Short-term Wind Speed Prediction Based On Quadratic Decomposition Improved Particle Swarm Optimization Extreme Learning Machine

Zhai, Chenchen; Li, Hanlin

doi:10.1109/ifeea57288.2022.10038229

Cited by 4 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the study offers guidelines for evaluating wind power forecasting processes, aiding wind turbine/plant operators in selecting the most suitable forecasting method based on factors such as the time period of interest, input features, computation time constraints, and error metrics. By providing a comprehensive review and guidance, this study contributes to the improvement of wind power forecasting practices, ultimately facilitating the efficient integration of wind energy into the power grid and enhancing the overall reliability and sustainability of energy systems [21], [22]. Figure 1 shows the block diagram of wind energy system.…”

Section: Methodsmentioning

confidence: 99%

Particle swarm optimization-extreme learning machine model combined with the ADA boost algorithm for short-term wind power prediction

Ponkumar,

Jayaprakash,

Ramasamy

et al. 2024

IJPEDS

View full text Add to dashboard Cite

In our proposed approach, we integrate ADA boosting with particle swarm optimization-extreme learning machine (PSO-ELM) to enhance the accuracy of wind power estimation, addressing the inherent unpredictability and variability in wind energy. Initially, we refine the thresholds and input weights of the extreme learning machine (ELM) and then construct the PSO-ELM prediction model. ADA Boost is utilized to generate multiple weak predictors, each comprising a distinct hidden layer node. The PSO technique is then employed to optimize the input weights and thresholds for each weak predictor. The final forecast is attained by amalgamating and weighting the outcomes from each weak predictor using a robust wind power forecast model. Experimental validation utilizing data from Turkish wind turbines underscores the efficacy of our approach. Comparative analysis against contemporary techniques such as ensemble learning models and optimal neural networks reveals that our ADA-PSO-ELM model demonstrates superior accuracy and generalizability in predicting wind power output under real-world conditions. The proposed approach offers a promising framework for addressing the challenges associated with wind power estimation, thereby facilitating more reliable and efficient utilization of wind energy resources.

show abstract

Section: Methodsmentioning

confidence: 99%

Particle swarm optimization-extreme learning machine model combined with the ADA boost algorithm for short-term wind power prediction

Ponkumar,

Jayaprakash,

Ramasamy

et al. 2024

IJPEDS

View full text Add to dashboard Cite

show abstract

“…On the one hand, it can improve the exploration speed of oil and gas fields, on the other hand, it can increase economic benefits and reduce pollution. Through a series of measures to achieve the goal of protecting ecological balance and sustainable developmentp [3][4]. Therefore, the state began to strengthen the regulation of sand and gravel, and achieved results.…”

Section: Introductionmentioning

confidence: 99%

Prediction Method of Sand Body Lithology Based on Machine Learning Algorithm and Dual Optimization of Attribute Characteristics

2023

View full text Add to dashboard Cite

With the development of science and technology, the prediction of earthquake disasters in China has made some progress. For some complex environments, target recognition gradually depends on machine learning algorithms. In order to accurately detect oil and gas fields, this paper intends to analyze and study the lithology of sand bodies before and after the earthquake. This paper intends to use machine learning algorithm to study the prediction method of sand body lithology, in order to improve the accuracy of identification and prediction. In this paper, experimental design and algorithm comparison are mainly used to analyze the scientific and technological use of sand body lithology prediction methods. The experimental results show that the ELLA algorithm proposed in this paper performs well in the classification and prediction of sand body lithology, and the error is less than 15%. Therefore, lithology prediction of sand body based on attribute characteristics can be calculated by ELLA algorithm.

show abstract

Multiple decomposition‐aided long short‐term memory network for enhanced short‐term wind power forecasting

Balci,

Dokur,

Yuzgec

et al. 2023

IET Renewable Power Gen

View full text Add to dashboard Cite

With the increasing penetration of grid‐scale wind energy systems, accurate wind power forecasting is critical to optimizing their integration into the power system, ensuring operational reliability, and enabling efficient system asset utilization. Addressing this challenge, this study proposes a novel forecasting model that combines the long‐short‐term memory (LSTM) neural network with two signal decomposition techniques. The EMD technique effectively extracts stable, stationary, and regular patterns from the original wind power signal, while the VMD technique tackles the most challenging high‐frequency component. A deep learning‐based forecasting model, i.e. the LSTM neural network, is used to take advantage of its ability to learn from longer sequences of data and its robustness to noise and outliers. The developed model is evaluated against LSTM models employing various decomposition methods using real wind power data from three distinct offshore wind farms. It is shown that the two‐stage decomposition significantly enhances forecasting accuracy, with the proposed model achieving R2 values up to 9.5% higher than those obtained using standard LSTM models.

show abstract

Short-term Wind Speed Prediction Based On Quadratic Decomposition Improved Particle Swarm Optimization Extreme Learning Machine

Cited by 4 publications

References 16 publications

Particle swarm optimization-extreme learning machine model combined with the ADA boost algorithm for short-term wind power prediction

Particle swarm optimization-extreme learning machine model combined with the ADA boost algorithm for short-term wind power prediction

Prediction Method of Sand Body Lithology Based on Machine Learning Algorithm and Dual Optimization of Attribute Characteristics

Multiple decomposition‐aided long short‐term memory network for enhanced short‐term wind power forecasting

Contact Info

Product

Resources

About