کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5012203 1462809 2017 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network
ترجمه فارسی عنوان
پیش بینی دقیق و احتمالاتی قدرت فتوولتائیک بر اساس شبکه عصبی کانولوشن عمیق
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی (عمومی)
چکیده انگلیسی
The penetration of photovoltaic (PV) energy into modern electric power and energy systems has been gradually increased in recent years due to its benefits of being abundant, inexhaustible and clean. In order to reduce the negative impacts of PV energy on electric power and energy systems, advanced forecasting approach with high-accuracy is a pressing need. Aimed at this, a novel hybrid method for deterministic PV power forecasting based on wavelet transform (WT) and deep convolutional neural network (DCNN) is firstly proposed in this paper. WT is used to decompose the original signal into several frequency series. Each frequency has better outlines and behaviors. DCNN is employed to extract the nonlinear features and invariant structures exhibited in each frequency. Then, a probabilistic PV power forecasting model that combines the proposed deterministic method and spine quantile regression (QR) is originally developed to statistically evaluate the probabilistic information in PV power data. The proposed deterministic and probabilistic forecasting methods are applied to real PV data series collected from PV farms in Belgium. Numerical results presented in the case studies demonstrate that the proposed methods exhibit the ability of improving forecasting accuracies in terms of seasons and various prediction horizons, when compared to conventional forecasting models.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy Conversion and Management - Volume 153, 1 December 2017, Pages 409-422
نویسندگان
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