Combined forecasting model of gas daily load based on weight distribution of ant colony algorithm

Abstract:

Urban gas daily load has the characteristics of randomness and variability, and single load forecasting models have certain limitations in practical applications, especially for specific time periods. To tackle such problem, five evaluation criteria were used to eliminate redundant model before combination forecasting, and a method of constructing variable weight combined forecasting model was proposed. The distribution weights of the combined forecasting model were determined by ant colony algorithm, so that the precision of the combined forecasting model was better than single models in a certain time period. First, the time-varying system of urban gas daily load and the characteristics of each forecasting model were analyzed, which contains many random and fuzzy uncertainties. Then, four kinds of single daily load forecasting models were determined, including ridge regression (Ridge), differential autoregressive integral moving average model (ARIMA), support vector machine regression (SVR), and extreme gradient lifting tree (XGB). According to the characteristics of urban gas daily load model, the parameters of each model and the input vector of the model were given. The redundant model was eliminated by using the comprehensive evaluation indexes calculated by the average relative error, root mean square error, grey correlation degree, correlation coefficient, and Theil unequal coefficient as the evaluation criteria. Finally, based on the ant colony algorithm, a weight distribution combination forecasting model was developed. Study results show that the long-term comprehensive prediction effect of the proposed model was superior to that of any single model. Compared with a single model, the combined forecasting model had higher stability and fault tolerance rate, and stronger generalization ability.