Prediction of Natural Gas Consumption for City-Level DHS Based on Attention GRU: A Case Study for a Northern Chinese City

Abstract

With the increasing energy demand and environmental protection requirements of heating systems, natural gas (NG), as an efficient and clean alternative energy source for coal, plays an increasingly important role in district heating systems (DHSs). The accurate prediction of the DHS consumption of NG in the whole city is helpful in formulating an efficient energy scheduling plan. However, because the DHS is a complex non-linear system with time-space delay effect and multi-factors, the traditional algorithms have deficiencies in the prediction accuracy of the natural gas consumption for city-level DHS. To achieve accurate prediction, a gas consumption prediction algorithm based on the attention gated recurrent unit (AGRU) model is proposed, which can obtain high-level features of historical data that affect gas consumption prediction. In addition, the attention mechanism can help to select more critical feature inputs and improve the prediction accuracy of gas consumption. Detailed comparative experiments were performed between the proposed AGRU and state-of-art NG consumption prediction algorithms, such as the recurrent neural network (RNN), long short-term memory (LSTM), GRU, attention RNN (ARNN), attention LSTM (ALSTM), support vector regression (SVR), random forest regression (RFR), gradient boosting regression (GBR) and decision tree regression (DTR). An analysis of the experimental results shows that the proposed AGRU algorithm has a prediction accuracy of 95.3%, which is significantly better than other algorithms. In addition, the hyperparameters of the AGRU algorithm are also tested in detail to optimize the selection. INDEX TERMS Natural gas consumption prediction, district heating system, attention mechanism, gated recurrent unit.

Modeling Energy Demand—A Systematic Literature Review

“…GIS techniques are used to reference data to geo-spatial shapes and visualization in the form of geographical maps. This has been used in the context of building simulation, where geometry and orientation was considered [54,57,77,78] or planning of rural [56] or urban [79,80] grid infrastructure. In other cases regional loads and trends in spatial energy consumption were analyzed [21,[81][82][83] or modelled using socioeconomic data [84][85][86].…”

Seasonal thermal energy storage of solar heat: Its role in the clean heating transition in China

“…The Harbin case has the highest reduction potential since 95.8% of the existing DH system is based on coal combustion [260]. The Urumqi case reduces the least CO2 emissions, primarily because natural gas dominates the existing DH system [261].…”