In order to address the challenges of improving energy efficiency and integration of renewable energy, multi-energy systems, composed of electric, natural gas, heat and other energy networks, have received more and more attention in recent years and have been rapidly developed. Through integration as a multi-energy system, different energy infrastructures can be scheduled and managed as one unit. One of the main stages in the optimal scheduling of a multi-energy system is the predictions of various demands and sustainable energy in the scheduling horizon. This paper proposes a prediction model based on adaptive random forest for demands and solar power of a real MES, Stone Edge Farm, in California.The adaptive random forest model can provide a probability distribution of the prediction results. This allows users to consider a variety of scenarios that may occur in the future for further system operation optimization and help users evaluate the reliability of the results. Besides, an online self-adaptability feature is implemented with the model so it can adapt to the new forecasting environment when new observations are detected. The simulations show that the adaptive random forest model is better than the benchmark models in terms of prediction accuracy.

In order to cope with the challenges of improving energy efficiency, increasing the integration of renewable energy, and achieving carbon emission reduction, multi-energy systems have received more and more attention in recent years and have been developing rapidly. Traditionally, different energy infrastructures are usually scheduled and operated independently, which leads to inefficient use of energy and waste of resources. By integrating into a multi-energy system, different energy infrastructures can be coupled and optimized into one unit. In this article, from a low-carbon point of view, the optimal scheduling of a real multi-energy system with hydrogen-based vehicle applications is proposed. The simulation results show that the proposed optimal scheduling can help quantify the daily operation cost and carbon emissions and achieve considerably operation cost saving and carbon reduction by reasonably arranging and utilizing all the devices in the system.