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67 

 

 

 

Review 

Advanced neural network and hybrid models for 

wind power forecasting: a comprehensive global 

review 
Malixole Sambane1, Bongumsa Mendu1,2*, Bessie Baakanyang Monchusi1 

1Department of Electrical and Smart Systems Engineering, University of South Africa, Pretoria, South Africa 
2National Transmission Company South Africa SOC Ltd, Maxwell Dr, Sunninghill, Sandton, 2157, South Africa 

A R T I C L E   I N F O 
 

Article history: 
Received 01 September 2024  
Received in revised form 
05 October 2024 
Accepted 14 October 2024 
 
Keywords: 
Neural network, Hybrid models, Forecasting,  
Wind power  
*Corresponding author 
Email address:  
mendubongumsa@gmail.com 

 
DOI: 10.55670/fpll.fuen.3.4.5 

A B S T R A C T 
 

Neural Network Algorithms (NNAs), modeled after the workings of biological 
neurons, are increasingly utilized in areas like data mining and robotics to 
address complex challenges in artificial intelligence (AI). This research will 
undertake a systematic review based on advanced neural networks and hybrid 
models for wind power forecasting. Using the Scopus database, a methodical 
search, acquisition, and filtering procedure was utilized to find pertinent 
publication documents; VOSviewer software was utilized to analyze trends. The 
emphasis on improving prediction accuracy and stability in wind power 
forecasting through the application of cutting-edge machine learning 
techniques and hybrid models is a prominent feature that unites the literature. 
Furthermore, attention is being paid to resolving issues pertaining to the 
production of wind energy, such as wind power fluctuation management, grid 
integration problems, wind speed prediction, and turbine health monitoring. A 
rising trend involves multi-dimensional, multi-step forecasting and 
incorporating factors like weather data and spatial-temporal features to 
enhance reliability. This paper contributes by exploring the integration of 
optimization techniques with neural networks, investigating hybrid models to 
improve wind power predictions, assessing LSTM-based approaches in 
forecasting, and suggesting directions for future research. 
 

 
1. Introduction  

Today, Neural Network Algorithms (NNAs) are 
computational models inspired by biological neural networks 
designed to process information and solve complex AI 
problems [1]. These algorithms have gained prominence in 
various fields, including robotics [2], and data mining [3]. 
NNAs are crucial for their ability to learn from data, adapt to 
new information, and make predictions [4]. Recent 
advancements in deep learning have further enhanced their 
capabilities, particularly in computer vision, speech 
processing, and IoT applications [5]. The effectiveness of 
NNAs depends on selecting appropriate architectures and 
training algorithms. Ongoing research focuses on developing 
innovative topologies, optimization methods, and 
applications in quantum computing and differential 
equations [6]. As NNAs continue to evolve, they offer powerful 
tools for handling high-dimensional data and automating 
feature extraction processes [7]. Furthermore, in engineering 
and construction, NNAs are used for structural analysis, 
materials optimization, energy efficiency forecasting, and 
smart city technologies [8]. They excel in pattern recognition 

tasks like character and handwriting recognition [9, 10]. In 
business, NNAs are employed for hedge fund analytics, 
marketing segmentation, and fraud detection [11]. 
Unsupervised NNAs, such as autoencoders and self-
organizing maps, are particularly useful in exploratory data 
analysis, biomedical imaging, and financial applications when 
labeled datasets are unavailable [12]. In healthcare, NNAs can 
predict disease severity, as demonstrated in epidermolysis 
bullosa simplex, with 78% accuracy [13]. NNAs are also 
applied in system identification, vehicle control, quantum 
chemistry, and natural resource management [14]. Their 
ability to simulate nonlinear phenomena and identify hidden 
patterns in large databases makes them valuable tools across 
various industries. In this work, the discussion will be 
narrowed specifically to its application for wind power 
generation. Wind power generation is a rapidly growing 
renewable energy source with significant potential for 
addressing global energy demands and environmental 
concerns [15]. It offers numerous advantages, including low 
carbon emissions, resource conservation, and flexible 
applications [16]. The development of wind energy 

 

 

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M. Sambane et al. /Future Energy                                                                                         November 2024| Volume 03 | Issue 04| Pages 67-79 

68 

 

technology has been dramatic since the 1980s, with many 
countries setting ambitious targets for its implementation 
[17]. Key aspects of wind power systems include importance 
analysis for identifying critical components [18], integration 
with smart grids and storage systems [19], and advanced 
control strategies for efficient operation [20]. However, 
challenges remain, such as technological limitations, 
environmental impacts, and grid integration issues [21]. 
Despite these obstacles, wind power is expected to play an 
increasingly important role in the global energy landscape, 
driven by ongoing research, technological advancements, and 
supportive government policies [22]. Neural networks have 
emerged as powerful tools for improving wind power 
efficiency and forecasting. They have been applied to turbine 
control, wind farm optimization, and blade design [23]. 
Various machine learning approaches, including artificial 
neural networks (ANNs), recurrent neural networks, support 
vector machines, and extreme learning machines, have shown 
promising results in wind power forecasting [24]. Multilayer 
perceptron structures with Purelin and Sigmoid activation 
functions are commonly used [25]. Recent advancements 
include combining ANNs with dependability models to 
enhance short-term production estimation. Time-series 
methods, fuzzy logic, and hybrid models have also been 
explored [26]. The integration of physical, statistical, and 
hybrid methods has improved forecasting accuracy across 
different time horizons [27]. Overall, neural network 
applications in wind power forecasting have increased 
significantly, offering improved accuracy compared to 
individual methods [28]. In summary of the key obversions 
from the previous related literature analyzed above, neural 
networks have emerged as powerful tools for enhancing wind 
power efficiency and forecasting. It is evidence that they have 
been effectively applied to turbine control, wind farm 
optimization, and blade design.  

