Exploring Load Forecasting: Bridging Statistical Methods to Deep Learning Techniques in Real-World Environment-Reference-Cited by-同舟云学术

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Exploring Load Forecasting: Bridging Statistical Methods to Deep Learning Techniques in Real-World Environment

Published:2024-04-29 Issue: Volume:16 Page:491-496
ISSN:
Container-title:2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)
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Author:

Makris Ioannis¹,Moschos Nikolaos¹,Radoglou-Grammatikis Panagiotis²,Andriopoulos Nikolaos³,Tzanis Nikolaos³,Malamaki Kyriaki-Nefeli³,Fotopoulou Maria⁴,Kaousias Konstantinos⁵,Tompros George Lampsidis⁵,Sarigiannidis Panagiotis¹

Affiliation:

1. MetaMind Innovations,Kozani,Greece

2. University of Western Macedonia,Department of Electrical and Computer Engineering,Kozani,Greece

3. Independent Power Transmission Operator,Athens,Greece

4. Centre for Research and Technology Hellas (CERTH),Athens,Greece

5. Hellenic Electricity Distribution Network Operator (H.E.D.N.O.),Athens,Greece

Funder

Horizon Europe

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10621442/10621052/10621486.pdf?arnumber=10621486

Reference19 articles.

1. The EVIDENT Platform: Leveraging Crowdsourcing Approaches for Assessing Consumers Behavior

2. Load forecasting techniques and their applications in smart grids;Habbak,2023

3. A review of machine learning in building load prediction;Zhang,2021

4. Load forecasting models in smart grid using smart meter information: A review;Dewangan,2023

5. Load forecasting with machine learning˜ and deep learning methods;Cordeiro-Costas,2023

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