An Expanded Study of the Application of Deep Learning Models in Energy Consumption Prediction

被引：0

作者：

Amaral, Leonardo Santos ^{[2
]}

de Araujo, Gustavo Medeiros ^{[2
]}

Moraes, Ricardo ^{[2
]}

de Oliveira Villela, Paula Monteiro ^{[1
]}

机构：

[1] Univ Estadual Montes Claros UNIMONTES, Ave Prof Rui Braga,S-N Vila Mauriceia, Montes Claros, MG, Brazil

[2] Univ Fed Santa Catarina UFSC, S-N Trindade, Florianopolis, SC, Brazil

来源：

DATA AND INFORMATION IN ONLINE ENVIRONMENTS, DIONE 2022 | 2022年 / 452卷

关键词：

Forecast; Energy; Demand; Deep learning;

D O I：

10.1007/978-3-031-22324-2_12

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The time series of electrical loads are complex, influenced by multiple variables (endogenous and exogenous), display non-linear behavior and have multiple seasonality with daily, weekly and annual cycles. This paper addresses the main aspects of demand forecast modeling from time series and applies machine learning techniques for this type of problem. The results indicate that through an amplified model including the selection of variables, seasonality representation technique selection, appropriate choice of model for database (deep or shallow) and its calibration, it's possible to archive better results with an acceptable computational cost. In the conclusion, suggestions for the continuity of the study are presented.

引用

页码：150 / 162

页数：13

共 50 条

[31] Applicability of energy consumption prediction models in a department store: A case study
Chen, Li-Yuan
Chen, Yen-Tang
Chen, Yu-Hsien
Lee, Da-Sheng
CASE STUDIES IN THERMAL ENGINEERING, 2023, 49
[32] Deep Learning Models for the Prediction of Rainfall
Aswin, S.
Geetha, P.
Vinayakumar, R.
PROCEEDINGS OF THE 2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION AND SIGNAL PROCESSING (ICCSP), 2018, : 657 - 661
[33] Toward Prediction of Energy Consumption Peaks and Timestamping in Commercial Supermarkets Using Deep Learning
Zhao, Mengchen
Gomez-Rosero, Santiago
Nouraei, Hooman
Zych, Craig
Capretz, Miriam A. M.
Sadhu, Ayan
ENERGIES, 2024, 17 (07)
[34] Research on building energy consumption prediction algorithm based on customized deep learning model
Zheng Liang
Junjie Chen
Energy Informatics, 8 (1)
[35] Deep Learning-Based Energy Consumption Prediction Model for Green Industrial Parks
Lai, Chaoan
Wang, Yina
Zhu, Jianhua
Zhou, Xuequan
Applied Artificial Intelligence, 2025, 39 (01)
[36] Interpretable deep learning model for building energy consumption prediction based on attention mechanism
Gao, Yuan
Ruan, Yingjun
ENERGY AND BUILDINGS, 2021, 252
[37] An Integrated Deep-Learning-Based Approach for Energy Consumption Prediction of Machining Systems
Zhang, Meihang
Zhang, Hua
Yan, Wei
Jiang, Zhigang
Zhu, Shuo
SUSTAINABILITY, 2023, 15 (07)
[38] Energy consumption prediction in water treatment plants using deep learning with data augmentation
Harrou, Fouzi
Dairi, Abdelkader
Dorbane, Abdelhakim
Sun, Ying
RESULTS IN ENGINEERING, 2023, 20
[39] Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep Learning
Pamula, Teresa
Pamula, Danuta
ENERGIES, 2022, 15 (05)
[40] INTERPRETABILITY OF MACHINE LEARNING MODELS: APPLICATION FOR LAWSUITS PREDICTION IN THE ENERGY SECTOR
Cavalcante, Andre Borges
PROCEEDINGS OF THE 2020 INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING (IWSSIP), 27TH EDITION, 2020, : 17 - 17

← 1 2 3 4 5 →