Various machine learning approaches, including artificial 
neural networks (ANNs), recurrent neural networks, support 
vector machines, and extreme learning machines, have shown 
promising results in wind power forecasting. Commonly used 
structures include multilayer perceptrons with Purelin and 
Sigmoid activation functions. Recent advancements involve 
combining ANNs with dependability models to improve 
short-term production estimation. Additionally, time-series 
methods, fuzzy logic, and hybrid models have been explored, 
leading to better forecasting accuracy. The integration of 
physical, statistical, and hybrid methods has further 
improved accuracy across different time horizons. Overall, 
the application of neural networks in wind power forecasting 
has significantly increased, offering enhanced accuracy 
compared to individual methods. However, there is a notable 
gap in the literature regarding a comprehensive review of 
neural network algorithms in wind power generation. Thus, 
the aim of this work is to conduct a systematic review of the 
applications of neural network algorithms in wind power 
generation, focusing on key contributions, among others, such 
as: 
• Reviewing the applications of LSTM models in wind power 

generation, highlighting their effectiveness in improving 
long-term forecasting accuracy. 

• Exploring the use of short-term memory-based models 
• Examining the integration of various optimization 

techniques with neural network models to enhance the 
accuracy and reliability of wind power forecasts. 

• Analyzing different neural network methodologies applied 
to time series data in wind farms, focusing on their 
predictive capabilities. 

• Investigating the combination of hybrid models and 
machine learning techniques to improve the prediction of 
wind speed and power output. 

• Detailing various algorithms and neural network models 
specifically designed for predicting wind energy, and their 
performance in different scenarios. 

• Reviewing the application of neural network models in 
accurately predicting wind speed and power output, 
emphasizing recent advancements. 

• Proposal for possible future work directions will be 
discussed.  

The remaining part of the paper is organized as follows: 
Section 2 introduces the relevant theory of similar or related 
work explored or completed, while Section 3 explores the 
methods or processes utilized to search, retrieve, collect, and 
analyze relevant information and documents on the different 
search engines and software. Section 4 consists of analyzed 
literature papers, and Section 5, summarizes the analyzed 
literature papers' observations. Section 5 interprets and 
discusses results, while Section 6 discusses the results under 
network and overlay visualization. Section 7, suggests and 
recommends proposals for future research, and Section 8 
summarizes the main conclusions of this work. 

2. Wind energy systems  

To combat climate change and achieve a sustainable 
energy future, offshore wind energy is becoming more and 
more important. Real-time performance monitoring and 
predictive maintenance are now possible thanks to the 
revolution in industrial systems brought about by advances in 
data-driven and machine-learning technologies. For fault 
detection and operational optimization, accurate wind 
turbine models are essential. Notwithstanding, there are 
obstacles, including wind direction, speed, power generation, 
and performance statistics [29]. Since wind energy doesn't 
produce pollution like hydropower or coal does, it is an 
essential alternative energy source. Its potential regions 
remain unidentified, and the variability of wind speed 
impacts its generation capacity. Statistics on the availability 
of wind energy are essential for inventory preparation. The 
generation of wind energy capacity is predicted using soft 
computing techniques, such as weather and historical data 
modeling. Artificial intelligence techniques such as neural 
networks and fuzzy logic have been used by researchers to 
develop energy estimation and prediction methods that are 
more accurate and efficient than conventional statistical 
methods [30]. The economic, social, political, and 
environmental aspects of renewable resources are the main 
subjects of research, with wind energy receiving special 
challenges for the operation of the electrical network. With a 
variety of storage technologies available, energy storage 
systems in conjunction with wind power can improve grid-
connection capability [31]. In the past, researchers have 
proposed various statistical approaches related to wind 
power, wind speed, and energy prediction [32]. The Artificial 
Neural Network structure comprises three layers: input, 
hidden, and output. The input layer receives network inputs, 
the hidden layer processes information, and the output layer 
provides network response. The number of neurons in the 
input layer equals the number of inputs, while the number of 
neurons in the output layer corresponds to the number of 
outputs, see Figure 1, where X1, X2 are the input functions 



M. Sambane et al. /Future Energy                                                                                         November 2024| Volume 03 | Issue 04| Pages 67-79 

69 

 

(wind speed, temperature etc) Wij is the signal weight, and f is 
an activation function [33]. 

