摘要
准确预测负荷对提高零碳智慧园区的能源利用效率和盈利能力至关重要,受到小时级数值天气预报数据难以获取以及需要在不同时间尺度进行预测的双重挑战,常规负荷预测技术的应用受到制约。在缺失气象预报数据的条件下,提出利用卷积神经网络(CNN)提取多元负荷之间的耦合空间特征,将重构的特征输入长短期记忆(LSTM)神经网络实现负荷时间特征提取,再利用自注意力(Self attention)机制强化模型提取特征信息,最后通过全连接网络进行负荷预测,构建基于CNN-LSTM-Self attention的多元负荷多时间尺度预测模型。以某园区为实例对象,对该园区未来1 h,1 d和1周的冷热电负荷进行预测。试验结果表明:在多个时间尺度上,CNN-LSTM-Self attention模型比CNN,LSTM,CNN-LSTM模型预测更为精确;其中,1 h尺度负荷预测时CNN-LSTM-Self attention模型的优势尤为明显,冷、热、电负荷预测的平均绝对百分比误差(MAPE)比CNN-LSTM模型分别提升了16.25%,19.16%,10.24%。
Accurate load prediction is critical for improving the energy efficiency and profitability of zero-carbon smart parks.However,the application of conventional load prediction techniques faces two main challenges which are the difficulty in obtaining hourly numerical weather forecast data and the need for predictions across different time scales.In the absence of weather forecast data,a method using Convolutional Neural Networks(CNN)was proposed to extract the coupled spatial features between multiple loads.The reconstructed features were input into a Long Short-Term Memory(LSTM)network to extract temporal features of the load,followed by the application of a self-attention mechanism to enhance the model's ability to extract feature information.A fully connected network was then employed for load prediction,resulting in a multi-variable-load,multi-time-scale prediction model based on CNN-LSTM-Self attention.A case study of a park was used to predict its cooling,heating,and electrical loads for the next 1 hour,1 day,and 1 week.Experimental results showed that the CNN-LSTM-Self attention model outperformed the CNN,LSTM,and CNN-LSTM models in terms of prediction accuracy across multiple time scales.Specifically,the CNN-LSTM-Self attention model showed a more significant advantage in predicting the 1-hour load,with the mean absolute percentage error(MAPE)of cooling,heating,and electrical load predictions improved by 16.25%,19.16%,and 10.24%,respectively,compared to the CNN-LSTM model.
作者
杨澜倩
郭锦敏
田慧丽
黄畅
刘敏
蔡阳
YANG Lanqian;GUO Jinmin;TIAN Huili;HUANG Chang;LIU Min;CAI Yang(Guangzhou Power Supply Bureau,Guangdong Power Grid Corporation,Guangzhou 510510,China;Energy and Electricity Research Center,Jinan University,Zhuhai 519070,China)
出处
《综合智慧能源》
2025年第2期79-87,共9页
Integrated Intelligent Energy
基金
南方电网公司科技项目(030100KC23020019)
国家自然科学基金项目(52306013)
中央高校基本科研业务费专项资金资助项目(21624212)。
关键词
零碳智慧园区
负荷预测
多时间尺度
卷积神经网络
长短期记忆神经网络
自注意力机制
zero-carbon smart park
load prediction
multi-time scale
convolutional neural network
long short-term memory neural network
self-attention mechanism
