E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

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: 31 October 2018

Special Issue Editors

Guest Editor
Prof. Dr. Sonia Leva

Department of Energy, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milano MI, Italy
Website | E-Mail
Interests: power quality; renewable energy and storages; smart grid; PV forecasting
Guest Editor
Dr. Emanuele Ogliari

Department of Energy, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 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 Power and 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 Power and 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

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

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

Published Papers (11 papers)

View options order results:
result details:
Displaying articles 1-11
Export citation of selected articles as:

Research

Jump to: Other

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
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)
Figures

Figure 1

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
PDF Full-text (3853 KB) | HTML Full-text | XML Full-text
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)
Figures

Figure 1

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
PDF Full-text (4000 KB) | HTML Full-text | XML Full-text
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)
Figures

Figure 1

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
PDF Full-text (1327 KB) | HTML Full-text | XML Full-text
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)
Figures

Figure 1

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
PDF Full-text (6705 KB) | HTML Full-text | XML Full-text
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)
Figures

Graphical abstract

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
PDF Full-text (408 KB) | HTML Full-text | XML Full-text
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)
Figures

Figure 1

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
PDF Full-text (12962 KB) | HTML Full-text | XML Full-text
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)
Figures

Figure 1

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
PDF Full-text (14219 KB) | HTML Full-text | XML Full-text
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)
Figures

Figure 1

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
PDF Full-text (18251 KB) | HTML Full-text | XML Full-text
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)
Figures

Figure 1

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 2 | 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)
Figures

Figure 1

Other

Jump to: Research

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
PDF Full-text (157 KB) | HTML Full-text | XML Full-text
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)

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Designing, developing and implementing a forecasting method for the produced and consumed electricity in the case of small wind farms situated on wind deflection hilly terrain  

Alexandru Pîrjan, George Căruțașu, Dana-Mihaela Petroșanu

Abstract: The 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, having as a main purpose the overcoming of limitations that consist in lowering the forecasting accuracy, arising from the wind deflection, caused by the hilly terrain. A specific aspect of our devised forecasting method consists in 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 that have the ability to map between a data set of numeric inputs and a set of numeric targets. Another specific element of our approach consists in improving the 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 as main beneficiaries the power plant operators, but it can also be successfully applied in order to assess the energy potential of a wind deflection hilly area, 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 wind deflection hilly terrain.

Keywords: forecasting method; produced and consumed electricity; small wind farms; hilly terrain; wind deflection; long short-term memory neural networks; feed-forward function fitting neural networks

Back to Top