Comparison of multi-step forecasting methods for renewable energy

被引：1

作者：

Dolgintseva, E. ^{[1
]}

Wu, H. ^{[1
]}

Petrosian, O. ^{[1
]}

Zhadan, A. ^{[1
]}

Allakhverdyan, A. ^{[1
]}

Martemyanov, A. ^{[1
]}

机构：

[1] St Petersburg State Univ, Fac Appl Math & Control Proc, Univ skii Prospekt 35, St Petersburg 198504, Russia

来源：

ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS | 2024年

关键词：

Multi-step forecasting; Energy forecasting; Renewable energy; Neural network; Direct forecasting; Recursive forecasting; LightGBM; PREDICTION; RECURRENT;

D O I：

10.1007/s12667-024-00656-w

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Multi-step forecasting influences systems of energy management a lot, but traditional methods are unable to obtain important feature information because of the complex composition of features, which causes prediction errors. There are numerous types of data to forecast in the energy sector; we present the following datasets for comparison in the paper: electricity demand, PV production, and heating, ventilation, and air conditioning load. For a detailed comparison, we took both classical and modern forecasting methods: Bayesian ridge, Ridge regression, Linear regression, ARD regression, LightGBM, RF, Bi-RNN, Bi-LSTM, Bi-GRU, and XGBoost.

引用

页数：32

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