Very short-term wind forecasting for Tasmanian power generation Export

@article{citeulike:788782, abstract = {This paper describes very short-term wind prediction for power generation, utilizing a case study from Tasmania, Australia. Windpower presently is the fastest growing power generation sector in the world. However, windpower is intermittent. To be able to trade efficiently, make the best use of transmission line capability, and address concerns with system frequency in a re-regulated system, accurate very short-term forecasts are essential. The research introduces a novel approach-the application of an adaptive neuro-fuzzy inference system to forecasting a wind time series. Over the very short-term forecast interval, both windspeed and wind direction are important parameters. To be able to be gain the most from a forecast on this time scale, the turbines must be directed toward on oncoming wind. For this reason, this paper forecasts wind vectors, rather than windspeed or power output.}, author = {Potter, C. W. and Negnevitsky, M.}, citeulike-article-id = {788782}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1626404}, journal = {Power Systems, IEEE Transactions on}, keywords = {energy, load-forecasting, wind-power}, number = {2}, pages = {965--972}, posted-at = {2006-08-07 14:53:18}, priority = {2}, title = {Very short-term wind forecasting for Tasmanian power generation}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1626404}, volume = {21}, year = {2006} }

TY - JOUR ID - citeulike:788782 L3 - citeulike-article-id:788782 N2 - This paper describes very short-term wind prediction for power generation, utilizing a case study from Tasmania, Australia. Windpower presently is the fastest growing power generation sector in the world. However, windpower is intermittent. To be able to trade efficiently, make the best use of transmission line capability, and address concerns with system frequency in a re-regulated system, accurate very short-term forecasts are essential. The research introduces a novel approach-the application of an adaptive neuro-fuzzy inference system to forecasting a wind time series. Over the very short-term forecast interval, both windspeed and wind direction are important parameters. To be able to be gain the most from a forecast on this time scale, the turbines must be directed toward on oncoming wind. For this reason, this paper forecasts wind vectors, rather than windspeed or power output. IS - 2 JF - Power Systems, IEEE Transactions on EP - 972 TI - Very short-term wind forecasting for Tasmanian power generation VL - 21 SP - 965 KW - energy KW - load-forecasting KW - wind-power AU - Potter, CW AU - Negnevitsky, M PY - 2006/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1626404 ER -