Data‐driven forecasting of local PV generation for stochastic PV ‐battery system management-Reference-Cited by-同舟云学术

Data‐driven forecasting of local PV generation for stochastic PV ‐battery system management

Published:2021-05-09 Issue:11 Volume:45 Page:15962-15979
ISSN:0363-907X
Container-title:International Journal of Energy Research
language:en
Short-container-title:Int J Energy Res

Author:

Kaffash Mahtab¹²^ORCID,Bruninx Kenneth²³,Deconinck Geert¹²

Affiliation:

1. Research Division ELECTA Department of Electrical Engineering (ESAT), KU Leuven Leuven Belgium

2. AMO Section EnergyVille Genk Belgium

3. Research Division TME Department of Mechanical Engineering (WTK), KU Leuven Leuven Belgium

Publisher

Wiley

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/er.6826

Reference46 articles.

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3. KaffashM ThoelenK DeconinckG.Optimal scheduling of a residential energy storage device in multi‐carrier energy systems. Paper presented at: IEEE Benelux PELS/PES/IAS Young Researchers Symposium2018.

4. KardakosEG AlexiadisMC VagropoulosSI SimoglouCK BiskasPN BakirtzisAG.Application of time series and artificial neural network models in short‐term forecasting of PV power generation. Paper presented at: 2013 48th International Universities' Power Engineering Conference (UPEC)2013:1‐6.

5. Combined Stochastic Optimization of Frequency Control and Self-Consumption With a Battery

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