摘要
燃气负荷预测对于燃气资源的优化调度至关重要.燃气负荷预测除了具有趋势性、周期性等时间特性外,相邻燃气调压站的负荷数据与温湿度数据之间也存在空间特性,导致燃气负荷预测机理建模困难且模型预测精度较低.针对以上问题,提出了一种基于自回归移动平均模型(ARIMA)与卷积长短时神经网络(ConvLSTM)结合的燃气负荷预测模型.首先,选取皮尔逊相关系数对燃气负荷数据进行分析,筛选与燃气负荷相关性强的变量作为模型的输入.其次,采用ARIMA模型去除数据的趋势性使其平稳化,利用ConvLSTM模型提取数据中的时空特征,并对ARIMA-ConvLSTM模型的参数进行寻优.最后,通过燃气负荷数据对模型进行训练和验证.实验结果表明,ARIMA-ConvLSTM模型的预测准确率为98.65%,在均方根误差、平均绝对误差、绝对误差百分比方面均优于ARIMA模型、ConvLSTM模型和CNN-LSTM并行组合模型.
as load forecasting is crucial to the optimal dispatch of gas resources.In addition to the temporal characteristics such as trend and periodicity,there are also spatial characteristics between the load data and temperature and humidity data of neighboring gas regulator stations,which makes it difficult to model the gas load prediction mechanism and the model prediction accuracy is low.In response to the above problems,a gas load forecasting model based on the combination of autoregressive moving average model(ARIMA)and convolutional long-short-term neural network(ConvLSTM)is proposed.Firstly,the Pearson correlation coefficient is selected to analyze the gas load data and the variables strongly correlated with the gas load are selected as the input of the model.Secondly,the ARIMA model is used to remove the trend of the data to make it smooth,the ConvLSTM model is used to extract the spatio-temporal features in the data,and the parameters of the ARIMA-ConvLSTM model are optimized.Finally,the model is trained and verified through gas load data.Experimental results show that the prediction accuracy of the ARIMA-ConvLSTM model is 98.65%,which is better than the ARIMA model,ConvLSTM model and CNN-LSTM parallel combined model in terms of root mean square error,average absolute error,and absolute error percentage.
作者
海文龙
王亚慧
宋洋
王怀秀
HAI Wenlong;WANG Yahui;SONG Yang;WANG Huaixiu(Beijing University of Civil Engineering and Architecture,Beijing 100044,China)
出处
《微电子学与计算机》
2022年第1期62-70,共9页
Microelectronics & Computer
基金
国家重点研发计划支撑项目(2018YFC0807806)
国家自然科学基金(51471019)。
关键词
燃气负荷预测
自回归移动平均模型
卷积长短时神经网络
卷积神经网络
长短时记忆网络
gas load forecast
autoregressive moving average model
vonvolutional long short-term memory network
vonvolutional neural network
long short-term memory
