Modeling of Multiple Heating Substations Based on Long Short-Term Memory Networks
The central heating is a complex nonlinear system. It is difficult to establish an accurate model based on multiple heating substations. In this paper, the Long Short-Term Memory (LSTM) algorithm is proposed to solve this problem. Heating substations generate data with the time series characteristics. The algorithm not only reflects the characteristics of time sequence of heating substations, but also solves the problem of long-term dependence. And, the necessary information can be saved in a limited memory capacity. Based on a large amount of historical data of the heating system of a Baotou heating company, ensuring that the total heat source is sufficient, the simulation results of the LSTM model in multiple substations show the validity, which provides the basis for the optimization of the central heating system, and a reference for LSTM to solve the complex time series modeling and prediction problems.
KeywordsTime sequence Long-term dependence LSTM Modeling of multiple heating substations
The work was supported by National Natural Science Foundation of China (61463040).
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