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An Efficient Approach to Forecasting Monthly Calls for Repair from Gas Consumers

Forecasting monthly calls for repair from gas consumers is an important part of the gas company to improve the level of service, optimize the allocation of resources and improve the living level of people. In this paper, through the study of historical data of monthly calls for repair from gas consumers, we find that it has the characteristics of seasonal periodic variation. A hybridization methodology based on Seasonal Autoregressive Integrated Moving Average (SARIMA) and back propagation(BP) neural network is proposed, which is used to forecast monthly calls for repair from gas consumers. The time series of monthly calls for repair from gas consumers is decomposed into linear autocorrelation and non-linear structure of two parts. The SARIMA model is used to predict the linear part of the sequence, and the BP neural network model is used to predict the non-linear residual part. Finally, the forecast results of two parts are synthesized into the final result. The case study shows that the hybrid model outperforms either of the models used separately. Moreover, the hybrid model can balance the deviation of a single model with better applicability and higher accuracy.