Estimate the daily consumption of natural gas in district heating system based on a hybrid seasonal decomposition and temporal convolutional network model

Estimate the daily consumption of natural gas in district heating system based on a hybrid seasonal decomposition and temporal convolutional network model

Natural gas

Forecasting

Pipelines

Regression analysis

Support vector machines

Convolution

Convolutional neural networks

Gases

Recurrent neural networks

District heating

Heating equipment

District heating system

Natural gas consumption prediction

Seasonal decomposition

Temporal convolutional network

Adaptive boosting

In recent years, natural gas was widely used as a primary clean energy source to replace coal-fired in northern cities in China, whose objective is to reduce the severe environmental pollution caused by coal-fired central heating in winter. The rapid growth of natural gas consumption has brought a significant burden for natural gas production and transportation, affecting residents' regular heating demand. Therefore, accurately predicting natural gas consumption is of great significance to the district heating system (DHS). However, accurately predicting natural gas consumption is challenging due to the complex nonlinear time-varying feature for the large-scale DHS system. A novel hybrid model was proposed to predict the daily natural gas consumption in the DHS based on the seasonal decomposition and temporal convolution network (SDTCN) in this paper, under the principle of Divide and Conquers strategy and deep learning algorithm. The seasonal decomposition of the natural gas consumption produces three distinct subsequences: the trend item, the seasonal item, and the residual item. The spatiotemporal features of these three subsequences are then modeled and predicted based on the TCN model, combining the advantages of recurrent neural networks (RNN) and convolution neural network (CNN) characteristics. Besides, we compare the two SDTCN models with state-of-the-art algorithms, such as support vector machine (SVM), Adaboost, extreme tree regression (ETR), passive-aggressive regression (PassAgg), nu support vector regression (NuSVR), and bootstrap aggregating (Bagging). The experimental results show that the proposed SDTCN model is superior to other algorithms, and the prediction accuracy can reach 94.4%.

118444

309

Applied Energy

2022

Song, Jiancai

Zhang, Liyi

Jiang, Qingling

Ma, Yunpeng

Zhang, Xinxin

Xue, Guixiang

Shen, Xingliang

Wu, Xiangdong

journalArticle

0306-2619

10.1016/j.apenergy.2021.118444

https://www.sciencedirect.com/science/article/pii/S030626192101669X