LUT-DEMAND is an open access, open source pre-processing modelling tool for energy system transition models. It enables to model a variety of energy demand scenarios as input for energy system transition studies. LUT-DEMAND follows a macro-economic approach, linking the energy service demand to gross domestic product and population. The model is implemented in the commonly used Microsoft EXCEL software, which makes it applicable for the majority of users without requiring further programming skills or special software. A dedicated documentation of the model is provided as well. LUT-DEMAND covers all relevant sectors: Power Heat Transport Industry Desalination Carbon dioxide removal (CDR) From energy services to final energy demand, LUT-DEMAND offers to specify energy demands, i.e. for example temperature levels for heat demand. Furthermore, shares of powertrains in the transport sector can be modeled, which results in a respective final energy demand for a variety of fuels, which can be further specified in fossil and electricity-based fuels. Finally, it is possible to estimate the total primary energy demand in LUT-DEMAND. This enables a fast impact-check of demand scenario design. Furthermore, it offers a fast overview on required capacities for the two main renewable energy resources of the future (solar photovoltaic and wind power), which in turn require certain areas. Both values are two of many output values of LUT-DEMAND. Information and updates are announced on Twitter: @Do_Keiner, @ChristianOnRE, hashtag #LUTdemand. Suggestions, ideas, discussions, and bug reports are warmly welcome. Please contact either Dominik Keiner (dominik.keiner@lut.fi) or Christian Breyer (christian.breyer@lut.fi). Many thanks to: The Jenny and Antti Wihuri Foundation for the valuable working grant awarded to Dominik Keiner, which mainly enabled the implementation of the model. The Academy of Finland for the 'Industrial Emissions & CDR' project under the number 343053 and 'DAC 2.0' project number under the number 329313, which partly funded this research. The LUT University Research Platform 'GREENRENEW', which partly funded this research. Please cite the journal article when using the model:

Keiner D., Gulagi A., Breyer C. (2023) Energy demand estimation using a pre-processing macro-economic modelling tool for 21st century transition analyses. Energy 272, 127199. https://doi.org/10.1016/j.energy.2023.127199