 
Figure 1. Artificial neural network structure  

Artificial Neural Network activation functions map 
inputs and hidden layers, advancing the node output from one 
layer to the following and introducing irregularities into the 
network's modeling abilities. A neuron's functional form is 
determined by its activation function; for example, a linear 
activation function multiplies the neuron's value by the 
learned weight [34]. The activation function is expressed as: 

𝑓 =  
1

1+exp(𝑥)
                                                                                       (1) 

A set of '𝑖′ synapses with weight 𝑊𝑖  that are supplied by 
a signal 𝑋𝑖  can have either a positive or a negative weight; a 
negative weight inhibits the sum of the junction's output, 
while a positive weight has a remarkable impact [35], signal 
weight is expressed as: 

𝑊 = 𝑊𝑖𝑗 + ∆𝑊𝑖𝑗                                                                             (2) 

The wind system's power production fluctuates depending on 
a few factors, including air density and rotor blade area, but it 
changes more dramatically in response to wind speed. Air 
density, blade area, and wind speed all affect the wind energy 
system's output power. The wind power distribution 
equation for a certain wind turbine can be calculated by 
utilizing its power curve. The wind speed distribution 
function at an area is determined by the mean wind speed. 
One can calculate the mean power density (mean power 
available) per unit of surface. Getting power from the wind, 
considering Betz's law, realistic values, and wind turbine 
factors like Cut-In and Cut-Out wind speed, rated speed, and 
rated power can all be used in this procedure [36]. One helpful 
tool for simulating the operation of a wind turbine is its power 
curve. It displays the power output at a given wind speed. 
Figure 2 displays a typical power curve for a pitch-regulated 
wind turbine. The lowest speed at which there is no power 
output is known as the cut-in speed. Power grows quickly in 
the second zone, which is between the rated speed and the 
cut-in. The output in the third region doesn't change until the 
cut-off speed is reached. After this, the turbine is turned off to 
shield its internal parts from strong winds [37]. Therefore, 
accurate wind speed and power forecasting are crucial for 
reducing wind power fluctuations in system dispatch 
planning. Deep learning-based models are increasingly being 
considered due to their ability to handle complex nonlinear 
problems. However, scheduling, management, and 
optimization remain the main challenges for high penetration 
of renewable energy sources like wind power [38]. 

 
Figure 2. Wind turbine power curve model 

The following depicts the equation: Where:𝑃𝑤 is the 
power extracted from the wind source in watts (W), 𝜌 is the 

density of air in (
𝐾𝑔

𝑚3
), 𝐴 is the area of blades of a rotor in (𝑚2), 

and 𝑣 is the speed of wind in (
𝑚

𝑠
) [39].  

𝑃𝑤 = 0.5𝜌𝐴𝑣3                                                                                  (3) 

3. Methodology 

The PRISMA approach was employed to systematically 
identify and refine the focus on the intersection of neural 
networks and wind power generation using the SCOPUS 
database. This comprehensive method followed the four key 
stages of PRISMA: Identification, Screening, Eligibility, and 
Inclusion. 

3.1 Identification  
The SCOPUS database was selected as the primary source 

for downloading relevant information. The process began by 
accessing the SCOPUS database and configuring the search 
parameters. Initially, the "Article title, Abstract, and 
Keywords" option was chosen from the dropdown menu in 
the search box. Keywords "Neural Network" AND "Wind 
Power Generation" were entered, ensuring that the search 
would return documents containing both phrases exactly as 
specified. This search yielded an initial result of 811 
documents. 

3.2 Screening  
To refine the search results, several filters were 

systematically applied. First, the results were sorted by 
relevance, ensuring that the most pertinent documents 
appeared at the top. Then, the publication year range was set 
from 2019 to 2023, reducing the document count to 435. To 
narrow the focus further, the subject area was limited to 
Engineering, which brought the count down to 285 
documents. Document type was then filtered to include only 
articles, resulting in 169 documents. The language filter was 
set to English, reducing the count to 158 documents. Finally, 
the year range was further restricted to 2021–2023 to ensure 
the most recent information, leaving 110 documents. Table 1 
is an overview of inclusions and exclusions considered in this 
study.  

3.3 Eligibility 
In the eligibility phase, the relevant papers were selected 

and exported for detailed analysis. All documents were 
highlighted, and the CSV format was chosen for export, 
successfully exporting 234 documents. This file was saved 
under a new folder on the desktop as the master file, which 
was preserved without any editing. A copy of this file, named 
"SCOPUS documents," was created for editing and analysis 
purposes. 



M. Sambane et al. /Future Energy                                                                                         November 2024| Volume 03 | Issue 04| Pages 67-79 

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Table 1. Inclusions and exclusions 

 

3.4 Inclusions 
The next step involved a thorough review of the data. The 

SCOPUS documents Excel file was opened, and the abstracts 
in column R were reviewed to assess the relevance of each 
document. To facilitate this process, three new columns (S, T, 
and U) were created. Column S was used to identify the aim, 
objective, or purpose of each document, the problems 
addressed, and the methods or techniques used. Column T 
listed all variables used as inputs and outputs in each 
document, while Column U identified the type of forecasting 
(short-term, medium-term, or long-term) if applicable. 
Documents that were deemed irrelevant were highlighted in 
red and subsequently deleted from both the SCOPUS 
documents file and the SCOPUS master file, ensuring 
consistency between the two files. Initially, both files 
contained 110 documents, but after removing non-relevant 
entries, the count was reduced to 64 documents. 

