Natural Gas Load Forecasting Based on FCGA-LSTM and Transfer Learning

doi:10.3969/j.issn.1006-2475.2023.07.002

Abstract

Abstract: Abstract：High precision natural gas load forecasting is of great significance to the smooth and efficient operation of natural gas pipeline network. Most of the existing natural gas load forecasting methods are based on the condition of sufficient historical data， and there is little research on the problem of natural gas load forecasting in areas lacking historical data. To solve these problems， a short-term natural gas load forecasting method based on long and short-term memory（LSTM）neural network optimized by Fuzzy Coded Genetic Algorithm (FCGA) and transfer learning is proposed. First， the source domain and the target domain are selected， and the FCGA-LSTM prediction model is constructed by using a large amount of historical load data in the source domain. After model training and testing， the source domain model is moved to the target domain lacking data as a whole， and then a small amount of data in the target domain is used to fine tune and retrain the model. Finally， the target domain load prediction model is obtained. Taking a new residential area in Xi’an as an example， the results show that the prediction accuracy of the prediction method based on FCGA-LSTM and transfer learning is improved by 15.6 percentage points and 35.2 percentage points respectively compared with the combination method of LSTM and transfer learning， LSTM under non transfer learning， which proves the effectiveness of the model. The proposed method has certain guiding significance for the prediction of natural gas load in new urban areas lacking historical data.

Key words: natural gas, load forecasting, genetic algorithm, LSTM, transfer learning

CLC Number:

TP181

ZHANG Zhi-xia, XIE Bao-qiang. Natural Gas Load Forecasting Based on FCGA-LSTM and Transfer Learning[J]. Computer and Modernization, 2023, 0(07): 7-12.

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