辽宁大学学报（自然科学版）2024，Vol.51Issue(4)：360-367,8.
基于MSA-LSTM的短期电力负荷预测模型
Short-Term Power Load Forecasting Model Based on MSA-LSTM
摘要
Abstract
Short-term power load forecasting refers to the process of predicting the power load over a relatively short period of time in the future.The current short-term power load forecasting faces problems such as strong uncertainty,rapid load changes,and high computational costs.In response to the above issues,this article proposes a novel MSA-LSTM model for short-term power load prediction by integrating multi-head self attention(MSA)mechanism and long short-term memory(LSTM)network.This model aims to handle the time dependence and complexity of power load data,add MSA structure as the input module of LSTM network structure,enhance the long-term memory ability of LSTM network and the ability to capture key time series features.Through experimental verification on the target dataset,the MSA-LSTM model outperforms traditional LSTM models and BiLSTM models in terms of prediction accuracy and stability.Using the dataset of the 9th Electric Power Cup power load data and meteorological data,a ten fold cross validation was conducted on the model proposed in this paper.Compared with the LSTM model and the bidirectional long short-term memory(BiLSTM)model,the average mean square error(MSE)of the MSA-LSTM model was reduced by 4.285%and 2.672%,respectively;The standard deviation of errors decreased by 6.575%and 3.406%,respectively.The research results indicate that the model has high application value in power system load forecasting and is of great significance for optimizing power system operation and decision support.关键词
短期电力负荷预测/多头自注意力机制/LSTMKey words
short-term power load forecasting/multi-head self-attention mechanism/LSTM分类
信息技术与安全科学引用本文复制引用
冯勇,张校铭..基于MSA-LSTM的短期电力负荷预测模型[J].辽宁大学学报（自然科学版）,2024,51(4):360-367,8.基金项目
辽宁省属本科高校基本科研业务费专项资金资助项目(LJKLJ202414) （LJKLJ202414）