3.5 Analysis using VOSviwer 
For deeper analysis, VOSviewer software was 

downloaded and installed. The software was used to identify 
research gaps (network visualization) and current trending 
field patterns (overlay visualization). The process began by 
selecting the option to create a map based on bibliographic 
data, specifically choosing the Scopus CSV file for upload. Co-
occurrence analysis was performed, focusing on all keywords 
with a full counting method. A threshold of a minimum of five 
occurrences was set, resulting in 22 keywords meeting the 
criteria. The resulting map highlighted key topics and trends 
within the research area. The map was saved and exported for 
further analysis. Clusters identified in the map were copied to 
an Excel spreadsheet, where they were edited and analyzed. 
This detailed analysis helped to identify current trends and 
gaps in the research on neural networks and wind power 
generation. 

4. Literature review results analysis 

4.1 Long short term forecasting 
Hong et al. [40] developed a mixed classical-quantum 

model that predicts wind speeds forecast for twenty-four 

hours ahead using a long term short term memory and a 
quantum network model to address the issue of power 
planning uncertainty of renewable energy by power 
companies in different countries, including South Korea, 
Taiwan, China, and Philippines. In Switzerland, Basel, Sun et 
al. [41] established a wind energy forecasting system using 2 
stage attention and a short long term memory system. The 
approach significantly increased forecast accuracy while 
reducing the variable nature of climatic circumstances. With 
a focus on a wind farm in Hunan Province, Wang et al. [42] 
explored a wind energy forecasting model that made use of a 
long-term short-term memory system and incorporated 
Gaussian mutation technique and erratic sequence for 
enhanced stability and search performance. Using statistically 
learned methods, Ahmad T and Zhang D [43] improved 
feature reliability and performance across Belgian, 
Distribution System Operators (DSO)-Connected, and Elia 
sites by using an in-depth sequence to sequence; long term 
short term memory regression approach for accurate wind 
energy projection. They employed the week ahead prediction. 

4.2 Short term memory based forecasting 
Anushalini T and Sri Revathi B [44] identified a deep 

learning model appropriate for wind power forecasting with 
the aim to correctly predict power produced per hour using 
wind speed, air temperature, pressure, and air direction as 
inputs and power produced as output. Cheng L et al. [45] 
explored a novel spatial temporal approach using an 
enhanced neural system for short-term forecasting of wind 
energy to address the issue of fluctuating. In Australia, 
Hossain et al. [46], in the Boco Rock Wind plant, used a 
combined model of deep learning to precisely forecast wind 
power production in intervals of five and ten minutes. In 
Australia, to accurately predict wind power production. 
Hossain et al. [47] explored a deep hybrid learning model to 
accurately enhance a short-term wind power prediction at 
the Bodangora Wind plant. An et al. [48] analyzed data 
provided by three organizations to predict accurately wind 
power using a short-term model that entailed a diverse wind 
velocity combination. Li et al. [49] explored a short-term 
adjustable graph network model based on temporospatial to 
accurately predict wind power. Sopena et al. [50] presented a 
comparative analysis of up to thirty minutes ahead of the 
short-term prediction of wind energy utilizing a collection of 
artificial models based on neural systems and the primary 
decomposition techniques from a wind plant in Ireland. In 
Ireland, Gonzalez et al. [51] presented a researched short-
term wind energy prediction system that utilized a spiked 
neural system that was tailored to the processing capabilities 
of Intel’s Loihi.   

4.3 Optimization techniques and neural network models 
for wind power forecasting  
To precisely forecast wind power values, Tarek et al. [52] 

proposed a novel optimization method based on swarm 
particle optimization and fractal stochastic search to enhance 
long-term short memory network parameters. Wu et al. [53] 
proposed a technique model to enhance the factors of Long-
short term memory using particle swarm adjustment and 
erratic fractal to forecast precise values of wind power. 
Becanin et al. [54] proposed a forecasting energy method that 
utilizes long short-term memory and gated recurrent units to 
address the issue of managing the grid power, which also 
explored an improved algorithm of swarm intelligence. Grace 
R.K, and Manimegalai R [55] integrated a novel model that 
entails grey wolf optimization and back propagation neural 
system to predict wind speed; wavelet transform to divide 

Item 
No. 

Description Criteria 
Inclusion  Exclusion 

1  Database Scopus  Other databases 

2  Publication 
period 

2021-2023 Documents published 
in 2020 and before. 

3  Document 
type 

Articles book chapters, books, 
notes, letters, editorials 
reviews, conferences. 

4  Subject area Engineering Energy, Mathematics, 
Physical, life, social, 
health and humanities 
sciences 

5  Language English All other languages 

6  File Type CSV RIS, BibTex, plain text, 
etc. (please list all as I 
showed you) 

7  
8  

Design 
keywords 

“Neural 
Network” 
AND “Wind 
Power 
Generation” 

All documents outside 
specified used 
keywords  



M. Sambane et al. /Future Energy                                                                                         November 2024| Volume 03 | Issue 04| Pages 67-79 

71 

 

wind speed into an in-depth band. To accomplish precise and 
effective power forecasting. Zhang et al. [56] suggested a wind 
energy forecasting system that used a logistic chaotic atom 
seek optimized enhanced back propagation neural system. 
Huang et al. [57] developed an ideal ensemble approach in 
Taiwan’s Changhua for an hourly prediction of wind energy 
one day ahead of time. Three steps made up the suggested 
optimum ensemble approach. In order to forecast wind speed 
in Northwest China, Zhu L and Hu W [58] suggested a 
technique method that processed wind speed data by 
combining an optimized variational modal breakdown 
method with an optimized depth belief neural network. Wang 
et al. [59] introduced a deep neural system to anticipate wind 
energy production, and it suggested a storage hydrogen wind-
pumped storage combination that employs deep learning as 
well as intelligent optimization. 

