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Energies
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Solar and Wind Energy Prediction and Its Application Technology
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Solar and Wind Energy Prediction and Its Application Technology

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

Deadline for manuscript submissions: 15 October 2024 | Viewed by 460

Special Issue Editor

Dr. Young-Min Wi

E-Mail Website
Guest Editor
Department of Electrical Engineering, Sangmyung University, Seoul 03016, Republic of Korea
Interests: power system operation and planning, particularly in load forecasting and its applications

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to original research on renewable energy forecasting technologies and their applications. Our aim is to disseminate knowledge and experience on topics from experts in various fields. The following topics will be focused on:

  • Short-term renewable energy forecasting.
  • Hybrid forecasting models for renewable energy integration.
  • Machine learning and artificial intelligence for advanced renewable energy forecasting.
  • Application of renewable energy forecasting in smart grids.
  • Behind-the-meter solar generation forecasting technology and its applications.
  • Power system operation technology based on renewable energy prediction technology.
  • Renewable energy forecasting techniques related to the electricity market.
  • Economic and regulatory aspects of renewable energy forecasting.

This Special Issue invites original and innovative research articles on renewable energy forecasting techniques and their application domains. The goal is to provide a comprehensive overview of the latest advancements and stimulate further research and development in this field.

Dr. Young-Min Wi
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 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 energy forecasting
  • behind-the-meter
  • hybrid forecasting models
  • smart grid
  • wind power
  • photovoltaic power

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Published Papers (1 paper)

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Research

27 pages, 4883 KiB  
Article
Applied Machine Learning to Study the Movement of Air Masses in the Wind Farm Area
by Vladislav N. Kovalnogov, Ruslan V. Fedorov, Andrei V. Chukalin, Vladimir N. Klyachkin, Vladimir P. Tabakov and Denis A. Demidov
Energies 2024, 17(16), 3961; https://doi.org/10.3390/en17163961 - 9 Aug 2024
Viewed by 283
Abstract
Modeling the atmospheric boundary layer (ABL) in the area of a wind farm using computational fluid dynamics (CFD) methods allows us to study the characteristics of air movement, the shading effect, the influence of relief, etc., and can be actively used in studies [...] Read more.
Modeling the atmospheric boundary layer (ABL) in the area of a wind farm using computational fluid dynamics (CFD) methods allows us to study the characteristics of air movement, the shading effect, the influence of relief, etc., and can be actively used in studies of local territories where powerful wind farms are planned to be located. The operating modes of a wind farm largely depend on meteorological phenomena, the intensity and duration of which cause suboptimal operating modes of wind farms, which require the use of modern tools for forecasting and classifying precipitation. The methods and approaches used to predict meteorological phenomena are well known. However, for designed and operated wind farms, the influence of meteorological phenomena on the operating modes, such as freezing rain and hail, remains an urgent problem. This study presents a multi-layered neural network for the classification of precipitation zones, designed to identify adverse meteorological phenomena for wind farms according to weather stations. The neural network receives ten inputs and has direct signal propagation between six hidden layers. During the training of the neural network, an overall accuracy of 81.78%, macro-average memorization of 81.07%, and macro-average memorization of 75.05% were achieved. The neural network is part of an analytical module for making decisions on the application of control actions (control of the boundary layer of the atmosphere by injection of silver iodide, ionization, etc.) and the formation of the initial conditions for CFD modeling. Using the example of the Ulyanovsk wind farm, a study on the movement of air masses in the area of the wind farm was conducted using the initial conditions of the neural network. Digital models of wind turbines and terrain were created in the Simcenter STAR-CCM+ software package, version 2022.1; an approach based on a LES model using an actuating drive disk model (ADM) was implemented for modeling, allowing calculation with an error not exceeding 5%. According to the results of the modeling of the current layout of the wind turbines of the Ulyanovsk wind farm, a significant overlap of the turbulent wake of the wind turbines and an increase in the speed deficit in the area of the wind farm were noted, which significantly reduced its efficiency. A shortage of speed in the near and far tracks was determined for special cases of group placement of wind turbines. Full article
(This article belongs to the Special Issue Solar and Wind Energy Prediction and Its Application Technology)
►▼ Show Figures

