Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models

2016 ◽  
Vol 60 ◽  
pp. 960-981 ◽  
Author(s):  
Jianzhou Wang ◽  
Yiliao Song ◽  
Feng Liu ◽  
Ru Hou
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Speed Forecasting ◽  
Wind Power Integration ◽  
Forecasting Models

scholarly journals A Hybrid Model Based on Ensemble Empirical Mode Decomposition and Fruit Fly Optimization Algorithm for Wind Speed Forecasting

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Zongxi Qu ◽  
Kequan Zhang ◽  
Jianzhou Wang ◽  
Wenyu Zhang ◽  
Wennan Leng
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Ensemble Empirical Mode Decomposition ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Wind Speed Forecasting ◽  
Forecasting Models ◽  
Mode Decomposition

As a type of clean and renewable energy, the superiority of wind power has increasingly captured the world’s attention. Reliable and precise wind speed prediction is vital for wind power generation systems. Thus, a more effective and precise prediction model is essentially needed in the field of wind speed forecasting. Most previous forecasting models could adapt to various wind speed series data; however, these models ignored the importance of the data preprocessing and model parameter optimization. In view of its importance, a novel hybrid ensemble learning paradigm is proposed. In this model, the original wind speed data is firstly divided into a finite set of signal components by ensemble empirical mode decomposition, and then each signal is predicted by several artificial intelligence models with optimized parameters by using the fruit fly optimization algorithm and the final prediction values were obtained by reconstructing the refined series. To estimate the forecasting ability of the proposed model, 15 min wind speed data for wind farms in the coastal areas of China was performed to forecast as a case study. The empirical results show that the proposed hybrid model is superior to some existing traditional forecasting models regarding forecast performance.

Wind Speed Forecasting Based on FNN in Wind Farm

2014 ◽  
Vol 651-653 ◽  
pp. 1117-1122
Author(s):  
Zheng Ning Fu ◽  
Hong Wen Xie
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Power Systems ◽  
Wind Power ◽  
Power Plants ◽  
Fuzzy Neural Network ◽  
Negative Impact ◽  
Wind Speed Forecasting ◽  
Wind Power Plants ◽  
Fuzzy Neural

Wind speed forecasting plays a significant role to the operation of wind power plants and power systems. An accurate forecasting on wind power can effectively relieve or avoid the negative impact of wind power plants on power systems and enhance the competition of wind power plants in electric power market. Based on a fuzzy neural network (FNN), a method of wind speed forecasting is presented in this paper. By mining historical data as the learning stylebook, the fuzzy neural network (FNN) forecasts the wind speed. The simulation results show that this method can improve the accuracy of wind speed forecasting effectively.

scholarly journals Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1958 ◽  
Author(s):  
Lilin Cheng ◽  
Haixiang Zang ◽  
Tao Ding ◽  
Rong Sun ◽  
Miaomiao Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Recurrent Neural Network ◽  
Fuzzy Inference ◽  
Ensemble Model ◽  
Probabilistic Forecasting ◽  
Inference System ◽  
Wind Speed Forecasting ◽  
Wind Power Integration ◽  
Neuro Fuzzy

Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and stability of power system. In this study, we propose an ensemble model for probabilistic wind speed forecasting. It consists of wavelet threshold denoising (WTD), recurrent neural network (RNN) and adaptive neuro fuzzy inference system (ANFIS). Firstly, WTD smooths the wind speed series in order to better capture its variation trend. Secondly, RNNs with different architectures are trained on the denoising datasets, operating as submodels for point wind speed forecasting. Thirdly, ANFIS is innovatively established as the top layer of the entire ensemble model to compute the final point prediction result, in order to take full advantages of a limited number of deeplearningbased submodels. Lastly, variances are obtained from submodels and then prediction intervals of probabilistic forecasting can be calculated, where the variances inventively consist of modeling and forecasting uncertainties. The proposed ensemble model is established and verified on less than one-hour-ahead ultra-short-term wind speed forecasting. We compare it with other soft computing models. The results indicate the feasibility and superiority of the proposed model in both point and probabilistic wind speed forecasting.

scholarly journals Short-Term Wind Speed Forecasting Using the Data Processing Approach and the Support Vector Machine Model Optimized by the Improved Cuckoo Search Parameter Estimation Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Chen Wang ◽  
Jie Wu ◽  
Jianzhou Wang ◽  
Zhongjin Hu
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Wind Power ◽  
Parameter Optimization ◽  
Cuckoo Search ◽  
Support Vector ◽  
Optimization Approach ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Comparison Results

