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Advanced Forecasting Methods for Sustainable Power Grid
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Advanced Forecasting Methods for Sustainable Power Grid

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

Deadline for manuscript submissions: 20 February 2025 | Viewed by 6031

Special Issue Editors

Dr. Cristina Ventura

E-Mail Website
Guest Editor
DIEEI – Electrical Electronic and Computer Engineering, University of Catania, 95125 Catania, Italy
Interests: photovoltaic systems; forecasting for photovoltaic systems; photovoltaic/thermal systems; photovoltaic systems monitoring; fault detection in photovoltaic systems; distributed photovoltaic resources
Special Issues, Collections and Topics in MDPI journals
Dr. Santi Agatino Rizzo

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, University of Catania, 95125 Catania, Italy
Interests: power electronics; power systems; applied optimization; applied machine learning; reliability Photo:
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There is currently a large deployment of smart power grid systems that include various renewable energy sources, such as photovoltaic and wind energy. These renewable energy sources could have considerable impacts on power grid systems from both the technical and environmental sides. The generated renewable energy can cause discontinuity of energy production due to the non-programmable and unpredictable nature of renewable sources. In fact, the output of plants powered by non-programmable renewable energy sources (NPRESs) significantly changes the hourly pattern of zonal loads that need to be met by conventional generation plants. Thus, NPRESs introduce a stochastic component into the electricity demand related to the inherent variability of weather conditions, making the residual electricity load increasingly intermittent and harder to predict. As a result, the high penetration of NPRESs plants results in increasing imbalances between demand and generation and an increasing difficulty in building up the reserve margins needed to manage the randomness of the load, while providing security and stability to the grid. For this reason, there have been increasing efforts by the research community to establish accurate forecasting systems. This Special Issue aims to present advanced forecasting methods with applications that cover various practical challenges in sustainable power grids.

Topics to be covered in this Special Issue include but are not limited to the following:
• Forecasting of PV and wind power generation;
• Energy demand forecasting;
• Forecast models for wind speed and solar radiations;
• Forecast models for grid connected MPPT;
• Electric vehicle load forecasting;
• Electricity price forecasting;
• Forecasting techniques for smart grids;
• Artificial intelligence and data-driven approaches;
• Application of forecasting techniques in power systems;
• Anomalies and faults prediction.

Dr. Cristina Ventura
Dr. Santi Agatino Rizzo
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 submissions that pass pre-check are 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 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 2600 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

  •  renewable forecasting
  •  renewable energy sources
  •  solar radiation forecasting
  •  wind speed forecasting
  •  fault detection
  •  power forecasting
  •  MPPT forecasting
  •  artificial intelligence

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Published Papers (6 papers)

