Forecasting aggregated wind power production of multiple wind farms using hybrid wavelet-PSO-NNs

Mandal, Paras; Zareipour, Hamidreza; Rosehart, William

doi:10.1002/er.3171

Cited by 56 publications

(35 citation statements)

References 55 publications

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“…Similar to Table 1, the results reported for the nine comparative methods in Table 2 are directly quoted from Ref. 20. Table 2 shows that the proposed wind power forecast strategy has the best average results among all methods in terms of all three error criteria.…”

Section: Alberta Test Casementioning

confidence: 72%

“…Another error index employed in this paper to measure wind power forecast accuracy is well-known Mean Absolute Percentage Error (MAPE) criterion, which is as follows: (20) However, MAPE criterion cannot be directly used to measure wind power forecast error as actual wind power output of a wind farm, i.e. S ACT(t) , may become zero in some hours.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…For this test case, one-hour-ahead wind power forecasting is considered. 20 A key issue for illustrating the effectiveness of a forecast method is comparing its results with the results obtained from the other methods and published literature figures. However, for the sake of a fair comparison, the same error criteria should be used for all methods; otherwise, the comparison will not be informative.…”

Section: Alberta Test Casementioning

confidence: 99%

See 2 more Smart Citations

Short-term wind power prediction based on Hybrid Neural Network and chaotic shark smell optimization

Abedinia

Amjady

2015

Int. J. of Precis. Eng. and Manuf.-Green Tech.

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By the quick growth of wind power generation in the world, this clean energy becomes an important green electrical source in many countries. However, volatile and non-dispatchable nature of this energy source motivates researchers to find accurate and robust methods to predict its future values. Because of nonlinear and complex behaviors of this signal, more efficient wind power forecast methods are still demanded. In this paper, a new forecasting engine based on Neural Network (NN) and a novel Chaotic Shark Smell Optimization (CSSO) algorithm is proposed. Choosing optimal number of nodes for the hidden layer can enhance the efficiency of the NN's training performance. Accordingly, a new meta-heuristic algorithm is presented in this paper, which is based on shark abilities in nature, for optimizing the number of hidden nodes pertaining to the NN. Effectiveness of the proposed forecasting strategy is tested on two real-world case studies for predicting wind power. The obtained results demonstrate the capability of the proposed technique to cope with the variability and intermittency of wind power time series for providing accurate predictions of its future values.

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Section: Alberta Test Casementioning

confidence: 72%

Section: Numerical Resultsmentioning

confidence: 99%

Section: Alberta Test Casementioning

confidence: 99%

See 1 more Smart Citation

Short-term wind power prediction based on Hybrid Neural Network and chaotic shark smell optimization

Abedinia

Amjady

2015

Int. J. of Precis. Eng. and Manuf.-Green Tech.

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“…Thus, the multi-scale decomposition of the original wind speed, which is indispensable in improving the prediction accuracy, is widely used. Wavelet transform (WT) is used to eliminate the irregular fluctuation of the weed speed [27,28]. Liu [27] described a wind speed forecasting method based on spectral clustering (SC), echo state networks (ESNs) and WT which was used to decompose the wind speed into multiple series to eliminate irregular fluctuation.…”

mentioning

confidence: 99%

“…Liu [27] described a wind speed forecasting method based on spectral clustering (SC), echo state networks (ESNs) and WT which was used to decompose the wind speed into multiple series to eliminate irregular fluctuation. Mandal [28] described a hybrid intelligent algorithm that used a data preprocessing model based on WT and a soft computing model (SCM) based on neural network (NN). WT was applied to decompose the original wind speed data.…”

mentioning

confidence: 99%

Wind Speed Forecasting Based on FEEMD and LSSVM Optimized by the Bat Algorithm

2015

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Abstract:Affected by various environmental factors, wind speed presents high fluctuation, nonlinear and non-stationary characteristics. To evaluate wind energy properly and efficiently, this paper proposes a modified fast ensemble empirical model decomposition (FEEMD)-bat algorithm (BA)-least support vector machines (LSSVM) (FEEMD-BA-LSSVM) model combined with input selected by deep quantitative analysis. The original wind speed series are first decomposed into a limited number of intrinsic mode functions (IMFs) with one residual series. Then a LSSVM is built to forecast these sub-series. In order to select input from environment variables, Cointegration and Granger causality tests are proposed to check the influence of temperature with different leading lengths. Partial correlation is applied to analyze the inner relationships between the historical speeds thus to select the LSSVM input. The parameters in LSSVM are fine-tuned by BA to ensure the generalization of LSSVM. The forecasting results suggest the hybrid approach outperforms the compared models.

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Power system observability enhancement for parallel restoration of subsystems considering renewable energy resources

Khodabakhshian

Esmaili

Hooshmand

et al. 2019

Int Trans Electr Energ Syst

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Summary The observability of power systems during the parallel restoration of subsystems is one of the most important issues for system operators to accomplish the restoration task as quick as possible. Thus, this article proposes a coordinated optimal plan to solve the observability and sectionalizing problems by determining the locations of phasor measurement units (PMUs) and subsystems. Also, the impact of renewable energy resources on power system sectionalizing and the reliability value of power generation are taken into account in the proposed model. The objective functions that are considered in the optimization problem are the cost of wide‐area measurement system (WAMS), the worst observability index among all subsystems and the lowest value of quality among all subsystems based on the reliability of subsystems. Since there are three contradictory objective functions, a multi‐objective problem (MOP) is proposed as a mixed‐integer nonlinear problem (MINLP). The Pareto curve of the proposed MOP is extracted by using a particle swarm optimization (PSO) algorithm. Two standard power grids are considered to validate the suggested technique. The outcomes of simulations confirm that the observability value of all sections is enhanced during the parallel restoration of the system. Also, the results show that the quality of subsystems in the presence of renewable energy resources is enhanced.

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Forecasting aggregated wind power production of multiple wind farms using hybrid wavelet-PSO-NNs

Cited by 56 publications

References 55 publications

Short-term wind power prediction based on Hybrid Neural Network and chaotic shark smell optimization

Short-term wind power prediction based on Hybrid Neural Network and chaotic shark smell optimization

Wind Speed Forecasting Based on FEEMD and LSSVM Optimized by the Bat Algorithm

Power system observability enhancement for parallel restoration of subsystems considering renewable energy resources

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