Employing categorical boosting (CatBoost) and meta-heuristic algorithms for predicting the urban gas consumption

Abstract

This study was conducted on the presentation of a method to improve the forecast of urban gas consumption based on the weather variables including temperature, pressure, humidity, wind speed and also the gas price. The diversity of input variables as well as investigating a short-term (daily) scale, led to creation complex and nonlinear relationships between the variables, which makes its solving difficult. To this end, the categorical boosting (CatBoost) method is combined with some meta-heuristic algorithms to create hybrid models. These meta-heuristic algorithms include Phasor Particle Swarm Optimization, Artificial Bee Colony, Battle Royale Optimizer, Grey Wolf Optimizer, Satin Bowerbird algorithm, and Fruit Fly Optimization Algorithm. During the network training, the K-Fold cross-validation has also been used to prevent overfitting. Finally, using an actual dataset, the performance of the proposed method is investigated. The results showed that the proposed method can predict the value of short-term urban gas consumption. The results showed that the hybrid Catboost-PPSO model had the best performance among all presented hybrid models. Therefore, using the PPSO algorithm to optimize the hyper-parameters of the CatBoost network is recommended for predicting gas consumption.