Journal ArticleDOI
Dynamic Neural Network Based Very Short-Term Wind Speed Forecasting
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TLDR
The result shows that the proposed model outperforms the BPNN based on the metrics used, and the nonlinear autoregressive model with exogenous inputs (NARX) is proposed for wind speed forecast.Abstract:
In this paper, the nonlinear autoregressive model with exogenous inputs (NARX) is proposed for wind speed forecast. Forecasting wind speed is a challenging task in wind energy research domain which influences the dynamic control of wind turbine and for system scheduling. The aim of this study is to obtain suitable forecast model for wind speed with time series input variables such as wind direction, humidity, pressure and time. The meteorological data observed with 15 minute time intervals is used for the model and the performance is evaluated and compared with the back propagation neural network (BPNN). The result shows that the proposed model outperforms the BPNN based on the metrics used.read more
Citations
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Journal ArticleDOI
One-day-ahead probabilistic wind speed forecast based on optimized numerical weather prediction data
TL;DR: The results on test set show the correction considering inherent errors of numerical techniques can integrate the physical with statistical information effectively and enhance the forecast accuracy indeed.
Journal ArticleDOI
A novel combined model based on echo state network for multi-step ahead wind speed forecasting: A case study of NREL
TL;DR: A novel combined model for wind speed forecasting, which combined hybrid models based on decomposition method and optimization algorithm, and ESN (Echo state network) is initially applied to integrate all the results obtained by each hybrid model and achieve the ultimate forecasting results.
Journal ArticleDOI
Accurate prediction of different forecast horizons wind speed using a recursive radial basis function neural network
TL;DR: An accurate prediction of wind speed based on a Recursive Radial Basis Function Neural Network possessing the three inputs of wind direction, temperature and wind speed to improve modern power system protection, control and management is proposed.
Journal ArticleDOI
Weather Forecasting for Renewable Energy System: A Review
R. Meenal,D. Binu,K.C. Ramya,Prawin Angel Michael,K. Vinoth Kumar,Eswar Rajasekaran,B. Sangeetha +6 more
Journal ArticleDOI
Validation of Neural Network-based Fault Diagnosis for Multi-stack Fuel Cell Systems: Stack Voltage Deviation Detection☆
TL;DR: An algorithm for the detection of unexpected stack voltage deviations in an Solid Oxide Fuel Cells (SOFC)-based power system with multiple stacks is presented and its validation in a simulated online environment is validated.
References
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Proceedings ArticleDOI
Identification of Chemical Processes using Recurrent Networks
Hong-Te Su,Thomas J. McAvoy +1 more
TL;DR: A recurrent neural network is used as an alternative to feed-forward networks to identify the dynamic behavior of a biological wastewater treatment plant and an approach to deriving the learning algorithm for recurrent networks is discussed.
Journal ArticleDOI
A new hybrid iterative method for short‐term wind speed forecasting
TL;DR: In this article, a hybrid iterative forecast method (HIFM) for wind speed forecasting is presented which takes into account the interactions of temperature and wind speed, and a two-stage feature selection technique is also introduced.
Journal ArticleDOI
Improving Forecast Accuracy of Wind Speed Using Wavelet Transform and Neural Networks
TL;DR: A new hybrid forecast method composed of wavelet transform and neural network is proposed to forecast the wind speed more accurately and outperforms the compared model based on the metrics used and conclusions were drawn appropriately.
Journal ArticleDOI
Sunspot Forecasting by Using Chaotic Time-series Analysis and NARX Network
Chuanjin Jiang,Fugen Song +1 more
TL;DR: This work establishes sunspot prediction model with NARX network and shows that compared with BP Network and ARIMA Model, N ARX network can better predict the chaos.
Proceedings ArticleDOI
Short-term electric load forecasting using data mining technique
TL;DR: By measuring their MAPE, Holt-Winters was shown to have better performance in short-term load forecasting, and with embodiment of a load classification procedure, it could be possible to provide more accurate load data.