Electricity Demand Forecasting and Risk Assessment for Campus Energy Management

This paper employs the Grey–Markov Model (GMM) to predict users’ electricity demand and introduces the Enhanced Monte Carlo (EMC) method to assess the reliability of the prediction results. The GMM integrates the advantages of the Grey Model (GM) and the Markov Chain to enhance prediction accuracy, while the EMC combines the Monte Carlo simulation with a dual-variable approach to conduct a comprehensive risk assessment. This framework helps decision-makers better understand electricity demand patterns and effectively manage associated risks. A university campus in southern Taiwan is selected as the case study. Historical data of monthly maximum electricity demand, including peak, semi-peak, Saturday semi-peak, and off-peak periods, were collected and organized into a database using Excel. The GMM was applied to predict the monthly maximum electricity demand for the target year, and its prediction results were compared with those obtained from the GM and Grey Differential Equation (GDE) models. The results show that the average Mean Absolute Percentage Error (MAPE) values for the GM, GDE, and GMM are 10.96341%, 9.333164%, and 6.56026%, respectively. Among the three models, the GMM exhibits the lowest average MAPE, indicating superior prediction performance. The proposed GMM demonstrates robust predictive capability and significant practical value, offering a more effective forecasting tool than the GM and GDE models. Furthermore, the EMC method is utilized to evaluate the reliability of the risk assessment. The findings of this study provide decision-makers with a reliable reference for electricity demand forecasting and risk management, thereby supporting more effective contract capacity planning.