Towards the Development of the Smart Grid: Fast Electricity Load Forecasting Using Different Hybrid Approaches

被引：0

作者：

Jurado, Sergio ^{[1
]}

Mugica, Francisco ^{[2
]}

Nebot, Angela ^{[2
]}

Avellana, Narcis ^{[1
]}

机构：

[1] Sensing & Control Syst SL, Barcelona, Spain

[2] Univ Politecn Cataluna, Dept Llenguatges i Sistemes InformAt, Barcelona, Spain

来源：

ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT: PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE OF THE CATALAN ASSOCIATION FOR ARTIFICIAL INTELLIGENCE | 2013年 / 256卷

关键词：

Smart Grid; Load Forecasting; Random Forest; Artificial Neural Networks; Fuzzy Inductive Reasoning; Hybrid Approach;

D O I：

10.3233/978-1-61499-320-9-185

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Currently, worldwide scientific community is doing a great effort of research in the area of Smart Grids because energy production, distribution, and consumption play a critical role in the sustainability of the planet. In this context, electricity load forecasting methodologies with fast response is a key component for demand-side management and the emergence of prosumers in the electricity grid. In this research it is shown that the computational intelligence techniques presented can deal with real time forecast, cope with incomplete measurement data and forecast signals of great variability, when applied to three real locations, with distinctly different characteristics.

引用

页码：185 / 188

页数：4

共 50 条

[21] An Approximate Forecasting of Electricity Load and Price of a Smart Home Using Nearest Neighbor
Nawaz, Muhammad
Javaid, Nadeem
Mangla, Fakhar Ullah
Munir, Maria
Ihsan, Farwa
Javaid, Atia
Asif, Muhammad
COMPLEX, INTELLIGENT, AND SOFTWARE INTENSIVE SYSTEMS (CISIS 2019), 2020, 993 : 521 - 533
[22] Performance of a Hybrid Neural-Based Framework for Alternative Electricity Price Forecasting in the Smart Grid
Lei G.
Xu C.
Chen J.
Zhao H.
Parvaneh H.
Distributed Generation and Alternative Energy Journal, 2022, 37 (03): : 405 - 434
[23] Comments on: Short-term forecasting the daily load curve for residential electricity usage in smart grid
Espasa, Antoni
Durban, Maria
APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, 2013, 29 (06) : 621 - 623
[24] New Clustering-based Forecasting Method for Disaggregated End-consumer Electricity Load Using Smart Grid Data
Laurinec, Peter
Lucka, Maria
2017 IEEE 14TH INTERNATIONAL SCIENTIFIC CONFERENCE ON INFORMATICS, 2017, : 210 - 215
[25] Enhancing smart grid reliability with advanced load forecasting using deep learning
Jasmine, J.
Nisha, M. Germin
Prasad, Rajesh
ELECTRICAL ENGINEERING, 2025,
[26] Real Time Energy Management and Load Forecasting in Smart Grid using CompactRIO
Thiyagarajan, K.
SaravanaKumar, R.
INTERNATIONAL CONFERENCE ON COMPUTATIONAL MODELLING AND SECURITY (CMS 2016), 2016, 85 : 656 - 661
[27] Long-term load forecasting for smart grid
Kumar, Vikash
Mandal, Rajib Kumar
ENGINEERING RESEARCH EXPRESS, 2024, 6 (04):
[28] Electricity Price and Demand Forecasting Under Smart Grid Environment
Masri, Dina
Zeineldin, Hatem
Woon, Wei Lee
2015 IEEE 15TH INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING (IEEE EEEIC 2015), 2015, : 1956 - 1960
[29] A Novel Hybrid Short-Term Load Forecasting Method of Smart Grid Using MLR and LSTM Neural Network
Li, Jian
Deng, Daiyu
Zhao, Junbo
Cai, Dongsheng
Hu, Weihao
Zhang, Man
Huang, Qi
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2021, 17 (04) : 2443 - 2452
[30] A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid
Hafeez, Ghulam
Khan, Imran
Jan, Sadaqat
Shah, Ibrar Ali
Khan, Farrukh Aslam
Derhab, Abdelouahid
APPLIED ENERGY, 2021, 299

← 1 2 3 4 5 →