Short-Term Wind Speed Forecasting of Lelystad Wind Farm by Using ANN Algorithms
- 1 March 2018
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-4
Abstract
The installation of wind energy based electricity systems is growing at a very fast pace all over the world because of the increased urge of using renewable energy resources and environmental concerns regarding electricity generation. Forecasting wind speed is found to be critical for wind energy systems since it greatly influences its large-scale integration. The intermittent nature of wind speed leads to further problems in its large-scale integration in the power systems. Wind speed forecasting is essential to operate wind energy based power systems in an efficient and secure way. In this paper, different ANN algorithms have been applied to forecast short-term wind speed of Lelystad Wind Farm, Nederland using MATLAB R1 \pmb4a. The data used in the forecasting are hourly historical data of the wind direction & wind speed. The simulation results have shown accurate one hour ahead forecasts with small error in wind speed forecasting.Keywords
This publication has 12 references indexed in Scilit:
- Short-term load forecasting of Toronto Canada by using different ANN algorithms2016
- One hour ahead price forecast of Ontario electricity market by using ANN2015
- Backpropagation and Levenberg-Marquardt Algorithm for Training Finite Element Neural Network2012
- A new strategy for wind speed forecasting using hybrid intelligent models2012
- AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural NetworkIEEE Transactions on Sustainable Energy, 2012
- Machine Learning Applications for Load, Price and Wind Power Prediction in Power Systems2009
- Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networksRenewable Energy, 2009
- A Simplified Wind Power Generation Model for Reliability EvaluationIEEE Transactions on Energy Conversion, 2006
- Very Short-Term Wind Forecasting for Tasmanian Power GenerationIEEE Transactions on Power Systems, 2006
- One-hour-ahead wind speed prediction using a Bayesian methodology2006