Intelligent Data‐Driven Framework for Fuel Consumption Prediction in Price Commitment Scenarios

ABSTRACT Accurate fuel consumption prediction is critical for effective supply chain management and decision‐making in industrial engineering systems. In price commitment scenarios, the discrepancies between market prices and transaction prices necessitate the development of scenario‐specific prediction methods. However, such scenarios present several challenges. Market price fluctuations can induce variations in consumption patterns, while transaction price dynamics often differ from those in traditional markets, rendering conventional prediction models inadequate. Additionally, the cold‐start characteristic of business results in limited historical data, and traditional methods relying on random sampling strategies often neglect sample heterogeneity, leading to biased weight allocation. To address these issues, we propose an Adaptive Sampling LightGBM framework (ASL), which integrates an adaptive sampling strategy and post‐sampling bagging to enhance model stability with limited samples. By constructing a scenario‐oriented risk function, the method incorporates market price volatility intensity and the divergence between commitment and conventional scenarios as dynamic weighting factors, thereby quantifying the time‐varying impact of price elasticity on consumption. Experimental results using data from two fuel stations in North China demonstrate that our method outperforms benchmark prediction methods.