Figure 1

Figure 1
<p>A fundamental example of how a single neuron works in a network.</p> Full article ">Figure 2
<p>The network architecture.</p> Full article ">Figure 3
<p>Weather study area [<a href="#B50-energies-17-03961" class="html-bibr">50</a>], with weather stations [<a href="#B49-energies-17-03961" class="html-bibr">49</a>].</p> Full article ">Figure 4
<p>A neural network training.</p> Full article ">Figure 5
<p>Error matrix for weather-classification model.</p> Full article ">Figure 6
<p>Precipitation formation zones.</p> Full article ">Figure 7
<p>Structure model of wind turbine.</p> Full article ">Figure 8
<p>Vertical profiles of the time-averaged streamwise velocity [<a href="#B58-energies-17-03961" class="html-bibr">58</a>]: solid line—results of CFD modeling using the proposed approach; dotted line—results obtained by Chamorro L.P.</p> Full article ">Figure 9
<p>Plane sections of the computational domain.</p> Full article ">Figure 10
<p>Speed distribution scene in the computational domain behind the simulated wind turbine.</p> Full article ">Figure 11
<p>Wind farm computational domain.</p> Full article ">Figure 12
<p>Velocity profile for eastern wind direction: (<b>a</b>) wind speed 6 m/s; (<b>b</b>) wind speed 9 m/s; (<b>c</b>) wind speed 12 m/s.</p> Full article ">Figure 12 Cont.
<p>Velocity profile for eastern wind direction: (<b>a</b>) wind speed 6 m/s; (<b>b</b>) wind speed 9 m/s; (<b>c</b>) wind speed 12 m/s.</p> Full article ">Figure 13
<p>Data lines across the wind farm in the computational domain for easterly wind direction.</p> Full article ">Figure 14
<p>Wind farm area velocity deficit with eastern wind direction: (<b>a</b>) wind speed 6 m/s; (<b>b</b>) wind speed 9 m/s; (<b>c</b>) wind speed 12 m/s.</p> Full article ">Figure 15
<p>Data line along a group of wind turbines in the computational domain for easterly wind direction.</p> Full article ">Figure 16
<p>Velocity deficit for a group of wind turbines with eastern wind direction: (<b>a</b>) wind speed 6 m/s; (<b>b</b>) wind speed 9 m/s; (<b>c</b>) wind speed 12 m/s.</p> Full article ">Figure 17
<p>Velocity profile for southern wind direction: (<b>a</b>) wind speed 6 m/s; (<b>b</b>) wind speed 9 m/s; (<b>c</b>) wind speed 12 m/s.</p> Full article ">Figure 17 Cont.
<p>Velocity profile for southern wind direction: (<b>a</b>) wind speed 6 m/s; (<b>b</b>) wind speed 9 m/s; (<b>c</b>) wind speed 12 m/s.</p> Full article ">Figure 18
<p>Data lines across the wind farm in the computational domain for southerly wind direction.</p> Full article ">Figure 19
<p>Velocity deficit behind the wind farm for eastern wind direction: (<b>a</b>) wind speed 6 m/s; (<b>b</b>) wind speed 9 m/s; (<b>c</b>) wind speed 12 m/s.</p> Full article ">Figure 20
<p>Longitudinal data line for a group of wind turbines in the computational domain for the southern wind direction.</p> Full article ">Figure 21
<p>Velocity deficit behind a group of wind turbines with a southern wind direction: (<b>a</b>) wind speed 6 m/s; (<b>b</b>) wind speed 9 m/s; (<b>c</b>) wind speed 12 m/s.</p> Full article ">
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