Short-term microgrid load probability density forecasting method based on k-means-deep learning quantile regression

Short-term microgrid load probability density forecasting method based on k-means-deep learning quantile regression

Traditional short-term load forecasting (STLF) methods for large utility grid systems usually provide the forecasted load with deterministic points. However, deterministic load forecasting cannot reveal the load pattern and uncertainty of controllable load in a microgrid, where the prediction errors...

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Bibliographic Details
Main Authors: Zilong Zhao, Jinrui Tang, Jianchao Liu, Ganheng Ge, Binyu Xiong, Yang Li
Format: Article
Language:English
Published: Elsevier 2022-08-01
Series:Energy Reports
Subjects:
Short-term load forecasting
Probability density
Quantile regression
Long short-term memory neural network
Kernel density estimation
Microgrid
Online Access:http://www.sciencedirect.com/science/article/pii/S2352484722006758

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http://www.sciencedirect.com/science/article/pii/S2352484722006758

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