4.4 Neural network approaches for predictive time 
series in wind farms  
In China’s Xinjiang wind farm, Ai et al. [59] explored the 

use of neural networks with erratic attribute analysis to 
create a predictive time series system. The wind farm data 
was analyzed using an integrated prediction approach, which 
improved the precision of predictions. To deal with the 
unpredictable and resolved nature of wind plants in local 
energy forecasting, a unique multiple-purpose optimal 
continuous neural system with time pattern awareness was 
explored by Chen et al. [60]. Qu et al. [61] explored 
multidimensional power time series, intrinsic mode signals, 
and convolutional neural systems which are combined with 
bidirectional long short-term memory and attention 
mechanisms to estimate wind energy in a wind farm located 
in Liaoning Province, China. To estimate wind speed in the 
short term, Chen et al. [63] explored a mixed approach that 
combined neural networks, time-varying restrictions, modal 
degradation, permutation entropy, adaptive noise, neuro-
fuzzy inference, packet data analysis, and an enhanced 
monarch butterfly optimization method. Drawing inspiration 
from the remarkable capabilities of deep neural systems in 
machine vision, Liu et al. [64] provided a novel method for 
forecasting short-term wind energy by utilizing the machine 
learning system to analyze time series pictures. 

4.5 Hybrid models and machine learning techniques for 
wind speed and power prediction  
In Canada, Saskatchewan, Abbasipour et al. [65] 

addressed the issue of wind speed prediction using a twenty-
four-hour ahead hybrid model of neural network that entailed 
5 algorithms of networks. Xiong et al. [66] worked on a hybrid 
design prediction system built on descent and meta-heuristic 
planning to accurately predict wind power and lessen 
computing load. Hong Y and Santos J [67] suggested a unique 
hybrid model that combines period and latent long-term, 
short-term memory improved by optimizing particle swarms 
to forecast a day wind speed. In Italy, Finamore et al. [68] 
explored a hybrid wind power prediction model that used an 
organized framework method that grouped weather data and 
then used the Person’s scrutiny method to locate key 
elements in each group. Peng et al. [69] proposed a 1 step-
ahead power prediction method using a hybrid long-term 
short-term memory and a convolutional deep learning 
method. Shah et al. [70] explored a hybrid prediction model 
that entails ripple transform and swarm particle optimization 
to address the issue of stability in wind power generation 
integration. In Indonesia, Barus and Dalimi [71] presented an 
extensive hybrid machine learning solution that integrated a 
seasonal moving average on daily hourly operational reserves 

with specific neural system variables. Long-term short-term 
memory produced the most precise results. Wang et al. [72] 
developed a combined innovative forecasting technique 
consisting of data preprocessing and combination strategy to 
accurately predict wind speeds for power generation. In this 
paper, Xiao et al. [73] explored a hybrid model with optimized 
hyperparameters gated recurrent unit neural network model 
and feature-weighted principal component analysis, which 
lessened the effects of unpredictability, noisy data, and 
instability in wind energy production. 

4.6 Hybrid and neuro-fuzzy models for enhanced wind 
power prediction  
In Singapore, Abdullah and Hassan [74] used a neuro-

fuzzy short-term hybrid forecast over a twenty-four-hour 
ahead model to improve the wind power generation 
prediction, and the results showed 94% accuracy. Roy et al. 
[75] forecasted 1 hour ahead wind speed accuracy using a 
synthetic neural network model that compares the input to 
output data and maps it out, and uses a changeable neuro-
fuzzy system to precisely estimate grid power reference for 
the forecasted hour period. In this paper, Xu et al. [76] 
presented a reiterative neuro fuzzy hammerstein approach-
based projective control system for wind turbines, which 
addressed the issue of regulating power output. 

4.7 Algorithms and neural network models for wind 
energy prediction  
Peiris et al. [77] created an artificial neural system 

