A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction

Yu, Jie; Chen, Kuilin; Mori, Junichi; Rashid, Mudassir

doi:10.1016/j.energy.2013.09.013

Cited by 56 publications

(20 citation statements)

References 41 publications

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“…Different hybrid wind speed prediction models have been proposed in the literature in order to benefit from the unique capability of single models [27][28][29][30][31][32][33][34][35][36][37]. Salcedo-Sanz et al [27] proposes the hybridization of the fifth-generation mesoscale physical forecasting model (MM5) with neural networks for shortterm wind speed prediction of a wind park located at Albacete in Spain.…”

Section: Related Workmentioning

confidence: 99%

“…The results of two cases show that the forecasting precisions of both hybrid ARIMA-ANN and ARIMAKalman models are higher than those of single models. Yu et al [32] combine Gaussian Mixture Copula Model (GMCM) and Gaussian Process Regression (GPR) as a wind speed forecasting method. The results show that GMCM-GPR has a higher prediction accuracy than that of GMCM-ARIMA and GMCM-SVR.…”

Section: Related Workmentioning

confidence: 99%

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Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Kang

Tan

Yuan

et al. 2017

Advances in Meteorology

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Hybrid Ensemble Empirical Mode Decomposition (EEMD) and Least Square Support Vector Machine (LSSVM) is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to sum the predicted values of every subseries as the final wind speed prediction. The performance of hybrid model is evaluated based on six metrics. Compared with LSSVM, Back Propagation Neural Networks (BP), Auto-Regressive Integrated Moving Average (ARIMA), combination of Empirical Mode Decomposition (EMD) with LSSVM, and hybrid EEMD with ARIMA models, the wind speed forecasting results show that the proposed hybrid model outperforms these models in terms of six metrics. Furthermore, the scatter diagrams of predicted versus actual wind speed and histograms of prediction errors are presented to verify the superiority of the hybrid model in short-term wind speed prediction.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Kang

Tan

Yuan

et al. 2017

Advances in Meteorology

View full text Add to dashboard Cite

show abstract

“…Figure 3e,f illustrate the context of Table 2 and they also show that the proposed model has a good performance for 70%, 80% and 90% interval forecasts. [41]. ELM is a single hidden-layer feedforward neural network, which randomly chooses the input weights and analytically determines the output weights [42].…”

Section: Experiments Imentioning

confidence: 99%

“…In this paper, BPNN utilizes historical data to train its parameters and directly obtains the upper bounds and lower bounds. [41]. ELM is a single hidden-layer feedforward neural network, which randomly chooses the input weights and analytically determines the output weights [42].…”

Section: Experiments Imentioning

confidence: 99%

Daily Average Wind Power Interval Forecasts Based on an Optimal Adaptive-Network-Based Fuzzy Inference System and Singular Spectrum Analysis

et al. 2016

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Abstract:Wind energy has increasingly played a vital role in mitigating conventional resource shortages. Nevertheless, the stochastic nature of wind poses a great challenge when attempting to find an accurate forecasting model for wind power. Therefore, precise wind power forecasts are of primary importance to solve operational, planning and economic problems in the growing wind power scenario. Previous research has focused efforts on the deterministic forecast of wind power values, but less attention has been paid to providing information about wind energy. Based on an optimal Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Singular Spectrum Analysis (SSA), this paper develops a hybrid uncertainty forecasting model, IFASF (Interval Forecast-ANFIS-SSA-Firefly Alogorithm), to obtain the upper and lower bounds of daily average wind power, which is beneficial for the practical operation of both the grid company and independent power producers. To strengthen the practical ability of this developed model, this paper presents a comparison between IFASF and other benchmarks, which provides a general reference for this aspect for statistical or artificially intelligent interval forecast methods. The comparison results show that the developed model outperforms eight benchmarks and has a satisfactory forecasting effectiveness in three different wind farms with two time horizons.

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“…Other examples can be found in the following references [150,[227][228][229][230][231][232][233][234][235][236][237]]. …”

Section: Gaussian Process Regressionmentioning

confidence: 99%

Multi-Sensor Condition Monitoring of Bearings Using Support Vector Machines

Kan¹

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Keywords AbstractBearings are one of the most commonly-used and crucial components in the majority of rotating machines in industrial applications. Failure of this component is common and can lead to machine breakdown. If it is not detected in time, bearing faults can even lead to catastrophic failures. An effective diagnostic system provides early warning and fault identification such that any machine breakdown can be avoided. Condition monitoring can benefit plant operators resulting in shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning.However, machines and associated systems today are increasingly complex, while diagnostic techniques have been mainly applied to relatively simple rotating machines. Therefore, there is a need for research on condition monitoring techniques that can be applied to complex systems in variable operating conditions. This thesis presents a study on bearing condition monitoring under variable operating condition using Support Vector Machines. Data collected from multiple sensors including accelerometers, acoustic emission sensors and tachometers are used for the studies presented in this thesis. Support Vector Machines are an efficient method for classification and prediction, and shows outstanding performance in modelling and generalization.The study begins with collecting bearing condition monitoring data. Vibration analysis is a well-established technique, while acoustic emission is gaining an increasing amount of attention especially on its capability to detect bearing incipient fault such as subsurface cracks. Existing studies so far have not demonstrated AE's diagnostic capabilities using naturally-damaged bearing, and only a very few studies adopt envelope and cyclostationary analysis on bearing fault diagnosis with this sensor technology. Due to the second-order cyclostationary nature of bearing signals and the success of envelope analysis in analysing bearing signals, the diagnostic and prognostic studies presented in this thesis make use of both envelope analysis and cyclostationary analysis to analyse vibration and acoustic emission bearing signals that will be collected from bearing diagnostics and run-to-failure prognostic experiments through a bearing diagnostic test-rig and a bearing prognostic test-rig.Using the collected data, the effectiveness and diagnostic capability of cyclostationary analysis will first be assessed using feature analysis with support vector machine. A diagnostic study is then conducted, investigating the diagnostic effectiveness of acoustic emission and its superiority to vibration, especially in the detection of bearing subsurface cracks. Through comparison of AE with vibration signals, this study confirms AE's superiority in detecting subsurface cracks in bearings and the absence of vibration symptoms of subsurface cracks.This diagnostic study has also shown the crucial ability of the cyclic logarithmic envelope iii spectrum in separating different cyc...

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A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction

Cited by 56 publications

References 41 publications

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Daily Average Wind Power Interval Forecasts Based on an Optimal Adaptive-Network-Based Fuzzy Inference System and Singular Spectrum Analysis

Multi-Sensor Condition Monitoring of Bearings Using Support Vector Machines

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