Power systems could be at risk when the power-grid collapse accident occurs. As a clean and renewable resource, wind energy plays an increasingly vital role in reducing air pollution and wind power generation becomes an important way to produce electrical power. Therefore, accurate wind power and wind speed forecasting are in need. In this research, a novel short-term wind speed forecasting portfolio has been proposed using the following three procedures: (I) data preprocessing: apart from the regular normalization preprocessing, the data are preprocessed through empirical model decomposition (EMD), which reduces the effect of noise on the wind speed data; (II) artificially intelligent parameter optimization introduction: the unknown parameters in the support vector machine (SVM) model are optimized by the cuckoo search (CS) algorithm; (III) parameter optimization approach modification: an improved parameter optimization approach, called the SDCS model, based on the CS algorithm and the steepest descent (SD) method is proposed. The comparison results show that the simple and effective portfolio EMD-SDCS-SVM produces promising predictions and has better performance than the individual forecasting components, with very small root mean squared errors and mean absolute percentage errors.

scholarly journals A Short-Term Ensemble Wind Speed Forecasting System for Wind Power Applications

2012 ◽  
Vol 51 (10) ◽  
pp. 1763-1774 ◽  
Author(s):  
Justin J. Traiteur ◽  
David J. Callicutt ◽  
Maxwell Smith ◽  
Somnath Baidya Roy
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Statistical Models ◽  
Bayesian Model Averaging ◽  
Numerical Models ◽  
Weather Prediction ◽  
Environmental Stability ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Forecasting System

AbstractThis study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 h ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model and persistence, autoregressive, and autoregressive moving-average models. The ensemble is calibrated against observations for a 6-month period (January–June 2006) at a potential wind-farm site in Illinois using the Bayesian model averaging technique. The forecasting system is evaluated against observations for the July 2006–December 2007 period at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble as well the time series models under all environmental stability conditions. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 min. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

Comparative models for multi-step ahead wind speed forecasting applied for expected wind turbine power output prediction

2021 ◽  
pp. 0309524X2110520
Author(s):  
Germaine Djuidje Kenmoé ◽  
Hervice Roméo Fogno Fotso ◽  
Claude Vidal Aloyem Kazé
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Wind Turbine ◽  
Arima Model ◽  
Physical Method ◽  
Computational Time ◽  
Dynamic Neural Networks ◽  
Wind Speed Forecasting ◽  
Forecasting Models ◽  
One Step

This paper investigates six of the most widely used wind speed forecasting models for a combination of statistical and physical methods for the purpose of Wind Turbine Power Generation (WTPG) prediction in Cameroon. Statistical method based on both single static and dynamic neural networks architectures and two hybrid neural networks architectures in comparison to ARIMA model are employed for multi-step ahead wind speed forecasting in two Datasets in Bapouh, Cameroon. The physical method is used to estimate 1 day ahead expected WTPG for each Dataset using the previous predicted wind speed from better forecasting models. The obtained results of multi-step ahead forecasting showed that the ARIMA and nonlinear autoregression with exogenous input neural network (NARXNN) models perform well the wind speed forecasting than other forecasting models in both Datasets. The better performances of ARIMA are achieved with one-step ahead and two-step ahead forecasting, while NARXNN is better with one-step ahead forecasting. But NARXNN models have more computational time than other models such as ARIMA models. Furthermore, the effectiveness of employed hybrid method for WTPG prediction is proven.

scholarly journals An Experimental Investigation of FNN Model for Wind Speed Forecasting Using EEMD and CS

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Jianzhou Wang ◽  
Haiyan Jiang ◽  
Bohui Han ◽  
Qingping Zhou
Keyword(s):  
Wind Speed ◽  
Power System ◽  
Wind Power ◽  
Wind Farm ◽  
Frequency Noise ◽  
Wind Speed Forecasting ◽  
System A ◽  
Wind Power System ◽  
And Performance ◽  
Using Data

With depletion of traditional energy and increasing environmental problems, wind energy, as an alternative renewable energy, has drawn more and more attention internationally. Meanwhile, wind is plentiful, clean, and environmentally friendly; moreover, its speed is a very important piece of information needed in the operations and planning of the wind power system. Therefore, choosing an effective forecasting model with good performance plays a quite significant role in wind power system. A hybrid CS-EEMD-FNN model is firstly proposed in this paper for multistep ahead prediction of wind speed, in which EEMD is employed as a data-cleaning method that aims to remove the high frequency noise embedded in the wind speed series. CS optimization algorithm is used to select the best parameters in the FNN model. In order to evaluate the effectiveness and performance of the proposed hybrid model, three other short-term wind speed forecasting models, namely, FNN model, EEMD-FNN model, and CS-FNN model, are carried out to forecast wind speed using data measured at a typical site in Shandong wind farm, China, over three seasons in 2011. Experimental results demonstrate that the developed hybrid CS-EEMD-FNN model outperforms other models with more accuracy, which is suitable to wind speed forecasting in this area.

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