algorithm to predict the amount of wind energy generated at 
Sri Lanka’s operational Pawan Danawi wind farm. The 
algorithm utilized the produced wind energy as a dependent 
factor and wind direction, wind speed, and local temperature 
as independent factors. Xiong et al. [78] suggested and 
explored a multiple-view deep learning network architecture 
to estimate wind energy using a wide range of information, 
including wind speed, wind direction, and wind power. Chen 
and Han [79] advanced to balance and stabilize the accuracy 
and wind speed control by using a control method of reward 
adaptive that entailed controlling the pitch angle and torque 
of the generator in different conditions. Xia et al. [80] 
explored and enhanced a stacked gated recurrent unit neural 
network. They used it to predict the production of wind 
energy and electrical load in both single and multiple-variable 
scenarios. To improve calculations and uncover hidden 
features, Liu et al. [81] presented a novel deep and transfer 
learning framework that developed efficient data-driven 
wind energy forecasting algorithms for wind turbines. Liu et 
al. [82] explored a sophisticated forecasting technique that 
allowed for both precise and accurate wind power estimates 
using a standard differential equation system with attention 
support paired with a long-term short-term memory system. 
In Montreal, Shirzandi et al. [83], due to the erratic behavior 
of wind power production, developed a forecasting model of 
48 hours ahead using arithmetic weather forecasting, 
comparing wind speed inputs and output power generation. 
A synthetic neural network architecture for wind energy 
prediction was examined in detail by Huang et al. [84] using 
just historical wind speed and wind energy production 
statistics for a half hour ahead from a Wind plant in southeast 
Australia. Gu et al. [85] used collected wind speed information 
from Chuanshan Port Area, Ningbo-Zhoushan Port, and 
developed an improved Wavelet Neural Network wind speed 
prediction model. In Northwest China, Qu et al. [86] 
investigated the high association properties of wind farm 
groups and learned their spatial features using a spatial-
temporal deep learning system. Ozbek et al. [87] explored a 



M. Sambane et al. /Future Energy                                                                                         November 2024| Volume 03 | Issue 04| Pages 67-79 

72 

 

machine intelligence method for precise prediction of wind 
speeds in Turkey's Marmara and Mediterranean regions. It 
does this by predicting short-term wind speed data 1 hour 
ahead of time using neural networks and the Adaptive Neuro 
Fuzzy Inference System. An algorithm for projecting the 
production of wind energy, both probabilistic and 
predictable, as well as related methods, were explored by Wu 
et al. [88]. Wind speed from Taiwan’s Central Weather Bureau 
was used. Sun et al. [89] to address wind power fluctuations 
on the grid. They explored a hybrid model of regulating 
disturbance with an inverter on the grid side. For wind energy 
projections in the Netherlands and Germany, Wahdany et al. 
[90] explored a neural network topology that directly 
considered changing energy system conditions to optimize 
system costs.  

4.8 Neural network models for wind speed and power 
prediction  
Li et al. [91] used eight models to accurately predict wind 

speeds. Results showed that the convolutional neural model 
performed better. Song Y et al. [92] presented a method used 
to precisely forecast wind power using convolutional graphs 
and convolutional neural systems, mixing spatiotemporal 
features. Wang et al. [93] created a comprehensive multiple 
variates mix short-term forecasting of wind speeds system. It 
consisted of sophisticated feature selection techniques and 
angle prediction models built on convolutional neural 
systems. In this research, Nguyen et al. [94] proposed a 
layered temporal convolutional system method to handle the 
multi-step forward prediction and increase the reliability of 
short-term wind energy predictions. This technique tackles 
the problem of depending on long-term memory. In Japan, 
Sari et al. [95] explored deep learning that is based on a wind 
model of one-hour ahead prediction with the intention of 
establishing a precise prediction model that is made of 3-
dimensional neural network convolutional and long-short-
term deep convolutional memory. 

4.9 Stability and predictability in renewable energy 
systems enhancement  
Alzain and Liu [96] undertook to resolve the issue of 

uncertainty stability of voltage in the system caused by 
renewable loads by exploring a model that extracts data 
between sources using a deep kernel emulator. To tackle the 
issue of turbine power imbalance causing frequency 
deviations, Sun et al. [97] explored the use of a load frequency 
control as a secondary measure for power systems. In 
Denmark and the Netherlands, Yu T and Yang R [98] explored 
a wind prediction method to address the wind generation 
issue by implementing a changeable model that retrieves 
wind data from different meteorological sources. In this 
paper, Memmel et al. [99] proposed a selection method of n-1 
to address the issue of congestion on the grid. To overcome 
blade health issues through an additional task and data 
enhancement neural systems, an independently overseen 
approach method was presented by Sun et al. [100]. Zhang et 
al. [101] used wind speed prediction and wind power 
simulations as its two main elements, developed a system of 
management for windfarms. To improve renewable system 
dependability, Tan et al. [102] presented an hour-ahead wind 
power production forecasting model that used wind speeds 
as input data. Wang et al. [103] explored a model free 
adaptable fifteen seconds ahead wind predicting controller to 
pitch-varying systems, including speed disruption 
suppression to address variations in wind output. 

 

5. Literature review analysis observations  

The literature provided presents a comprehensive 
overview of various methodologies and models employed for 
wind power forecasting across different regions globally. One 
key noticeable aspect throughout the literature is the 
emphasis on enhancing prediction accuracy and stability in 
wind power forecasting through the utilization of advanced 
machine learning techniques and hybrid models. These 
techniques include deep learning architectures such as 
convolutional neural networks (CNNs), long short-term 
memory (LSTM) networks, gated recurrent units (GRUs), and 
their combinations with optimization algorithms like particle 
swarm optimization and fractal stochastic search. 
Additionally, there is a focus on addressing specific challenges 
related to wind energy production, such as wind speed 
prediction, wind power fluctuation management, grid 
integration issues, and turbine health monitoring. Moreover, 
there is a notable trend towards incorporating multi-
dimensional and multi-step forecasting methods, as well as 
considering various environmental factors such as weather 
data and spatial-temporal features to improve forecasting 
reliability. The literature highlights the significance of 
accurate wind power forecasting for optimizing energy 
system operations, enhancing grid stability, and facilitating 
the integration of renewable energy sources into the power 
grid. 

