Experimental and Computational Approaches to AI-Driven Load Forecasting and Dynamic Pricing in Smart Grids

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This research demonstrates improved forecasting and pricing in smart grids using AI systems, indicating enhanced cost-efficiency.

AI-based systems reduced peak load by 15% and consumer costs by 8%.
A hybrid method using ANN and LSTM improved prediction accuracy compared to individual models.
Reinforcement learning developed adaptive pricing schemes responsive to real-time demand.
Incorporating explainable AI methods allows for transparent insights into pricing and demand forecasts.

Veera Siva Prasad Rajulapati (2025) studied this question.

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