. For this purpose, the angular–linear model for approximating wind direction and speed characteristics were adopted and constructed with specified marginal distributions. Specifically, Weibull–Weibull distribution, lognormal–lognormal distribution and Weibull–lognormal distribution were applied to represent the marginal distribution of wind speed. Moreover, the finite mixture of von Mises function (FVMF) model was used to investigate the marginal distribution of wind direction. The parameters of those models were estimated by the expectation–maximum method. The optimal model was obtained by comparing the coefficient of determination value (R2) and Akaike’s information criteria (AIC). In the numerical study, wind data measured at a featured wind farm in north China was adopted. Results showed that the proposed joint distribution function could accurately represent the actual wind data at different heights, with the coefficient of determination value (R2) of 0.99. Full article
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability 2019)
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Open AccessArticle Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM
Sustainability 2018, 10(10), 3693; https://doi.org/10.3390/su10103693
Received: 29 September 2018 / Revised: 11 October 2018 / Accepted: 12 October 2018 / Published: 15 October 2018
Cited by 1 | PDF Full-text (1230 KB) | HTML Full-text | XML Full-text
Abstract
Short-term wind speed prediction is of cardinal significance for maximization of wind power utilization. However, the strong intermittency and volatility of wind speed pose a challenge to the wind speed prediction model. To improve the accuracy of wind speed prediction, a novel model [...] Read more.
Short-term wind speed prediction is of cardinal significance for maximization of wind power utilization. However, the strong intermittency and volatility of wind speed pose a challenge to the wind speed prediction model. To improve the accuracy of wind speed prediction, a novel model using the ensemble empirical mode decomposition (EEMD) method and the combination forecasting method for Gaussian process regression (GPR) and the long short-term memory (LSTM) neural network based on the variance-covariance method is proposed. In the proposed model, the EEMD method is employed to decompose the original data of wind speed series into several intrinsic mode functions (IMFs). Then, the LSTM neural network and the GPR method are utilized to predict the IMFs, respectively. Lastly, based on the IMFs’ prediction results with the two forecasting methods, the variance-covariance method can determine the weight of the two forecasting methods and offer a combination forecasting result. The experimental results from two forecasting cases in Zhangjiakou, China, indicate that the proposed approach outperforms other compared wind speed forecasting methods. Full article
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability 2019)
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