Short-term Electric Load Combination Forecasting Model Based on LSTM-LSSVM

被引：0
|
作者：
Fang, Lei [1 ]
Li, Guoqiang [1 ]
Liu, Kun [1 ]
Jin, Feng [1 ]
Yang, Yuxin [1 ]
Guo, Xiao [2 ]
机构：
[1] Weifang Power Supply Co State Grid Shandong Prov, Weifang, Shandong, Peoples R China
[2] Tianjin Univ, Minist Educ, Key Lab Intelligent Grid, Tianjin, Peoples R China
来源：
2024 6TH ASIA ENERGY AND ELECTRICAL ENGINEERING SYMPOSIUM, AEEES 2024 | 2024年
关键词：
Short-term memory network; Least squares support vector machine; Combined forecasting; Short-term power load forecasting; FEATURE-SELECTION;
D O I：
10.1109/AEEES61147.2024.10544994
中图分类号：
学科分类号：
摘要：
Load forecasting is crucial for economic dispatch of power systems, with accuracy impacting grid operation. Due to rising energy demand and changing load characteristics, forecasting complexity has increased. Traditional methods struggle with nonlinear data, complicating load forecasting. This study proposes a novel approach using a hybrid long and short-term memory network with a least-squares support vector machine model. A hybrid seagull algorithm and an improved whale algorithm are employed to optimize the prediction model. Results show superior accuracy compared to individual models, promising advancement in power load forecasting.
引用
收藏
页码：1168 / 1173
页数：6
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