کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8071326 1521393 2018 26 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method
ترجمه فارسی عنوان
پیش بینی احتمال احتمالی کوتاه مدت منابع انرژی باد با استفاده از روش تقویت ارتقاء یافته
کلمات کلیدی
پیش بینی احتمالی قدرت باد، پیش بینی کوتاه مدت، روش گروهی، گروه زمانی، گروه فضایی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی (عمومی)
چکیده انگلیسی
Unlike other traditional energy resources, wind power outputs depend on natural wind resources that vary over space and time. Accurate wind power forecasting can reduce the burden of balancing energy equilibrium in electrical power systems. In this paper, we propose the short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method. The enhanced ensemble forecasting methods are grouped into two main categories: temporal ensemble and spatial ensemble forecasting. The temporal ensemble forecasting is implemented by autoregressive integrated moving average with explanatory variable model, polynomial regression with time-series data, and analog ensemble for a probabilistic approach. The spatial ensemble forecasting is implemented by geostatistical model and interpolation with geographical property data. In addition, the stochastic approach, analog ensemble is applied to reduce the uncertainty in wind power forecasting and use of Numerical Weather Prediction models for accurate wind power forecasting is considered. We conduct stochastic wind power forecasting using practical data of Jeju power system and evaluate the system reliability on wind power generation variations. As a result, the proposed model shows better performances than single models, while at the same time providing probabilistic forecasts. Based on these forecasts, the grid operators can identify critical operating time points to prepare for problems that can occur in the system due to wind power variations in advance.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy - Volume 157, 15 August 2018, Pages 211-226
نویسندگان
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