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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 March 2024 | Viewed by 1129

Special Issue Editors

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

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

Published Papers (1 paper)

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Research

23 pages, 5845 KiB  
Article
A Novel Twin Support Vector Regression Model for Wind Speed Time-Series Interval Prediction
Energies 2023, 16(15), 5656; https://doi.org/10.3390/en16155656 - 27 Jul 2023
Cited by 1 | Viewed by 754
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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