کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8075756 1521465 2015 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A hybrid short-term load forecasting with a new input selection framework
ترجمه فارسی عنوان
یک پیش بینی بار کوتاه مدت ترکیبی با چارچوب انتخاب جدید ورودی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی (عمومی)
چکیده انگلیسی
This paper proposes a hybrid STLF (short-term load forecasting) framework with a new input selection method. BNN (Bayesian neural network) is used to forecast the load. A combination of the correlation analysis and ℓ2-norm selects the appropriate inputs to the individual BNNs. The correlation analysis calculates the correlation coefficients between the training inputs and output. The Euclidean distance with respect to a desired correlation coefficient is then calculated using the ℓ2-norm. The input sub-series with the minimum Euclidean norm is selected as the most correlated input and decomposed by a wavelet transform to provide the detailed load characteristics for BNN training. The sub-series whose Euclidean norms are closest to the minimum norm are further selected as the inputs for the individual BNNs. A weighted sum of the BNN outputs is used to forecast the load for a particular day. New England load data are used to evaluate the performance of the proposed input selection method. A comparison of the proposed STLF with the existing state-of-the-art forecasting techniques shows a significant improvement in the forecast accuracy.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy - Volume 81, 1 March 2015, Pages 777-786
نویسندگان
, , , ,