Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting
المؤلفون المشاركون
Deng, Zhuofu
Zhu, Zhiliang
Wang, Binbin
Guo, Heng
Chai, Chengwei
Wang, Yanze
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-18، 18ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-01-22
دولة النشر
مصر
عدد الصفحات
18
التخصصات الرئيسية
الملخص EN
Residential load forecasting is important for many entities in the electricity market, but the load profile of single residence shows more volatilities and uncertainties.
Due to the difficulty in producing reliable point forecasts, probabilistic load forecasting becomes more popular as a result of catching the volatility and uncertainty by intervals, density, or quantiles.
In this paper, we propose a unified quantile regression deep neural network with time-cognition for tackling this challenging issue.
At first, a convolutional neural network with multiscale convolution is devised for extracting more behavioral features from the historical load sequence.
In addition, a novel periodical coding method marks the model to enhance its ability of capturing regular load pattern.
Then, features generated from both subnetworks are fused and fed into the forecasting model with an end-to-end manner.
Besides, a globally differentiable quantile loss function constrains the whole network for training.
At last, forecasts of multiple quantiles are directly generated in one shot.
With ablation experiments, the proposed model achieved the best results in the AQS, AACE, and inversion error, and especially the average of the AACE is grown by 34.71%, 75.22%, and 32.44% compared with QGBRT, QCNN, and QLSTM, respectively, indicating that our method has excellent reliability and robustness rather than the state-of-the-art models obviously.
Meanwhile, great performances of efficient time response demonstrate that our proposed work has promising prospects in practical applications.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Deng, Zhuofu& Wang, Binbin& Guo, Heng& Chai, Chengwei& Wang, Yanze& Zhu, Zhiliang. 2020. Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting. Complexity،Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1145419
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Deng, Zhuofu…[et al.]. Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting. Complexity No. 2020 (2020), pp.1-18.
https://search.emarefa.net/detail/BIM-1145419
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Deng, Zhuofu& Wang, Binbin& Guo, Heng& Chai, Chengwei& Wang, Yanze& Zhu, Zhiliang. Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting. Complexity. 2020. Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1145419
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1145419
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر