A Fuzzy Adaptive Probabilistic Wind Power Prediction Framework Using Diffusion Kernel Density Estimators

Khorramdel, Benyamin; Chung, C. Y.; Safari, Nima; Price, G. C. D.

doi:10.1109/tpwrs.2018.2848207

Cited by 96 publications

(35 citation statements)

References 39 publications

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“…In recent years, heuristic technologies including support vector machine (SVM), artificial neural network (ANN), ant colony algorithm, and fuzzy logic algorithm are widely involved to wind power prediction [12]- [17]. Among these methods, SVM can provide prediction result based on limited set of information, it is useful when the parameters are optimized by other intelligent methods.…”

Section: Introductionmentioning

confidence: 99%

Clustered Hybrid Wind Power Prediction Model Based on ARMA, PSO-SVM, and Clustering Methods

Wang²,

2020

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Wind power prediction is the key technology to the safe dispatch and stable operation of power system with large-scale integration of wind power. In this work, based on the historical data of wind power, wind speed and temperature, the autoregressive moving average (ARMA) prediction model and the support vector machine (SVM) prediction model are established, particle swarm optimization (PSO) algorithm is involved for parameter optimization of SVM model. Furthermore, a hybrid PSO-SVM-ARMA prediction model based on ARMA and PSO-SVM model is illustrated for wind power prediction, and the covariance minimization method and PSO are employed to find the optimal weights. Moreover, with the basis of clustering theory, time series are clustered to examine the effective dataset for wind power prediction, and a clustered hybrid PSO-SVM-ARMA (C-PSO-SVM-ARMA) wind power prediction model is prospectively proposed. In case study, different prediction models are carried out and the prediction performance is examined based on different evaluation indices, the C-PSO-SVM-ARMA model shows better performance for wind power prediction with computational efficiency and satisfying precision.INDEX TERMS Autoregressive moving average (ARMA) model, clustered hybrid wind power prediction model, clustering method, particle swarm optimization (PSO), support vector machine (SVM).

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Section: Introductionmentioning

confidence: 99%

Clustered Hybrid Wind Power Prediction Model Based on ARMA, PSO-SVM, and Clustering Methods

Wang²,

2020

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“…As an intermediate entity between the end users and the power system operator, an aggregator (Agg) has been proposed to manage the energy of local RESs and DERs [2], and will play an important role in future smart grids. However, the power outputs of RESs have uncertain characteristics [3], [4], causing challenges in the energy management of the Agg. Employing the flexibility of electric vehicles (EVs) is widely considered as an economical and efficient solution to the problem [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Multi-time Scale Energy Management Strategy of Aggregator Characterized by Photovoltaic Generation and Electric Vehicles

Zhou

Yang

et al. 2020

Journal of Modern Power Systems and Clean Energy

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The increasing number of photovoltaic (PV) generation and electric vehicles (EVs) on the load side has necessitated an aggregator (Agg) in power system operation. In this paper, an Agg is used to manage the energy profiles of PV generation and EVs. However, the daily management of the Agg is challenged by uncertain PV fluctuations. To address this problem, a robust multi-time scale energy management strategy for the Agg is proposed. In a day-ahead phase, robust optimization is developed to determine the power schedule. In a real-time phase, a rolling horizon-based convex optimization model is established to track the day-ahead power schedule based on the flexibilities of the EVs. A case study indicates a good scheduling performance under an uncertain PV output. Through the convexification, the solving efficiency of the real-time operation model is improved, and the overcharging and over-discharging problems of EVs can be suppressed to a certain extent. Moreover, the power deviation between day-ahead and real-time scheduling is controllable when the EV dispatching capacity is sufficient. The strategy can ensure the flexibility of the Agg for real-time operation.

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“…Firstly, the data is preprocessed to remove the redundant items from the original data, and a new external verification index related to wind speed in numerical weather prediction is proposed. A framework based on the bandwidth selection concept was proposed for new flexible kernel density estimation in [17]. This method uses diffusion-based nuclear density estimator to achieve high-quality interval prediction of non-stationary wind power time series.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine

Li²,

Lin

et al. 2019

Sustainability

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Renewable energy technologies are essential contributors to sustainable energy including renewable energy sources. Wind energy is one of the important renewable energy resources. Therefore, efficient and consistent utilization of wind energy has been an important issue. The wind speed has the characteristics of intermittence and instability. If the wind power is directly connected to the grid, it will impact the voltage and frequency of the power system. Short-term wind power prediction can reduce the impact of wind power on the power grid and the stability of power system operation is guaranteed. In this study, the improved chicken swarm algorithm optimization support vector machine (ICSO-SVM) model is proposed to predict the wind power. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum when solving high-dimensional problems due to its own characteristics. So the CSO algorithm is improved and the ICSO algorithm is developed. In order to verify the validity of the ICSO-SVM model, the following work has been done. (1) The particle swarm optimization (PSO), ICSO, CSO and differential evolution algorithm (DE) are tested respectively by four standard testing functions, and the results are compared. (2) The ICSO-SVM and CSO-SVM models are tested respectively by two sets of wind power data. This study draws the following conclusions: (1) the PSO, CSO, DE and ICSO algorithms are tested by the four standard test functions and the test data are analyzed. By comparing it with the other three optimization algorithms, the ICSO algorithm has the best convergence effect. (2) The number of training samples has an obvious impact on the prediction results. The average relative error percentage and root mean square error (RMSE) values of the ICSO model are smaller than those of CSO-SVM model. Therefore, the ICSO-SVM model can efficiently provide credible short-term predictions for wind power forecasting.

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A Fuzzy Adaptive Probabilistic Wind Power Prediction Framework Using Diffusion Kernel Density Estimators

Cited by 96 publications

References 39 publications

Clustered Hybrid Wind Power Prediction Model Based on ARMA, PSO-SVM, and Clustering Methods

Clustered Hybrid Wind Power Prediction Model Based on ARMA, PSO-SVM, and Clustering Methods

Multi-time Scale Energy Management Strategy of Aggregator Characterized by Photovoltaic Generation and Electric Vehicles

Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine

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