AI-Driven Dynamic Pricing and Tariff Optimization: Machine Learning Approaches for Energy Market Efficiency

Abstract

Dynamic pricing and tariff optimization have become critical components in modern energy markets to effectively balance supply and demand, enhance operational efficiency, and maximize revenue generation. The increasing penetration of renewable energy sources and the growing complexity of electricity markets require advanced techniques capable of handling uncertainty, variability, and large-scale data processing. Traditional pricing mechanisms are no longer sufficient to capture the dynamic behavior of modern power systems, necessitating the adoption of intelligent, data-driven approaches [1], [2].

This paper presents a comprehensive review of artificial intelligence (AI) and machine learning (ML) approaches for developing intelligent dynamic pricing systems in electricity markets. Various state-of-the-art models, including transformer-based architectures, recurrent neural networks (RNNs), gradient boosting decision trees (GBDT), temporal convolutional networks (TCN), and reinforcement learning (RL) techniques, are analyzed for their ability to capture temporal patterns, nonlinear relationships, and market dynamics [3]–[5]. These models enable accurate price forecasting, demand prediction, and adaptive tariff optimization in highly volatile environments.

Furthermore, this study identifies key challenges associated with AI-driven pricing systems, including data quality issues, real-time computational constraints, model interpretability, and regulatory compliance requirements [6], [7]. To address these challenges, the paper proposes integrated hybrid frameworks that combine multiple machine learning techniques to improve prediction accuracy, robustness, and scalability. In addition, practical deployment considerations such as demand response integration, smart grid infrastructure, and consumer behavioral modeling are discussed in detail.

A case study on electricity price forecasting and optimal tariff design demonstrates the effectiveness of ensemble hybrid approaches, achieving improved forecasting accuracy and enhanced market efficiency. The results highlight the potential of AI-driven systems to reduce peak demand, stabilize prices, and improve overall system performance [8]. This work provides valuable insights for researchers, policymakers, and industry practitioners aiming to implement scalable and efficient AI-based pricing strategies in deregulated energy markets.