6. Trends analysis  

6.1 Network visualization   
The tables below represent the network visualization or 

gaps within this topic. The table consists of keywords, namely 
clusters, links, total link strength, and occurrences as 
headings. They are categorized according to clusters, which 
range from clusters 1 to 5. Table 2 is the combination of 
clusters 1 and 2, where cluster 1 consists of 16 keywords, and 
long short-term memory has a higher number of 243 total link 
strength, indicating a higher association and relation strength 
for the keyword. This normally indicates that the higher the 
total link strength, the higher the occurrence for the keyword, 
and this is observable from Table 2. Cluster 2 consists of 14 
keywords, and electric load dispatching has a higher number 
of 89 total link strengths, which indicates that there is a higher 
association and relation strength for the keyword. This 
normally indicates that the higher the total link strength, the 
higher the occurrence for the keyword, and this is observable 
from Table 2 below.  

Table 3 represents clusters 3, 4, and 5, and in cluster 3, it 
can be observed that it consists of 9 keywords. Wind Power 
has a higher number of 487 total link strength, which 
indicates that there is a higher association and relation 
strength on the keyword. This normally indicates that the 
higher the total link strength, the higher the occurrence for 
the keyword, which is observable from the table below. 
Cluster 4, it can be observed that it consists of 8 keywords. 
Wind power generation has a higher number of 214 total link 
strengths, which indicates that there is a higher association 
and relation strength on the keyword. This normally indicates 
that the higher the total link strength, the higher the 
occurrence for the keyword, and this is observable from Table 
3. Cluster 5, it can be observed that it consists of 5 keywords. 
Particle swarm optimization (pso) has a higher number of 
128 total link strengths, which indicates that there is a higher 
association and relation strength on the keyword. This 
normally indicates that the higher the total link strength, the 
higher the occurrence for the keyword, which is observable 
from Table 3. 



M. Sambane et al. /Future Energy                                                                                         November 2024| Volume 03 | Issue 04| Pages 67-79 

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Table 2. Keywords with links, total link strength, occurrences based on application of neural network regression algorithms in wind power 

generation 

Cluster 1 - Red 
Keywords cluster Links Total link strength Occurrences 

long short-term memory 1 50 243 24 
wind speed 1 49 185 17 
mean square error 1 45 133 12 
learning systems 1 43 112 11 
deep learning 1 45 111 12 
wind speed forecasting 1 38 98 9 
convolution 1 38 90 9 
machine learning 1 39 84 7 
errors 1 44 83 7 
stochastic systems 1 32 72 8 

machine-learning 1 34 69 6 
learning algorithms 1 35 62 5 
multilayer neural networks 1 36 60 5 
deep neural networks 1 32 59 7 
time series 1 28 56 6 
convolutional neural networks 1 29 52 5 

Cluster 2 - Green 
Keywords cluster Links Total link strength Occurrences 
electric load dispatching 2 36 89 9 
brain 2 37 77 7 
electric utilities 2 35 61 7 
energy utilization 2 24 61 7 
neural-networks 2 48 61 15 
optimization 2 36 61 8 
power generation 2 49 61 26 
recurrent neural networks 2 30 61 7 
weather forecasting 2 51 61 40 
wind farm 2 37 61 10 
wind power forecasting 2 40 61 12 
wind power prediction 2 27 61 5 
wind power predictions 2 36 61 9 
algorithm 2 34 54 5 

 

Table 3. Keywords with links, total link strength, occurrences based on application of neural network regression algorithms in wind power 

generation 

Cluster 3 - Blue 
Keywords cluster Links Total link strength Occurrences 
wind power 3 51 487 56 
electric power generation 3 51 311 36 
forecasting 3 47 122 12 
wind 3 35 86 11 
electric power transmission networks 3 39 72 8 
speed 3 32 70 8 
fuzzy inference 3 30 57 6 
fuzzy neural networks 3 30 57 6 
fuzzy systems 3 28 50 5 

Cluster 4 - Yellow 
Keywords cluster Links Total link strength Occurrences 
wind power generation 4 49 214 25 
artificial neural network 4 47 128 13 
neural networks 4 35 92 11 
predictive models 4 39 79 8 
wind turbines 4 34 70 9 
short term prediction 4 32 57 5 
short-term prediction 4 32 57 5 
wind forecasting 4 25 50 5 

CLUSTER 5 - PURPLE 
Keywords cluster Links Total link strength Occurrences 
particle swarm optimization (pso) 5 43 128 12 
particle swarm 5 40 92 8 
swarm optimization 5 36 81 7 
particle swarm optimization 5 33 63 5 
meteorology 5 27 55 5 

 

 

 



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74 

 

