Probabilistic net load forecasting framework for application in distributed integrated renewable energy systems-Reference-Cited by-同舟云学术

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Probabilistic net load forecasting framework for application in distributed integrated renewable energy systems

Published:2024-06 Issue: Volume:11 Page:2535-2553
ISSN:2352-4847
Container-title:Energy Reports
language:en
Short-container-title:Energy Reports

Author:

Telle Jan-Simon^ORCID,Upadhaya Ajay^ORCID,Schönfeldt Patrik^ORCID,Steens Thomas^ORCID,Hanke Benedikt^ORCID,von Maydell Karsten^ORCID

Funder

Bundesministerium für Wirtschaft und Klimaschutz

Publisher

Elsevier BV

Reference73 articles.

1. A review of uncertainty quantification in deep learning: Techniques, applications and challenges;Abdar;Inf. Fusion,2021

2. Energieversorgungskonzepte für klimaneutrale Logistikzentren;Anon,2019

3. Proceedings of the 25th international conference on artificial intelligence and statistics;Anon,2022

4. Deutscher Wetterdienst - Open Data;Anon,2023

5. Review of photovoltaic power forecasting;Antonanzas;Sol. Energy,2016

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1. Study on technical principles of distribution network planning considering high penetration distributed new energy access;2024 3rd International Conference on Energy, Power and Electrical Technology (ICEPET);2024-05-17

2. A Deep Learning Approach Based on Novel Multi-Feature Fusion for Power Load Prediction;Processes;2024-04-15

3. PV-Wind-Diesel based Grid Connected Water Pumping System Driven by Induction Motor;E3S Web of Conferences;2024

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