Study of the Wind Speed Forecasting Applying Computational Intelligence

Zucatelli, Pedro Junior; Nascimento, Erick Giovani Sperandio; Santos, Alex Álisson Bandeira; Arce, Alejandro Mauricio Gutiérrez; Moreira, Davidson Martins

doi:10.5772/intechopen.89758

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…This research effort primarily concentrated on assessing the techno-economical benefit of wind energy potential attainable in the primary four wind passages of Aralvaimozhi, Shencottah, Palghat, and Cumbum in order to appreciate the nature of wind flow and its potential in the Tamil Nadu area [21], [22]. In this research study, authors had taken twenty years of (from the year 2000 to 2019) time-series data of historical, wind speed and direction data of 4 wind passes.…”

Section: Wind Energy Potential In Selected Locations With Unknown Fac...mentioning

confidence: 99%

The accessible large-scale renewable energy potential and its projected influence on Tamil Nadu's grid stability

Chandarahasan,

Sheeba Percis

2023

IJEECS

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Due to its inherent geographical potential for wind/solar power and other renewable energy sources (RES) generation, Tamil Nadu is one of the pioneering Indian states in the early development and utilisation of RES. Tamil Nadu accounts for roughly 25% of India's total wind energy capacity. Increased penetration of RES in a power system indicates that RESs have replaced traditional power plants that have historically managed and stabilised the power system, resulting in novel power flow situations. Grid stability must be enhanced in light of the integration of large-scale RES into the grid. The purpose of this research is to analyse and evaluate the impact of large-scale integration of RES by examining mitigating methods from the most recent academic articles and technical reports in this field. This will aid in identifying the key risks affecting the network's stability margin as a result of the widespread integration of RES generation, allowing for a reasonable increase in the network's stability margin. The study's findings will also help to guide the selection of appropriate mitigation strategies. As the stability margin improves, the future fraction of RES additions to the Tamil Nadu extra high voltage (EHV) grid will be increased significantly.

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Section: Wind Energy Potential In Selected Locations With Unknown Fac...mentioning

confidence: 99%

The accessible large-scale renewable energy potential and its projected influence on Tamil Nadu's grid stability

Chandarahasan,

Sheeba Percis

2023

IJEECS

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“…The generalized equation for STFT is given below in Eqs. (8) and (9). In addition, the spectrogram equation is shown in Eq.…”

Section: Fourier Transform and Stftmentioning

confidence: 99%

“…According to 2018 statistics, China has a maximum capacity of 221 GW, using more than one-third of the world's capacity. America 96.4 GW, Germany 59.3 GW, India 35 GW, and Spain 23 GW capacity are ranked with [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Wind Speed Analysis Using Signal Processing Technique

Akgün¹,

Akıncı²

2020

Renewable Energy - Resources, Challenges and Applications

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In this study, wind energy, the formation of this energy, and the necessary stages for the production of electrical energy will be discussed. Then, the countries' investments in wind energy will be mentioned. In the mathematical background, statistical methods and signal processing methods are used in the calculation of wind energy efficiency. In this chapter, a detailed analysis of the use of wind speed data with signal processing techniques will be made using the hourly wind speed data of Istanbul for the last 10 years. This data will be analyzed by the Fourier method. Afterward, analyses will be made with short-time Fourier transform (STFT) and bi-spectrum analysis method, and these results will be compared. The data obtained from the study can be considered as a framework for the wind farms to be constructed.

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“…As the motion of wind turbines and energy production depend on wind behavior, it is vital to accurately anticipate wind speed. Utilizing machine learning techniques, this prediction can be made (Zucatelli, Nascimento, Santos, Arce, & Moreira, 2021). The amount of wind that blows across the earth is more than enough to meet the world's electricity demand.…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach to forecast wind speed based on geographical location in Bangladesh

Shipra,

Rahaman,

Ara

et al. 2024

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This paper examines a learning approach to forecasting wind speed based on geographical location in Bangladesh. Most people use wind energy, a rapidly expanding renewable energy source, to produce electricity, replacing traditional fossil fuel-based electricity generation. The variable nature of wind speed necessitates an accurate estimate for planning wind power generation and grid integration. Machine learning models, based on various methods, commonly make wind speed predictions. This study implements machine learning algorithms, namely Random Forest Regression, XGBoost Regression, Multi-Layer Perceptron Regression, Ridge Regression, and Lasso Regression, to determine wind speed prediction accuracy. Firstly, we divide the dataset on wind records of Bangladesh into seven categories, each encompassing different geographical locations in Bangladesh, to account for the change in wind characteristics based on location, and then apply the algorithms to each category. The performance metrics Mean Absolute Error, Mean Squared Error, and Root Mean Square Error are utilized to draw comparisons. The findings showed that the performance of XGBoost makes it the most reliable tool for predicting wind speed in Bangladesh. The near proximity to large water bodies causes considerable variation in accuracy, i.e., it showed higher accuracy compared to the other three algorithms after applying the ensemble learning approach, which is more effective and accurate. This research will help in identifying optimal power plant locations and efficient linking methods to Bangladesh's power grid, ensuring smooth electricity access and efficient utilization of the country's energy resources.

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Study of the Wind Speed Forecasting Applying Computational Intelligence

Cited by 6 publications

References 23 publications

The accessible large-scale renewable energy potential and its projected influence on Tamil Nadu's grid stability

The accessible large-scale renewable energy potential and its projected influence on Tamil Nadu's grid stability

Wind Speed Analysis Using Signal Processing Technique

A machine learning approach to forecast wind speed based on geographical location in Bangladesh

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