Enhancing Short-Term Electric Load Forecasting for Households Using Quantile LSTM and Clustering-Based Probabilistic Approach

Electricity load forecasting is an essential part of power system planning and operation, and it is crucial to make accurate predictions. The smart grid paradigm and the new energy market necessitate better demand-side management (DSM) and more reliable end-user forecasts to system scale. This paper...

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Principais autores:	Zaki Masood, Rahma Gantassi, Yonghoon Choi
格式:	Artigo
語言:	Inglês
出版:	IEEE 2024-01-01
叢編:	IEEE Access
主題:	Deep regression learning electricity load clustering probabilistic forecasting short-term load forecasting smart grid
在線閱讀:	https://ieeexplore.ieee.org/document/10540128/
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