A machine learning approach for mapping the very shallow theoretical geothermal potential

• Published in: Geothermal energy (Heidelberg) 2019-07, Vol.7 (1), p.1-50, Article 19
• Main Authors: Assouline, Dan, Mohajeri, Nahid, Gudmundsson, Agust, Scartezzini, Jean-Louis
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Clean energy; Data; Earth and Environmental Science; Earth Sciences; Forests; Geoecology/Natural Processes; Geographic Information Systems; Geographical information systems; Geotechnical Engineering & Applied Earth Sciences; Geothermal potential; Geothermal resources; Information systems; Learning algorithms; Machine learning; Mapping; Methods; Remote sensing; Renewable and Green Energy; Rural areas; Soil; Soil layers; Surface layers; Switzerland; Thermal conductivity; Thermal diffusivity; Thermal energy; Training; Very shallow system; Weather
• ISSN: 2195-9706; 2195-9706
• DOI: 10.1186/s40517-019-0135-6

The very shallow geothermal potential (vSGP) is increasingly recognized as a viable resource for providing clean thermal energy in urban and rural areas. This is primarily due to its reliability, low-cost installation, easy maintenance, and little constraints regarding ground-related laws and policies. We propose a methodology to extract the theoretical vSGP (installed in the uppermost 10 m of the ground, and mostly at depths of 1–2 m) at the national scale for Switzerland, based on a combination of Geographic Information Systems, traditional modelling, and machine learning (ML). The theoretical vSGP is based on the estimation of three thermal characteristics of the ground that impact significantly the geothermal potential, namely the monthly temperature at various depths in the surface layer, the thermal conductivity, and the thermal diffusivity. Each of the three variables is estimated separately, to a depth of 1 m below the surface, using the following general strategy: (1) collect significant data related to the variable, (2) if not existing, extract values for the variable at available locations with the help of traditional models and part of the data as input for these models, (3) train a ML model (with the Random Forests algorithm) using the extracted variable values as examples (training output labels) and related information contained in the data as features (training input samples), (4) use the trained ML model to estimate the variable in unknown locations, (5) estimate the uncertainty attached to the estimations. The methodology estimates values for (200 × 200) (m 2 ) pixels forming a grid over Switzerland. The strategy, however, can be generalized to any country with significant data (topographic, weather, and surface layer/soil data) available. The results indicate a very non-negligible potential for very shallow geothermal systems in Switzerland.