One-day-ahead probabilistic wind speed forecast based on optimized numerical weather prediction data

A novel combined model based on echo state network for multi-step ahead wind speed forecasting: A case study of NREL

Environmental considerations have prompted the use of renewable energy resources worldwide for reduction of greenhouse gas emissions. An accurate prediction of wind speed plays a major role in environmental planning, energy system balancing, wind farm operation and control, power system planning, scheduling, storage capacity optimization, and enhancing system reliability. This paper proposes an accurate prediction of wind speed based ona Recursive Radial Basis Function Neural Network (RRBFNN) possessing the three inputs of wind direction, temperature and wind speed to improve modern power system protection, control and management. Simulation results confirm that the proposed model improves the wind speed prediction accuracy with least error when compared with other existing prediction models.

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Accurate prediction of different forecast horizons wind speed using a recursive radial basis function neural network

Weather Forecasting for Renewable Energy System: A Review

Validation of Neural Network-based Fault Diagnosis for Multi-stack Fuel Cell Systems: Stack Voltage Deviation Detection☆

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.

Babu. N Ramesh

Papers

Dynamic Neural Network Based Very Short-Term Wind Speed Forecasting