Embedding based quantile regression neural network for probabilistic load forecasting
Abstract Compared to traditional point load forecasting, probabilistic load forecasting (PLF) has great significance in advanced system scheduling and planning with higher reliability. Medium term probabilistic load forecasting with a resolution to an hour has turned out to be practical especially i...
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Online Access: | http://link.springer.com/article/10.1007/s40565-018-0380-x |
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doaj-336008108bee43a8b843ebfe4ae5437d2021-05-02T23:15:36ZengIEEEJournal of Modern Power Systems and Clean Energy2196-56252196-54202018-02-016224425410.1007/s40565-018-0380-xEmbedding based quantile regression neural network for probabilistic load forecastingDahua GAN0Yi WANG1Shuo YANG2Chongqing KANG3Department of Electrical Engineering, Tsinghua UniversityDepartment of Electrical Engineering, Tsinghua UniversityDepartment of Electrical Engineering, Tsinghua UniversityDepartment of Electrical Engineering, Tsinghua UniversityAbstract Compared to traditional point load forecasting, probabilistic load forecasting (PLF) has great significance in advanced system scheduling and planning with higher reliability. Medium term probabilistic load forecasting with a resolution to an hour has turned out to be practical especially in medium term energy trading and can enhance the performance of forecasting compared to those only utilizing daily information. Two main uncertainties exist when PLF is implemented: the first is the temperature fluctuation at the same time of each year; the second is the load variation which means that even if observed indicators are fixed since other observed external indicators can be responsible for the variation. Therefore, we propose a hybrid model considering both temperature uncertainty and load variation to generate medium term probabilistic forecasting with hourly resolution. An innovative quantile regression neural network with parameter embedding is established to capture the load variation, and a temperature scenario based technique is utilized to generate temperature forecasting in a probabilistic manner. It turns out that the proposed method overrides commonly used benchmark models in the case study.http://link.springer.com/article/10.1007/s40565-018-0380-xProbabilistic load forecastingFeature embeddingArtificial neural networkQuantile regressionMachine learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dahua GAN Yi WANG Shuo YANG Chongqing KANG |
spellingShingle |
Dahua GAN Yi WANG Shuo YANG Chongqing KANG Embedding based quantile regression neural network for probabilistic load forecasting Journal of Modern Power Systems and Clean Energy Probabilistic load forecasting Feature embedding Artificial neural network Quantile regression Machine learning |
author_facet |
Dahua GAN Yi WANG Shuo YANG Chongqing KANG |
author_sort |
Dahua GAN |
title |
Embedding based quantile regression neural network for probabilistic load forecasting |
title_short |
Embedding based quantile regression neural network for probabilistic load forecasting |
title_full |
Embedding based quantile regression neural network for probabilistic load forecasting |
title_fullStr |
Embedding based quantile regression neural network for probabilistic load forecasting |
title_full_unstemmed |
Embedding based quantile regression neural network for probabilistic load forecasting |
title_sort |
embedding based quantile regression neural network for probabilistic load forecasting |
publisher |
IEEE |
series |
Journal of Modern Power Systems and Clean Energy |
issn |
2196-5625 2196-5420 |
publishDate |
2018-02-01 |
description |
Abstract Compared to traditional point load forecasting, probabilistic load forecasting (PLF) has great significance in advanced system scheduling and planning with higher reliability. Medium term probabilistic load forecasting with a resolution to an hour has turned out to be practical especially in medium term energy trading and can enhance the performance of forecasting compared to those only utilizing daily information. Two main uncertainties exist when PLF is implemented: the first is the temperature fluctuation at the same time of each year; the second is the load variation which means that even if observed indicators are fixed since other observed external indicators can be responsible for the variation. Therefore, we propose a hybrid model considering both temperature uncertainty and load variation to generate medium term probabilistic forecasting with hourly resolution. An innovative quantile regression neural network with parameter embedding is established to capture the load variation, and a temperature scenario based technique is utilized to generate temperature forecasting in a probabilistic manner. It turns out that the proposed method overrides commonly used benchmark models in the case study. |
topic |
Probabilistic load forecasting Feature embedding Artificial neural network Quantile regression Machine learning |
url |
http://link.springer.com/article/10.1007/s40565-018-0380-x |
work_keys_str_mv |
AT dahuagan embeddingbasedquantileregressionneuralnetworkforprobabilisticloadforecasting AT yiwang embeddingbasedquantileregressionneuralnetworkforprobabilisticloadforecasting AT shuoyang embeddingbasedquantileregressionneuralnetworkforprobabilisticloadforecasting AT chongqingkang embeddingbasedquantileregressionneuralnetworkforprobabilisticloadforecasting |
_version_ |
1721486594309881856 |