AI-Based Renewable Energy Forecasting and Industrial Decision Support Using SARIMA

Harshatha R; Harshini Angel M; Hema Harshini J; Shobarani R; F Jerald; Subbulakshmi M

AI-Based Renewable Energy Forecasting and Industrial Decision Support Using SARIMA

Author(s)	Ms. Harshatha R, Ms. Harshini Angel M, Ms. Hema Harshini J, Prof. Dr. Shobarani R, Prof. Dr. F Jerald, Prof. Subbulakshmi M
Country	India
Abstract	Renewable energy resources have been gradually being embraced by industries to reduce the cost of operation and make their operations more sustainable; however, natural fluctuations in renewable energy resources renders it difficult to plan the production and energy management. The proposed Industrial Renewable Energy Production Intelligence System is an AI-based decision-support system and the focus of this paper, which attempts to generate the optimal use of energy in industries and decrease reliance on the grid. The proposed system preprocesses historical information on the generation of renewable energy, industrial production, and consumption of power to compute energy intensity measures with regard to production. A Seasonal AutoRegressive Integrated Moving Average (SARIMA) time series model is used to predict the availability of renewable energy resources. The energy demand in the industry is determined in regard to the projected production level and the energy intensity measures of the past. An optimization strategy based on rules is employed and the forecasted renewable supply is compared with the forecasted demand and preferential utilization of renewable energy sources is given alongside supplementation of the grid energy only when necessary to make the total cost of energy as low as possible. The offered solution is created as an interactive Streamlit app that will provide historical analysis, forecasting, energy allocation planning, cost comparison, diesel dependence analysis, and AI-based production adjusting recommendations. The experimental study confirms the fact that the given solution will improve the utilization of renewable sources of energy, reduce the reliance on the grid, and assist in managing industrial energy effectively.
Keywords	Renewable Energy Forecasting, SARIMA, Industrial Energy Management, Energy Demand Estimation, Rule-Based Optimization, Cost Minimization
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-23

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AI-Based Renewable Energy Forecasting and Industrial Decision Support Using SARIMA

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