Point-based and probabilistic electricity demand prediction with a Neural Facebook Prophet and Kernel Density Estimation model-Reference-Cited by-同舟云学术

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Point-based and probabilistic electricity demand prediction with a Neural Facebook Prophet and Kernel Density Estimation model

Published:2024-09 Issue: Volume:135 Page:108702
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Ghimire Sujan,Deo Ravinesh C.^ORCID,Ali Pourmousavi S.^ORCID,Casillas-Pérez David^ORCID,Salcedo-Sanz Sancho

Funder

Ministerio de Ciencia e Innovación

Gobierno de España Ministerio de Ciencia e Innovación

Publisher

Elsevier BV

Reference107 articles.

1. Deep-based conditional probability density function forecasting of residential loads;Afrasiabi;IEEE Trans. Smart Grid,2020

2. CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production;Agga;Electr. Power Syst. Res.,2022

3. Hybrid CNN-LSTM model for short-term individual household load forecasting;Alhussein;IEEE Access,2020

4. A review of deep learning methods applied on load forecasting;Almalaq,2017

5. PV power forecast using a nonparametric PV model;Almeida;Sol. Energy,2015

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Model run monitoring and parameter modification methods;Applied Mathematics and Nonlinear Sciences;2024-01-01

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