کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6680141 | 1428069 | 2018 | 20 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms
ترجمه فارسی عنوان
پیش بینی قیمت برق نقطه ای: رویکردهای فراگیری یادگیری و مقایسه تجربی الگوریتم های سنتی
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کلمات کلیدی
پیش بینی قیمت برق، یادگیری عمیق، مطالعه معیار،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
مهندسی انرژی و فناوری های برق
چکیده انگلیسی
In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning models for predicting electricity prices and we show how they lead to improvements in predictive accuracy. In addition, we also consider that, despite the large number of proposed methods for predicting electricity prices, an extensive benchmark is still missing. To tackle that, we compare and analyze the accuracy of 27 common approaches for electricity price forecasting. Based on the benchmark results, we show how the proposed deep learning models outperform the state-of-the-art methods and obtain results that are statistically significant. Finally, using the same results, we also show that: (i) machine learning methods yield, in general, a better accuracy than statistical models; (ii) moving average terms do not improve the predictive accuracy; (iii) hybrid models do not outperform their simpler counterparts.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Energy - Volume 221, 1 July 2018, Pages 386-405
Journal: Applied Energy - Volume 221, 1 July 2018, Pages 386-405
نویسندگان
Jesus Lago, Fjo De Ridder, Bart De Schutter,