Prediction of Fuel Consumption in Forest Machines with the Application of Experimentable Digital Twins

Fuel consumption in forest machines is a widely discussed topic due to its ecological relevance to climate-neutral wood harvesting operations. Extensive research has been conducted—and continues to be carried out—to better understand forest machinery, its movements, and the operational factors that influence fuel use.

However, fuel consumption—and consequently carbon emissions—is not determined solely by the machine itself. It is also heavily influenced by various physical characteristics of the forest environment, including terrain, soil conditions, forest structure, and tree parameters. This complexity highlights the need for effective systems capable of predicting fuel consumption under diverse forest conditions.

Accurate prediction of fuel consumption and carbon emissions requires detailed simulations that incorporate all relevant machine and environmental parameters. This paper provides a comprehensive overview of the current state of the art and offers in-depth insights into fuel consumption prediction. It outlines key aspects such as data requirements, data acquisition methods, and the simulation design process.