Using Machine Learning to estimate the technical potential of shallow ground-source heat pumps with thermal interference

The increasing use of ground-source heat pumps (GSHPs) for heating and cooling of buildings raises questions regarding the technical potential of GSHPs and their impact on the temperature in the shallow subsurface. In this paper, we develop a method using Machine Learning to estimate the technical potential of shallow GSHPs, which enables such an estimation for Switzerland with limited data and computational resources. A training dataset is constructed based on meteorological and geological data across Switzerland. We analyse correlations and the importance of each of the input data for estimating the GSHP potential and compare different input feature sets and Machine Learning models. The Random Forest algorithm, trained on the full dataset, provides the best performance to estimate the GSHP potential. The resulting model yields an R2 score of 0.95 for the annual energy potential, 0.86 for the heat extraction rate, and 0.82 for the potential number of boreholes per GSHP system

Residential density classification for sustainable housing development using a machine learning approach

Using Machine Learning (ML) algorithms for classification of the existing residential neighbourhoods and their spatial characteristics (e.g. density) so as to provide plausible scenarios for designing future sustainable housing is a novel application. Here we develop a methodology using a Random Forests algorithm (in combination with GIS spatial data processing) to detect and classify the residential neighbourhoods and their spatial characteristics within the region between Oxford and Cambridge, that is, the 'Oxford-Cambridge Arc'. The classification model is based on four pre-defined urban classes, that is, Centre, Urban, Suburban, and Rural for the entire region. The resolution is a grid of 500 m × 500 m. The features for classification include (1) dwelling geometric attributes (e.g. garden size, building footprint area, building perimeter), (2) street networks (e.g. street length, street density, street connectivity), (3) dwelling density (number of housing units per hectare), (4) building residential types (detached, semi-detached, terraced, and flats), and (5) characteristics of the surrounding neighbourhoods. The classification results, with overall average accuracy of 80% (accuracy per class: Centre: 38%, Urban 91%, Suburban 83%, and Rural 77%), for the Arc region show that the most important variables were three characteristics of the surrounding area: residential footprint area, dwelling density, and number of private gardens. The results of the classification are used to establish a baseline for the current status of the residential neighbourhoods in the Arc region. The results bring data-driven decision-making processes to the level of local authority and policy makers in order to support sustainable housing development at the regional scale

City Shape, Entropy, and Street Networks

Cities are among the most complex man-made structures ever generated. This complexity is reflected in the structure of street networks, which form a part of the field of complex networks. Street networks have distinct geometric properties that are controlled partly by internal (social and economic) and partly by external (geographic) parameters. Analyses of the street networks of 50 cities (with a total of 823,202 streets) from widely different areas (Brazil, Britain, Chile, and Iran) indicate, first, that landscape constraints (the sea, lakes, mountains, valley, and rivers) have large effects on the general shapes of cities and the configuration of their street networks - in particular on the lengths, trends, and associated entropies. The length entropies are mostly controlled by the space available for the network growth, whereas the trend entropies are mostly controlled by the shapes of the constraining landscape features. Second, the results also indicate significant geometric differences between the street networks of the inner (older) parts and the outer (more recent) parts of cities. More specifically, the inner parts have lower Shannon/Gibbs trend/length entropies - are more tightly ordered and with denser networks - than the outer parts. Entropy and street length increase, as a result of spreading, with distance from the inner parts. Tracing the evolution of several networks indicates network growth through densification and expansion and a gradual increase in entropy over time. The results also indicate that larger cities have fewer streets and less total street length per capita than smaller cities, indicating that as the city size increases its street network becomes more energy efficient. In conclusion, the entropy measures and the visualisation techniques introduced in this work offer new methods for analysing street (and other) networks worldwide, including their complex geometrical variations in space and, where appropriate, their development through time