HCLTS: Mining Customers' Consumption Patterns in Natural Gas Time Series with Hierarchical Contrastive Learning

Abstract

Accurate forecasting of resource consumption, such as gas, is essential for efficient energy management, cost reduction, and sustainability. Time series forecasting (TSF) techniques like recurrent neural networks (RNNs), convolutional networks (TCNs), and Transformers have been employed to model complex temporal variations but face challenges in capturing long-term dependencies or inter-series relationships. In this paper, we propose a Hierarchical Contrastive Learning approach for Time Series (HCLTS) to improve gas consumption prediction for industrial and commercial users. HCLTS employs a prototype-based approach to construct positive and negative samples, leveraging industry labels and hierarchical sampling. We further adopt a hierarchical contrastive training objective to model underlying consumption patterns across industries. Extensive experiments on a real-world gas consumption dataset demonstrate that HCLTS achieves superior performance over existing methods, highlighting its potential for practical applications in diverse industrial contexts.