Ultra-short-term photovoltaic power prediction based on similar day clustering and temporal convolutional network with bidirectional long short-term memory model: A case study using DKASC data-Reference-Cited by-同舟云学术

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Ultra-short-term photovoltaic power prediction based on similar day clustering and temporal convolutional network with bidirectional long short-term memory model: A case study using DKASC data

Published:2024-12 Issue: Volume:375 Page:124085
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Zhang Mingyue,Han Yang,Wang Chaoyang,Yang Ping,Wang Congling,Zalhaf Amr S.

Funder

Sichuan Province Science and Technology Support Program

Sichuan Provincial Postdoctoral Science Foundation

Shanxi Provincial Postdoctoral Science Foundation

Key Science and Technology Program of Shaanxi Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference44 articles.

1. Pairing ensemble numerical weather prediction with ensemble physical model chain for probabilistic photovoltaic power forecasting;Mayer;Renew Sust Energ Rev,2023

2. Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge;Luo;Energy,2021

3. Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network;Han;Appl Energy,2019

4. Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model;Wang;Energy,2023

5. Memory long and short term time series network for ultra-short-term photovoltaic power forecasting;Huang;Energy,2023

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