Trends in Electrical Engineering Original Research
AI-BASED SMART RESOURCE DEMANDAND PRICE PREDICTION USING MACHINE LEARNING FOR EFFICIENT RESOURCE MANAGEMENT
Abstract
AI and machine learning models now help manage resource supplies and price changes across key Indian markets. The development and application of advanced predictive analytics in India's agriculture supply chain and wholesale energy market systems are examined here. Although agriculture occupies over 40% of India's land area, it accounts for approximately 15–18% of the national Gross Domestic Product (GDP). In order to regulate the PDS, unify land information, and optimize the Localized Agricultural Value Chains (LAVC), new ML- target prediction algorithms will be tested and implemented, focusing on reforming the agriculture sector which suffers from enormous inefficiencies. A strong forecasting model, that can accommodate large variations, is also needed to forecast the day ahead electricity prices in the IEX. This paper proposes a point-wise prediction and interpretable framework for Electricity Price Forecasting (EPF) using a Lasso Estimated AutoRegressive (LEAR) model and Deep Neural Networks. In addition, the paper provides evidence of serious flaws in existing forecasting literature and the need for both standardization and strict evaluation criteria in the field. Using an extensive 973-day hourly dataset (23,352 samples) from the IEX, predictive performance is evaluated using Relative Mean Absolute Error (RMAE) and Diebold-Mariano (DM) statistical tests. Experimental results demonstrate that the LEAR model achieves an RMAE of 0.812 and the DNN achieves 0.785 against naive benchmarks, confirming superior accuracy and statistical significance (p
Keywords
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