Comparative Analysis of Machine Learning Models for Short-Term Net Load Forecasting in Renewable Integrated Microgrids-Reference-Cited by-同舟云学术

Comparative Analysis of Machine Learning Models for Short-Term Net Load Forecasting in Renewable Integrated Microgrids

Published:2022-10-17 Issue: Volume: Page:
ISSN:
Container-title:2022 2nd International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)
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Author:

Tziolis Georgios¹,Lopez-Lorente Javier¹,Baka Maria-Iro¹,Koumis Anastasios²,Livera Andreas¹,Theocharides Spyros¹,Makrides George¹,Georghiou George E.¹

Affiliation:

1. University of Cyprus,PV Technology Laboratory, FOSS Research Centre for Sustainable Energy,Department of Electrical and Computer Engineering,Nicosia,Cyprus

2. Aristotle University of Thessaloniki,Department of Electrical and Computer Engineering,Thessaloniki,Greece

Funder

Research and Innovation Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9941347/9941375/09941378.pdf?arnumber=9941378

Reference19 articles.

1. The Computation of Equivalent Potential Temperature

2. Data processing and quality verification for improved photovoltaic performance and reliability analytics

3. A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

4. Extreme gradient boosting model based on improved Jaya optimizer applied to forecasting energy consumption in residential buildings;sauer;Evol Syst,2021

5. A composite k-nearest neighbor model for day-ahead load forecasting with limited temperature forecasts

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