Short-term wind speed syntheses correcting forecasting model and its application

Nan, Xiaoqiang; Li, Qunzhan; Qiu, Daqiang; Zhao, Yuanzhe; Guo, Xinxin

doi:10.1016/j.ijepes.2013.01.014

Cited by 21 publications

(12 citation statements)

References 7 publications

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“…The main idea of EOF regression is that, decomposed into orthogonal principal component, EOF model completes regression with a small amount of principal components and changes into orthogonal variables. There is no large error on regression equation [48].…”

Section: Eof Regressionmentioning

confidence: 99%

“…The flow chart of Elman-ANN prediction model is shown in Figure 2 [48]. T r a i n i n g p r o c e s s…”

Section: Wind Speed Elman Artificial Neural Network Predictionmentioning

confidence: 99%

“…It is similar to principal component analysis but different from meanings and analysis methods [48]. The basic idea is to decompose data that contain variables in time observation field.…”

Section: Eof Decomposition Eof Is Also Called Empirical Orthogonal Dmentioning

confidence: 99%

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Short-Term Wind Speed Hybrid Forecasting Model Based on Bias Correcting Study and Its Application

Niu

Sun

et al. 2015

Mathematical Problems in Engineering

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The accuracy of wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. In particular, reliable short-term wind speed forecasting can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, due to the strong stochastic nature and dynamic uncertainty of wind speed, the forecasting of wind speed data using different patterns is difficult. This paper proposes a novel combination bias correcting forecasting method, which includes the combination forecasting method and forecasting bias correcting model. The forecasting result shows that the combination bias correcting forecasting method can more accurately forecast the trend of wind speed and has a good robustness.

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Section: Eof Regressionmentioning

confidence: 99%

“…The flow chart of Elman-ANN prediction model is shown in Figure 2 [48]. T r a i n i n g p r o c e s s…”

Section: Wind Speed Elman Artificial Neural Network Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Short-Term Wind Speed Hybrid Forecasting Model Based on Bias Correcting Study and Its Application

Niu

Sun

et al. 2015

Mathematical Problems in Engineering

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“…The built hybrid model could forecast wind speed with a higher accuracy than ARIMA or ANN models separately according to the calculation of the mean error (ME), the mean square error (MSE), and the mean absolute error (MAE). Nan et al [15] established a forecasting model composed of time series and back propagation neural network (BPNN) prediction model. As for the time series analysis, bias correction method on empirical orthogonal function (EOF) was proposed.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Model Based on Data Preprocessing and an Intelligent Optimization Algorithm for Electrical Power System Forecasting

Jiang

Liu

2015

Mathematical Problems in Engineering

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The establishment of electrical power system cannot only benefit the reasonable distribution and management in energy resources, but also satisfy the increasing demand for electricity. The electrical power system construction is often a pivotal part in the national and regional economic development plan. This paper constructs a hybrid model, known as the E-MFA-BP model, that can forecast indices in the electrical power system, including wind speed, electrical load, and electricity price. Firstly, the ensemble empirical mode decomposition can be applied to eliminate the noise of original time series data. After data preprocessing, the back propagation neural network model is applied to carry out the forecasting. Owing to the instability of its structure, the modified firefly algorithm is employed to optimize the weight and threshold values of back propagation to obtain a hybrid model with higher forecasting quality. Three experiments are carried out to verify the effectiveness of the model. Through comparison with other traditional well-known forecasting models, and models optimized by other optimization algorithms, the experimental results demonstrate that the hybrid model has the best forecasting performance.

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“…More specifically, the former rely on the physics of the lower atmospheric boundary layer to produce wind flow information, and are thus characterized by high computational complexity [9]. The latter are based on historical wind speed data and employ either statistics-based methods (e.g., time series [10], Kalman filtering [11], Markov chain models [12], and Bayesian methods [13]) or artificial intelligence-based (e.g., artificial neural networks (ANNs) [14], fuzzy systems [15], and support vector machines [16]), or hybrid approaches that combine both techniques to produce wind speed forecasts [17][18][19]. Depending on the intended application, the time scale of wind forecasting can be divided into ultra-short-term (minutes to 1 h ahead), short-term (1 h to several hours ahead), medium-term (several hours to one week ahead), and long-term (one week to more than six months ahead), with respect to the time horizon of the forecasts [20,21].In general, microgrids can operate either in grid-connected or islanded mode (e.g., in autonomous applications or due to faults in the upstream network), incorporating appropriate control strategies to ensure that the techno-economic requirements for an enhanced energy utilization rate and reduced operating costs are met [22,23].…”

mentioning

confidence: 99%

Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting

et al. 2017

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The inherently intermittent and highly variable nature of wind necessitates the use of wind power forecasting tools in order to facilitate the integration of wind turbines in microgrids, among others. In this direction, the present paper describes the development of a short-term wind power forecasting model based on artificial neural network (ANN) clustering, which uses statistical feature parameters in the input vector, as well as an enhanced version of this approach that adjusts the ANN output with the probability of lower misclassification (PLM) method. Moreover, it employs the Monte Carlo simulation to represent the stochastic variation of wind power production and assess the impact of energy management decisions in a residential wind-battery microgrid using the proposed wind power forecasting models. The results indicate that there are significant benefits for the microgrid when compared to the naïve approach that is used for benchmarking purposes, while the PLM adjustment method provides further improvements in terms of forecasting accuracy.Appl. Sci. 2017, 7, 1142 2 of 15 buy/sell electricity from/to the grid, forecasting the power output from microRES within a certain time frame enables more informed decisions in order to maximize the profit for the prosumer [7].A typical approach to estimate the power output of a wind turbine consists in matching forecasted wind speed values on the wind turbine power curve. The relevant literature in the field classifies the wind speed forecasting methods into two broad categories, namely physics-based numerical weather prediction models and data-driven approaches [8]. More specifically, the former rely on the physics of the lower atmospheric boundary layer to produce wind flow information, and are thus characterized by high computational complexity [9]. The latter are based on historical wind speed data and employ either statistics-based methods (e.g., time series [10], Kalman filtering [11], Markov chain models [12], and Bayesian methods [13]) or artificial intelligence-based (e.g., artificial neural networks (ANNs) [14], fuzzy systems [15], and support vector machines [16]), or hybrid approaches that combine both techniques to produce wind speed forecasts [17][18][19]. Depending on the intended application, the time scale of wind forecasting can be divided into ultra-short-term (minutes to 1 h ahead), short-term (1 h to several hours ahead), medium-term (several hours to one week ahead), and long-term (one week to more than six months ahead), with respect to the time horizon of the forecasts [20,21].In general, microgrids can operate either in grid-connected or islanded mode (e.g., in autonomous applications or due to faults in the upstream network), incorporating appropriate control strategies to ensure that the techno-economic requirements for an enhanced energy utilization rate and reduced operating costs are met [22,23]. Given that wind energy is one of the most attractive renewable energy sources (RES) in terms of efficiency and cost-competitiveness, yet it is in...

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Short-term wind speed syntheses correcting forecasting model and its application

Cited by 21 publications

References 7 publications

Short-Term Wind Speed Hybrid Forecasting Model Based on Bias Correcting Study and Its Application

Short-Term Wind Speed Hybrid Forecasting Model Based on Bias Correcting Study and Its Application

A New Hybrid Model Based on Data Preprocessing and an Intelligent Optimization Algorithm for Electrical Power System Forecasting

Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting

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