Wind Power Prediction Based on LSTM Networks and Nonparametric Kernel Density Estimation

Zhou, Bowen; Ma, Xiangjin; Luo, Yanhong; Yang, Dongsheng

doi:10.1109/access.2019.2952555

Cited by 120 publications

(38 citation statements)

References 35 publications

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“…However, Figs. [3][4][5] show that there are no big differences among the results from the 5-fold, 8-fold, and 10-fold cross-validations. Thus, in the following analysis, we only consider the 5-fold cross-validation.…”

Section: Full Modelmentioning

confidence: 92%

“…However, this approach cannot be implemented in real-time due to the use of a discrete wavelet transformation that needs the availability of batch data. In [3], an advanced machine learning-based model has been applied to predict wind power. This model is based on K-Means-long short-term memory (K-Means-LSTM) network model, which has an extended capacity to describe time dependencies in time series data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wind Power Prediction Using Ensemble Learning-Based Models

Lee

Harrou

et al. 2020

IEEE Access

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Wind power is one of the most potential energies and the major available renewable energy sources. Precisely predicting wind power production is essential for the management and the integration of wind power in a smart grid. The goal of this study is to predict wind power production with sufficient accuracy based on various factors using ensemble learning-based methods that consider the time-dependent nature of the wind power measurements. Essentially, the ensemble learning methods combine multiple learners to obtain an enhanced prediction performance in comparison to conventional standalone learners. In addition, they reduce the overall prediction error and have the capacity to merge various models. At first, this paper investigates the prediction capability of the well-known ensemble approaches Boosted Trees, Random Forest, and Generalized Random Forest for wind power prediction. We compared the prediction performance of these ensemble models to two frequently used prediction methods: Gaussian process regression, and Support Vector Regression. Experimental measurements recorded every ten minutes from actual wind turbines located in France and Turkey are used to test the prediction efficiency of the studied models. Experimental results have shown that the ensemble methods can predict wind power production with high accuracy compared to the standalone models. Furthermore, the findings clearly reveal that the lagged variables contribute significantly to the ensemble models, and permits constructing more parsimonious models.

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Section: Full Modelmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Wind Power Prediction Using Ensemble Learning-Based Models

Lee

Harrou

et al. 2020

IEEE Access

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show abstract

“…Several models are used to predict the original wind power series, and the weighted method is used to combine the prediction results of each model to get the prediction results. These models include particle swarm optimization-support vector regression (PSO-SVR) and Grey model (Zhang et al, 2019b), SCADA and deep learning neural networks (Lin et al, 2020), long short-term memory (LSTM) networks and nonparametric kernel density (Zhou et al, 2019), multi-Layer perceptron (MLP) and adaptive-network-based fuzzy inference system (ANFIS) (Morshedizadeh et al, 2018), Gaussian processes and neural network (Lee and Baldick, 2014), chaotic theory and Bernstein neural network (Wang et al, 2016), ARIMA, ELM, SVM, LSSVM, and Gaussian process regression (GPR) (Wang et al, 2015b), and so on.…”

Section: The Deterministic Prediction Of Wind Powermentioning

confidence: 99%

A state-of-the-art review on wind power deterministic prediction

Tian

2020

Wind Engineering

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With the continuous growth of wind power access capacity, the impact of intermittent and volatile wind power generation on the grid is becoming more and more obvious, so the research of wind power prediction method has been widely concerned. Accurate wind power prediction can provide necessary support for the power grid dispatching, combined operation of generating units, operation, and maintenance of wind farms. According to the existing wind power prediction methods, the wind power prediction methods are systematically classified according to the time scale, model object, and model principle of prediction. The physical methods, statistical methods include single and ensemble prediction methods related to wind power prediction are introduced in detail. The error evaluation indicator of the prediction method is analyzed, and the advantages and disadvantages of each prediction method and its applicable occasions are given. At the same time, in view of the existing problems in the wind power prediction method, the corresponding improvement plan is put forward. Finally, this article points out that the research is needed for wind power prediction in the future.

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“…Recent studies have mostly focused on the prediction of the wind power time series. In [4], Zhou et al proposed to use K-means clustering to process historical wind power data to acquire multiple training sets, and then use long short-term memory (LSTM) neural network to predict the future wind power. Yan et al [5] used wavelet transform to deconstruct the wind power time series to obtain multiple sub-sequences, input the kernel extreme learning machine (KELM) for prediction, and then integrate the output results to obtain the final prediction value.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Power Prediction Based On Particle Swarm Optimization-Extreme Learning Machine Model Combined With Adaboost Algorithm

Jiang

Cao

et al. 2021

IEEE Access

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As the proportion of wind power in the world's electricity generation increases, improving wind power prediction accuracy is vital for making full use of wind energy and ensuring the safe and stable operation of the power grid. Given the uncertainty and volatility of wind power and the weak generalization ability of the current wind power prediction models, we propose a wind power prediction model that combines Adaboost algorithm with extreme learning machine optimized by particle swarm optimization (PSO-ELM). First, particle swarm optimization is used to optimize the initial thresholds and input weights of the ELM to obtain the PSO-ELM basic prediction model. Then, combined with the Adaboost algorithm, a series of PSO-ELM weak predictors with input weights and thresholds optimized by PSO and containing different hidden layer nodes are composed. Finally, each weak predictor is weighted and fused into a strong prediction model of wind power, and the final prediction results are output. In this paper, the Adaboost-PSO-ELM model is verified by a wind turbine's measured data in Turkey. The prediction indicators are compared with the current wind power prediction methods including optimized neural networks and ensemble learning models. The results show that the Adaboost-PSO-ELM wind power prediction model has higher accuracy and better generalization ability.

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Wind Power Prediction Based on LSTM Networks and Nonparametric Kernel Density Estimation

Cited by 120 publications

References 35 publications

Wind Power Prediction Using Ensemble Learning-Based Models

Wind Power Prediction Using Ensemble Learning-Based Models

A state-of-the-art review on wind power deterministic prediction

Short-Term Wind Power Prediction Based On Particle Swarm Optimization-Extreme Learning Machine Model Combined With Adaboost Algorithm

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