Figure 3 is a Network visualization, which consists of 
different colors that represent a gap between the different 
research topics. The bigger the circle means that much 
research has been done on that topic, and the smaller the 
circle means less work or research has been done on that 
topic and needs further research.  The lines between the 
topics show how the topics are related to each other; the more 
lines you will have between two topics, the more strength 
exists between them, and the closer the topics are, the more 
relationships can be observed between the topics. The graph 
has five different colors, which are clustered accordingly, 
namely, Cluster number 1 is indicated in red. It can be 
observed that long short-term memory has a bigger circle, 
meaning more research has been done on the topic, with 
machine learning having a smaller circle, indicating less 
research work being completed on the topic. Cluster number 
2 is indicated in green. It can be observed that weather 
forecasting as well as power generation have bigger circles, 
meaning more research has been done on those topics, with 
algorithm and optimization having smaller circles, indicating 
less research work being completed on the topics. Cluster 
number 3 is indicated in blue. It can be observed that wind 
power as well as electric power generation have bigger 
circles, meaning more research has been done on those topics, 
with fuzzy inference and fuzzy systems having smaller circles 
indicating less research work being completed on the topics. 
Cluster number 4 is indicated in yellow. It can be observed 
that wind power generation, as well as artificial neural 
networks, has bigger circles, meaning more research has been 
done on those topics, with wind turbines and neural networks 
having smaller circles, indicating less research work being 
completed on the topics. Cluster number 5 is indicated in 
purple. It can be observed that particle swarm optimization 
(pso) has a bigger circle, meaning more research has been 
done on the topic, and meteorology has a smaller circle, 
indicating less research work is being completed on the topic. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6.2 Overlay visualization results 
Figure 4 illustrates an overlay visualizing which 

represents a research trend on the topics. It shows the most 
recently researched topics and areas by using different colors. 
The periods have been averaged as depicted on the scale; it 
starts from 2021.8; this means that 2021 is the year, and the 
.8 represents month 8; this follows the same trend for the 
other periods. From 2021.8 to 2022.0, it shows that the 
investigation has been more on speed, wind, etc., and moving 
towards 2022.0 to 2022.2, the trends were more into learning 
systems and errors, then by 2022.2 to 2022.4, the 
investigation was more based on weather foresting and wind 
power. From around 2022.4 to 2022.6, the focus was more on 
particle swarm optimization artificial neural networks. The 
research topic for the above graph shows the yellow color that 
from the year period of 2022.6 coming towards 2022.8, the 
researched topics have been under Power generation, which 
has a bigger circle representing that much research has been 
explored on the subject and also a few topics namely, electric 
load dispatching, algorithm, neural networks having smaller 
circles representing less researched or explored topics for the 
future. 

7. Future proposal recommendation 

Owing to the wind's sudden changes in density, speed, 
and other important factors, the following topics are 
recommendations that still need to be thoroughly researched 
to guarantee consistent generation and a larger role for this 
source in the electrical power framework.  
• Examine how learning algorithms like convolutional neural 

networks (CNNs) and multilayer neural networks can be 
combined to increase the accuracy of wind power 
prediction time series forecasting. 

• Create innovative wind power prediction algorithms by 
combining knowledge from cutting-edge machine learning 
techniques and conventional forecasting methods. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Figure 3. Network visualization based on the application of neural network regression algorithms in wind power generation 

  

  



M. Sambane et al. /Future Energy                                                                                         November 2024| Volume 03 | Issue 04| Pages 67-79 

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• Investigate how to incorporate fuzzy neural networks and 
fuzzy inference systems into wind power prediction 
models to address imprecision and uncertainty in 
meteorological data. 

• By utilizing particle swarm optimization algorithms and 
incorporating knowledge from meteorology research, 
create sophisticated short-term prediction models for wind 
forecasting. 

• Create optimization frameworks that improve the accuracy 
and dependability of short-term wind power predictions 
by fusing meteorological data with particle swarm 
optimization techniques. 

8. Conclusion 

This research undertook a systematic review based on 

the advanced neural network and hybrid models for wind 

power forecasting. Using the Scopus database, a methodical 

search, acquisition, and filtering procedure was utilized to 

find pertinent publication documents; VOSviewer software 

was employed to analyze trends. Numerous studies 

demonstrate the critical role that precise wind power 

forecasting plays in improving grid stability, accelerating the 

integration of renewable energy sources into the grid, and 

optimizing the performance of the energy system. By applying 

sophisticated machine learning algorithms and hybrid 

models, wind power forecasting can be made more accurate 

and stable. Deep learning techniques involve the use of 

various designs, such as convolutional neural networks 

(CNNs), long short-term memory (LSTM) networks, and gated 

recurrent units (GRUs), along with optimization algorithms 

like particle swarm optimization and fractal stochastic search.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

In addition to analyzing the integration of optimization 

techniques with neural networks and offering a thorough 

review of LSTM and short-term memory-based models in 

wind power generation, this paper contributed by examining 

hybrid models that can enhance predictions of wind speed 

and power output. Along with outlining the effectiveness of 

different neural network approaches and algorithms used in 

wind energy forecasting, it also provided recommendations 

for future research directions. 

Acknowledgment 
The authors gratefully acknowledge the University of South 
Africa, 28 Pioneer Ave, Florida Park, Roodepoort, for 
providing the resources essential to this research.  

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically concerning authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere in any language. 

Data availability statement 
Data sharing does not apply to this article as no datasets were 

generated or analyzed during the current study.  

Conflict of interest 

The authors declare no potential conflict of interest. 

 

 

 

Figure 4. Overlay visualization based on application of neural network regression algorithms in wind power generation 

  

  



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