An Optimized Grey–Markov Computational Framework for Accurate Energy Demand Forecasting and Industrial Decision Support

Abstract

Accurate long-term energy demand forecasting is essential for supporting strategic planning, infrastructure development, and resource allocation under increasing uncertainty. However, conventional forecasting methods often struggle to achieve reliable predictions when historical observations are limited, and energy consumption exhibits nonlinear and stochastic behavior. This study proposes a Simulated Annealing Markov Chain Grey Model (SA-MCGM) that integrates Grey System Theory, Markov Chain modelling, and Simulated Annealing (SA) optimization into a unified computational forecasting framework. The proposed approach combines deterministic trend modelling with probabilistic residual correction while simultaneously optimizing the grey model parameters and the number of Markov states to improve forecasting accuracy. The model is validated using annual energy consumption data for Asia from 2000 to 2022 and is compared with GM(1,1), the conventional Markov Chain Grey Model (MCGM), and ARIMA. Experimental results show that the proposed model achieves the best predictive performance during the testing period, reducing the Mean Squared Error (MSE) by 98.17% compared with the conventional MCGM and producing stable long-term forecasts through 2035. The findings demonstrate that simultaneous optimization of deterministic and stochastic model components significantly enhances forecasting robustness and generalization. The proposed SA-MCGM provides a computationally efficient forecasting framework that can support industrial engineering applications, including energy planning, infrastructure development, production planning, and strategic decision-making under uncertain conditions.