基于小波变换和深度学习的短期天然气负荷预测研究

Research on Short⁃Term Natural Gas Load Forecasting Based on Wavelet Transform and Deep Learning

田文才 乔伟彪 周国峰 刘伟

Wencai Tian Weibiao Qiao Guofeng Zhou Wei Liu

华北水利水电大学 环境与市政工程学院，河南 郑州 450046 重庆大学 资源与安全学院，重庆 400044

School of Environmental and Municipal Engineering，North China University of Water Resources and Electric Power，Zhengzhou Henan 450046，China School of Resources and Safety Engineering，Chongqing University，Chongqing 400044，China

随着天然气在能源消耗中占比越来越大，如何准确预知未来的天然气消耗量，对天然气资源合理规划具有重大意义。针对此问题，提出一种基于小波变换和深度学习的短期天然气负荷预测模型。首先对所收集的天然气负荷数据利用不同小波变换进行分解，之后对其进行归一化处理；其次利用深度学习算法对数据进行训练与预测；然后利用小波重构对预测的数据分别进行整合；最后以平均绝对百分误差、平均绝对误差和均方根误差为评价指标，评价不同小波变换的预测结果，计算最优小波变换的最优阶数和层数。结果表明，Fk小波变换第22阶第6层相对于其他小波变换和直接利用LSTM进行预测具有更高的预测精度。

As natural gas accounts for an increasing proportion of energy consumption, how to accurately predict the future natural gas consumption is of great significance to the rational planning of natural gas. For this problem,a short?term natural gas load forecasting model based on wavelet transform and deep learning was proposed. First,the collected natural gas load was decomposed by using different wavelets , and then normalized it.Secondly, the data wes trained and predictd by using the deep learning algorithm Long Short?Term Memory (LSTM); then the predicted data was separately integrated by using wavelet reconstruction.Finally, the average absolute percentage error, average absolute error and root mean square error were used as evaluation indicators to evaluate the prediction results of different wavelets, and the optimal order and number of layers of the optimal wavelet were calculated.The examples show that the 22nd?order 6th layer of Fk wavelet transforms has higher prediction accuracy than other wavelets transforms and direct use of LSTM for prediction.

重庆市基础研究与前沿探索项目(cstc2018jcyjAX0441)

10.3969/j.issn.1672-6952.2021.05.016