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Research

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12 pages, 2529 KiB  
Article
Evaluation and Long-Term Prediction of Annual Wind Farm Energy Production
by Seunggun Hyun and Youn Cheol Park
Energies 2024, 17(21), 5332; https://doi.org/10.3390/en17215332 - 26 Oct 2024
Viewed by 575
Abstract
A comparison and evaluation of the AEP(Annual Energy Production) of a wind farm were conducted in this study with a feasibility study and using the actual operation data from the S wind farm on Jeju Island from January 2020 to December 2022. The [...] Read more.
A comparison and evaluation of the AEP(Annual Energy Production) of a wind farm were conducted in this study with a feasibility study and using the actual operation data from the S wind farm on Jeju Island from January 2020 to December 2022. The free wind speed data were selected from the data measured from a nacelle anemometer, the correlation equation between wind speed and AEP was obtained, and the annual average wind speed for the past 20 years was predicted using the MCP method. As a result, comparing the AEP from the operation data with that estimated in the feasibility study, we found that the AEP was reduced by approximately 2.40% in 2020 and 12.14% in 2021, and increased by 6.76% in 2022. The wind speeds over the 20-year lifetimes of the wind turbines were obtained, and the AEP that could be generated at the S wind farm indicated that it could be used for operation. In the future, the S wind farm will operate at between 25% and 30% availability for the remaining 17 years of operation. If the availability falls below 25%, there will be a need to check the reasons for the deterioration of wind turbine performance and the frequency of failures. Full article
(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid)
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27 pages, 5740 KiB  
Article
Advanced Optimal System for Electricity Price Forecasting Based on Hybrid Techniques
by Hua Luo and Yuanyuan Shao
Energies 2024, 17(19), 4833; https://doi.org/10.3390/en17194833 - 26 Sep 2024
Viewed by 531
Abstract
In the context of the electricity sector’s liberalization and deregulation, the accurate forecasting of electricity prices has emerged as a crucial strategy for market participants and operators to minimize costs and maximize profits. However, their effectiveness is hampered by the variable temporal characteristics [...] Read more.
In the context of the electricity sector’s liberalization and deregulation, the accurate forecasting of electricity prices has emerged as a crucial strategy for market participants and operators to minimize costs and maximize profits. However, their effectiveness is hampered by the variable temporal characteristics of real-time electricity prices and a wide array of influencing factors. These challenges hinder a single model’s ability to discern the regularity, thereby compromising forecast precision. This study introduces a novel hybrid system to enhance forecast accuracy. Firstly, by employing an advanced decomposition technique, this methodology identifies different variation features within the electricity price series, thus bolstering feature extraction efficiency. Secondly, the incorporation of a novel multi-objective intelligent optimization algorithm, which utilizes two objective functions to constrain estimation errors, facilitates the optimal integration of multiple deep learning models. The case study uses electricity market data from Australia and Singapore to validate the effectiveness of the algorithm. The forecast results indicate that the hybrid short-term electricity price forecasting system proposed in this paper exhibits higher prediction accuracy compared to traditional single-model predictions, with MAE values of 7.3363 and 4.2784, respectively. Full article
(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid)
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22 pages, 4605 KiB  
Article
Probabilistic Analysis of Green Hydrogen Production from a Mix of Solar and Wind Energy
by Agnieszka Dudziak, Arkadiusz Małek, Andrzej Marciniak, Jacek Caban and Jarosław Seńko
Energies 2024, 17(17), 4387; https://doi.org/10.3390/en17174387 - 2 Sep 2024
Viewed by 755
Abstract
This article describes an example of using the measurement data from photovoltaic systems and wind turbines to perform practical probabilistic calculations around green hydrogen generation. First, the power generated in one month by a ground-mounted photovoltaic system with a peak power of 3 [...] Read more.
This article describes an example of using the measurement data from photovoltaic systems and wind turbines to perform practical probabilistic calculations around green hydrogen generation. First, the power generated in one month by a ground-mounted photovoltaic system with a peak power of 3 MWp is described. Using the Metalog family of probability distributions, the probability of generating selected power levels corresponding to the amount of green hydrogen produced is calculated. Identical calculations are performed for the simulation data, allowing us to determine the power produced by a wind turbine with a maximum power of 3.45 MW. After interpolating both time series of the power generated by the renewable energy sources to a common sampling time, they are summed. For the sum of the power produced by the photovoltaic system and the wind turbine, the probability of generating selected power levels corresponding to the amount of green hydrogen produced is again calculated. The presented calculations allow us to determine, with probability distribution accuracy, the amount of hydrogen generated from the energy sources constituting a mix of photovoltaics and wind. The green hydrogen production model includes the hardware and the geographic context. It can be used to determine the preliminary assumptions related to the production of large amounts of green hydrogen in selected locations. The calculations presented in this article are a practical example of Business Intelligence. Full article
(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid)
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24 pages, 5692 KiB  
Article
Short-Term Forecasts of Energy Generation in a Solar Power Plant Using Various Machine Learning Models, along with Ensemble and Hybrid Methods
by Paweł Piotrowski and Marcin Kopyt
Energies 2024, 17(17), 4234; https://doi.org/10.3390/en17174234 - 24 Aug 2024
Viewed by 654
Abstract
High-quality short-term forecasts of electrical energy generation in solar power plants are crucial in the dynamically developing sector of renewable power generation. This article addresses the issue of selecting appropriate (preferred) methods for forecasting energy generation from a solar power plant within a [...] Read more.
High-quality short-term forecasts of electrical energy generation in solar power plants are crucial in the dynamically developing sector of renewable power generation. This article addresses the issue of selecting appropriate (preferred) methods for forecasting energy generation from a solar power plant within a 15 min time horizon. The effectiveness of various machine learning methods was verified. Additionally, the effectiveness of proprietary ensemble and hybrid methods was proposed and examined. The research also aimed to determine the appropriate sets of input variables for the predictive models. To enhance the performance of the predictive models, proprietary additional input variables (feature engineering) were constructed. The significance of individual input variables was examined depending on the predictive model used. This article concludes with findings and recommendations regarding the preferred predictive methods. Full article
(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid)
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23 pages, 5845 KiB  
Article
A Novel Twin Support Vector Regression Model for Wind Speed Time-Series Interval Prediction
by Xinyue Fu, Zhongkai Feng, Xinru Yao and Wenjie Liu
Energies 2023, 16(15), 5656; https://doi.org/10.3390/en16155656 - 27 Jul 2023
Cited by 4 | Viewed by 1307
Abstract
Although the machine-learning model demonstrates high accuracy in wind speed prediction, it struggles to accurately depict the fluctuation range of the predicted values due to the inherent uncertainty in wind speed sequences. To address this limitation and enhance the reliability, we propose an [...] Read more.
Although the machine-learning model demonstrates high accuracy in wind speed prediction, it struggles to accurately depict the fluctuation range of the predicted values due to the inherent uncertainty in wind speed sequences. To address this limitation and enhance the reliability, we propose an effective wind speed interval prediction model that combines twin support vector regression (TSVR), variational mode decomposition (VMD), and the slime mould algorithm (SMA). In our methodology, the complex wind speed series is decomposed into multiple relatively stable subsequences using the VMD method. The principal component and residual series are then subject to interval prediction using the TSVR model, while the remaining components undergo point prediction. The SMA method is employed to search for optimal parameter combinations. The prediction interval of wind speed is obtained by aggregating the forecasting results of all TSVR models for each subseries. Our proposed model has demonstrated superior performance in various applications. It ensures that the wind speed value falls within the designated interval range while achieving the narrowest prediction interval. For instance, in the spring dataset with 1-period, we obtained a predicted interval with a prediction intervals coverage probability (PICP) value of 0.9791 and prediction interval normalized range width (PINRW) value of 0.0641. This outperforms other comparative models and significantly enhances its practical application value. After adding the residual interval prediction model, the reliability of the prediction interval is significantly improved. As a result, this study presents a novel twin support vector regression model as a valuable approach for multi-step wind speed interval prediction. Full article
(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid)
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Review

