Wind Speed Prediction Based VMD-BLS and Error Compensation

Jiao, Xuguo; Zhang, Daoyuan; Song, Dongran; Mu, Dongdong; Tian, Yu‐Ping; Wu, H. R.

doi:10.20944/preprints202304.0785.v1

Cited by 4 publications

(1 citation statement)

References 20 publications

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“…Also, combustion of non-renewable sources such as fossil fuels is inviting serious environmental hazards. Henceforth advancing renewable energy is one of the vital gears of today's world in the ways of suggesting the best technique to address the variability bags that are attached to wind power generation (Jiao et al, 2023). At this stage at which energy needs are globally advancing, greenhouse gasses capture for higher feasting of energy produced by red-hot fossil fuels; those sources should be reduced by renewable energy sources.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

A. M.,

J. M.,

J. K.

et al. 2024

Advanced Journal of Science, Technology and Engineering

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Climate change always had a massive effect on worldwide cities. which can only be decreased through considering renewable energy sources (wind energy, solar energy). However, the need to focus on wind energy prediction will be the best solution to the world electricity petition. Wind power (WP) estimating techniques have been used for diverse literature studies for many decades. The hardest way to improve WP is its nature of differences that make it a tough undertaking to forecast. In line with the outdated ways of predicting wind speed (WS), employing machine learning methods (ML) has become an essential tool for studying such a problem. The methodology used for this study focuses on sanitizing efficient models to precisely predict WP regimens. Two ML models were employed “Gaussian Process Regression (GPR), and Feed Forward Neural Network (FFNN)” for WS estimation. The experimental methods were used to focus the WS prediction. The prophecy models were trained using a 24-hour’ time-series data driven from Kano state Region, one of the biggest cities in Nigeria. Thus, investigating the (ML) forecast performance was done in terms of coefficient of determination (R²), linear correlation coefficient (R), Mean Square Error (MSE), and Root Mean square error (RMSE). Were. The predicted result shows that the FFNN produces superior outcomes compared to GPR. With R²= 1, R = 1, MSE = 6.62E-20, and RMSE = 2.57E-10

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

A. M.,

J. M.,

J. K.

et al. 2024

Advanced Journal of Science, Technology and Engineering

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Wind Speed Modeling for Wind Farms Based on Deterministic Broad Learning System

Wang,

Xue

2023

Atmosphere

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As the penetration rate of wind power in the grid continues to increase, wind speed forecasting plays a crucial role in wind power generation systems. Wind speed prediction helps optimize the operation and management of wind power generation, enhancing efficiency and reliability. However, wind speed is a nonlinear and nonstationary system, and traditional statistical methods and classical intelligent algorithms struggle to cope with dynamically updating operating conditions based on sampled data. Therefore, from the perspective of optimizing intelligent algorithms, a wind speed prediction model for wind farms was researched. In this study, we propose the Deterministic Broad Learning System (DBLS) algorithm for wind farm wind speed prediction. It effectively addresses the issues of data saturation and local minima that often occur in continuous-time system modeling. To adapt to the continuous updating of sample data, we improve the sample input of the Broad Learning System (BLS) by using a fixed-width input. When new samples are added, an equivalent number of old samples is removed to maintain the same input width, ensuring the feature capture capability of the model. Additionally, we construct a dataset of wind speed samples from 10 wind farms in Gansu Province, China. Based on this dataset, we conducted comparative experiments between the DBLS and other algorithms such as Random Forest (RF), Support Vector Regression (SVR), Extreme Learning Machines (ELM), and BLS. The comparison analysis of different algorithms was conducted using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Among them, the DBLS algorithm exhibited the best performance. The RMSE of the DBLS ranged from 0.762 m/s to 0.776 m/s, and the MAPE of the DBLS ranged from 0.138 to 0.149.

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Comprehensive Evaluation of Marine Ship Fires Risk Based on Fuzzy Broad Learning System

Zhang

Liu

et al. 2023

JMSE

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Ship fires exhibit the main characteristics of a high possibility of occurrence, large load, fast spreading, high difficulty in extinguishing, and serious losses. Therefore, once a fire occurs, it will cause huge loss in terms of economic and personnel safety. Firstly, a ship fire risk evaluation indicator system was constructed based on the causes and severity of the fires. Secondly, a comprehensive evaluation method for the fuzzy broad learning system (FBLS) was proposed. The fuzzy system was used to implement feature mapping on the input data, and the extracted fuzzy features were further input into the BLS enhancement layer. A fuzzy broad learning neural network structure was constructed by combining fuzzy features, feature nodes, and enhancement nodes. The method was applied to the field of risk assessment for the first time, and is a reference for subsequent studies. Finally, the risk levels of ship fires were classified and compared with evaluation methods such as fuzzy support vector machine (FSVM) and Fuzzy BP neural network (FBPNN) to demonstrate effectiveness and accuracy. The proposed FBLS method was used to predict actual cases, and the results showed consistency with the level determined by the accident investigation report published by the Maritime Bureau Administration.

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Wind Speed Prediction Based VMD-BLS and Error Compensation

Cited by 4 publications

References 20 publications

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

Wind Speed Modeling for Wind Farms Based on Deterministic Broad Learning System

Comprehensive Evaluation of Marine Ship Fires Risk Based on Fuzzy Broad Learning System

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