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Advanced Machine Learning Models for Dynamic Pricing in Energy Markets
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Author(s): Usharani Bhimavarapu (Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India)
Copyright: 2026
Pages: 30
Source title:
Harnessing Data Science for Sustainable Insurance
Source Author(s)/Editor(s): Gagan Kukreja (College of Business Administration, University of Bahrain, Bahrain), Ayben Koy (Fenerbahçe University, Turkey), Pooja Kansra (Mittal School of Business, Lovely Professional University, India), Diksha Verma (Chandigarh Business School of Administration, Landran Mohali, India)and S.L. Gupta (Birla Institute of Technology, India)
DOI: 10.4018/979-8-3373-1882-0.ch001
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Abstract
Dynamic pricing schemes have an important function in firms aiming to optimize revenue while being fair to consumers. In this study, dynamic pricing methods application in markets like electric vehicle (EV) charging is examined, where fluctuating demand, shifting consumer tastes, and weather conditions raise the intricacy of the decision-making process regarding prices. The research will strike a balance between profitability and customer equity using advanced machine learning techniques, specifically a Bidirectional Stacked Gated Recurrent Unit (BiStacked GRU) model. The model identifies the intricate, sequential relationships among time-series data, which allows for demand fluctuation and customer behavior forecasting in response to price adjustments. Particle Swarm Optimization (PSO) is also applied in feature selection, the identification of the most influential variables that impact pricing strategies.
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