Title:

REVIEW OF REINFORCEMENT LEARNING APPLICATIONS IN ADAPTIVE LOAD FORECASTING FOR SMART GRIDS

Author:

Nagraj Pralhad Kamble and Dr. Nilesh Vasant Ingale

Abstract:

The increasing complexity of modern smart grids, driven by renewable energy integration, distributed generation, electric vehicles, and dynamic demand patterns, has elevated the need for accurate and adaptive load forecasting techniques. Reinforcement Learning with its ability to learn optimal decision policies through interactions with the environment, has emerged as a promising paradigm for adaptive load forecasting. This review examines major RL techniques Q-learning, Deep Q-Networks Policy Gradient methods, and Actor–Critic approaches applied to short-term, mid-term, and long-term load forecasting scenarios. The paper highlights key methodologies, performance comparisons, challenges, and future research prospects. A tabular summary of RL models used in load forecasting is included for clarity.

Keyword:

Reinforcement Learning, Adaptive Load Forecasting, Smart Grids.