首页|基于特征构建及CAE-LSTM的短期电量预测方法

基于特征构建及CAE-LSTM的短期电量预测方法

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线损率能够反映企业的管理水平和经济效益，而供售电不同期会导致线损统计存在误差，因此需要进行短期电量预测。针对现有方法未能充分挖掘电量影响因素的问题，提出基于特征构建及CAE-LSTM的短期电量预测方法。通过数据分析构建特征，并使用MIC进行筛选;使用ARIMA预测电量值，并与特征进行数据重构;通过CAE-LSTM对数据进行特征提取，得到预测结果。实验结果表明，提出的方法能够更有效地提取数据特征，实现更高的预测精度。
A SHORT-TERM ELECTRICITY FORECASTING APPROACH BASED ON FEATURE CONSTRUCTION AND CAE-LSTM
The line loss rate can reflect the management level and economic benefits of the enterprise.The supply and sale of electricity in different periods will cause errors in the line loss statistics,so short-term electricity forecasting is needed.To solve the problems that the existing approaches cannot fully mine the factors affecting the electricity,a short-term electricity forecast approach based on feature construction and CAE-LSTMis proposed.Features were constructed by data analysis,and MIC was employed for screening.ARIMA was employed to forecast the electricity value and new data was reconstructed by features.CAE-LSTM was applied to extract the features of the data and get the predicted result.Experimental results show that the proposed approach can extract data features more effectively and achieve higher prediction accuracy.

Data analysisFeature constructionCAELSTMARIMAElectricity forecastingMaximum infor-mation coefficient

罗俊然、温蜜、何蔚

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上海电力大学计算机科学与技术学院 上海 201306

公安部第三研究所 上海 200031

数据分析 特征构建 CAE LSTM ARIMA 电量预测 最大信息系数

国家自然科学基金项目国家自然科学基金项目国家自然科学基金项目上海市 2019年度"科技创新行动计划"高新技术领域项目上海市科委项目

6187223061802249617023211951110370020020500600

2024

10.3969/j.issn.1000-386x.2024.02.006

计算机应用与软件
上海市计算技术研究所 上海计算机软件技术开发中心

计算机应用与软件

CSTPCD北大核心
影响因子：0.615
ISSN：1000-386X
年,卷(期)：2024.41(2)
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