کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4959448 1364862 2018 16 صفحه PDF ندارد دانلود رایگان
عنوان انگلیسی مقاله
Short-run electricity load forecasting with combinations of stationary wavelet transforms
ترجمه فارسی عنوان
پیش بینی بار الکتریسته کوتاه مدت با ترکیبی از تبدیل موجک ثابت
کلمات کلیدی
پیش بینی؛ تقاضای برق؛ فصلی؛ تبدیل موجک؛ ترکیبات؛
Forecasting; Electricity demand; Seasonality; Wavelet transform; Combinations;
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
چکیده انگلیسی

•Day-ahead forecast of electricity load is performed in the French wholesale Market.•The aggregate load is decomposed in subseries with stationary wavelet transforms.•New boundary treatments are proposed for the wavelet decomposition.•Combining forecasts including the wavelet predictions beats individual predictors.

Short-term forecasting of electricity load is an essential issue for the management of power systems and for energy trading. Specific modeling approaches are needed given the strong seasonality and volatility in load data. In this paper, we investigate the benefit of combining stationary wavelet transforms to produce one day-ahead forecasts of half-hourly electric load in France. First, we assess the advantage of decomposing the aggregate load into several subseries with a wavelet transform. Each component is predicted separately and aggregated to get the final forecast. One innovation of this paper is to propose several approaches to deal with the boundary problem which is particularly detrimental in electricity load forecasting. Second, we examine the benefit of combining forecasts over individual models. An extensive out-of-sample evaluation shows that a careful treatment of the border effect is required in the multiresolution analysis. Combinations including the wavelet predictions provide the most accurate forecasts. This result is valid with several assumptions about the forecast error in temperature and for different types of hours (peak, normal, off-peak), different days of the week and various forecasting periods.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: European Journal of Operational Research - Volume 264, Issue 1, 1 January 2018, Pages 149-164
نویسندگان
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