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25 pages, 1979 KiB  
Review
Comprehensive Review of Building Energy Management Models: Grid-Interactive Efficient Building Perspective
by Anujin Bayasgalan, Yoo Shin Park, Seak Bai Koh and Sung-Yong Son
Energies 2024, 17(19), 4794; https://doi.org/10.3390/en17194794 - 25 Sep 2024
Viewed by 1359
Abstract
Energy management models for buildings have been designed primarily to reduce energy costs and improve efficiency. However, the focus has recently shifted to GEBs with a view toward balancing energy supply and demand while enhancing system flexibility and responsiveness. This paper provides a [...] Read more.
Energy management models for buildings have been designed primarily to reduce energy costs and improve efficiency. However, the focus has recently shifted to GEBs with a view toward balancing energy supply and demand while enhancing system flexibility and responsiveness. This paper provides a comprehensive comparative analysis of GEBs and other building energy management models, categorizing their features into internal and external dimensions. This review highlights the evolution of building models, including intelligent buildings, smart buildings, green buildings, and zero-energy buildings, and introduces eight distinct features of GEBs related to their efficient, connected, smart, and flexible aspects. The analysis is based on an extensive literature review and a detailed comparison of building models across the aforementioned features. GEBs prioritize interaction with the power grid, which distinguishes them from traditional models focusing on internal efficiency and occupant comfort. This paper also discusses the technological components and research trends associated with GEBs, providing insights into their development and potential evolution in the context of sustainable and efficient building design. Full article
(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid)
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