Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform

Taşçıkaraoğlu, Akın; Sanandaji, Borhan M.; Poolla, Kameshwar; Varaiya, Pravin

doi:10.1016/j.apenergy.2015.12.082

Cited by 153 publications

(35 citation statements)

References 39 publications

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“…Zhou et al [48] proposed a spatial and temporal correlation model and it was found that this model could improve ultra-short-term wind power forecasting accuracy. Tascikaraoglu et al [49] proposed a novel method, which first utilized a Wavelet Transform (WT) method to decompose the wind speed data into more stationary components and then used a spatio-temporal model on each of the subseries to incorporate both the temporal and spatial information for wind speed forecasting. Ye et al [50] analyzed uncertainty and dependence in wind power output, and employed a physical spatio-temporal correlation model.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA

Lin

Sun

et al. 2018

Energies

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Abstract:Wind power time series data always exhibits nonlinear and non-stationary features, making it very difficult to accurately predict. In this paper, a novel hybrid wind power time series prediction model, based on ensemble empirical mode decomposition-permutation entropy (EEMD-PE), the least squares support vector machine model (LSSVM), and gravitational search algorithm (GSA), is proposed to improve accuracy of ultra-short-term wind power forecasting. To process the data, original wind power series were decomposed by EEMD-PE techniques into a number of subsequences with obvious complexity differences. Then, a new heuristic GSA algorithm was utilized to optimize the parameters of the LSSVM. The optimized model was developed for wind power forecasting and improved regression prediction accuracy. The proposed model was validated with practical wind power generation data from the Hebei province, China. A comprehensive error metric analysis was carried out to compare the performance of our method with other approaches. The results showed that the proposed model enhanced forecasting performance compared to other benchmark models.

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Section: Introductionmentioning

confidence: 99%

A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA

Lin

Sun

et al. 2018

Energies

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“…Tascikaraoglu et al [251] used WT method to efficiently capture wind speed data stationary components, which were subsequently translated into more accurate wind forecast via compressive spatio-temporal wind speed forecasting (CST-WSF) model. In addition, Wang and Hu [252] developed a multiplemodel framework comprising of empirical wavelet transform (EWT), ARIMA, LSSVM, ELM, SVM and Gaussian Process Regression (GPR) models for forecast reliability and robustness enhancement.…”

Section: Referencesmentioning

confidence: 99%

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Theo

Lim

et al. 2017

Renewable and Sustainable Energy Reviews

242

105

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“…Spatial correlation models [13] Obtaining a satisfactory wind speed forecasting by vast quantities of information that need be considered and collected [13].…”

Section: Spatial Correlation Modelsmentioning

confidence: 99%

A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting

Yao

Wang

2018

Sustainability

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Abstract:In the last few years, researchers have paid increasing attention to improving the accuracy of wind speed forecasting because of its vital impact on power dispatching and grid security. However, it is difficult to achieve a good forecasting performance due to the randomness and intermittency characteristics of wind speed time series. Current forecasting models based on neural network theory could adapt to various types of time series data; however, these models ignore the importance of data pre-processing and model parameter optimization, which leads to poor forecasting accuracy. In this paper, a new hybrid model is developed for short-term multi-step wind speed forecasting, which includes four modules: (1) the data pre-processing module; (2) the optimization module; (3) the hybrid nonlinear forecasting module and (4) the evaluation module. In order to estimate the forecasting ability of the proposed hybrid model, 10 min wind speed data were applied in this paper as a case study. The experimental results in six real forecasting cases indicate that the proposed hybrid model can provide not only accurate but also stable performance in terms of multi-step wind speed forecasting can be considered an effective tool in planning and dispatching for smart grids.

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Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform

Cited by 153 publications

References 39 publications

A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA

A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting

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