Spatio-Temporal Estimation of Renewable Energy Potential in Built Environments using Big Data
Authors: Alina Ursula Rosa WALCH
Date: 2022-01-13 00:00:00
Proceeding: EPFL 2022 1

To achieve the goals of the Paris Agreement on climate change, governments around the
world must shift to renewable energy (RE) to drastically reduce their CO2 emissions within
the next 10-30 years. The development of effective strategies for RE integration into national
energy systems requires assessing the spatial and temporal availability of these resources at
country scale. This spatio-temporal availability is described by the technical RE potential,
defined as the maximum electrical or thermal energy that can be obtained from a given
technology. While most previous technical potential assessments have used empiricalmodels
and geospatial tools to estimate RE potential, the increasing opportunities provided by Big
Data and Machine Learning approaches, which extract additional information from large
building and environmental datasets, are largely under-exploited. Integrating such datadriven
methods with existingmodels enables national-scale RE resource assessments with
higher precision and at higher spatial and temporal resolution than ever before.
This work proposes Big Data approaches to estimate renewable energy potentials at national
scale, with application to Switzerland. Focusing on the built environment, the technical
potentials of Rooftop Solar Photovoltaics (RPV) and shallow Ground-Source Heat Pumps
(GSHPs) are assessed, at the spatial resolution of individual buildings and at hourly (RPV) or
monthly (GSHP) temporal resolution. The proposed approaches combine large-scale data
processing andMachine Learning with novel computational methods and state-of-the-art
models of RE technologies. Furthermore, the quantification and propagation of uncertainties
is addressed for RPV. The results are publicly available datasets of RE potentials, which enable
spatio-temporal assessments of energy systems with a high share of renewables. At national
level, the estimated RPV potential for Switzerland is 25 § 9 TWh, which may increase up to
39 TWh for more optimistic scenarios for solar PV panel installation, or beyond 60 TWh if
rooftop-integrated PV is used. The shallow GSHP potential is estimated at up to 97 TWh, of
which 20% is located in urban areas. I show that this potential may increase by over 50%
through seasonal regeneration from space cooling. Spatial mismatches between supply and
demand may be reduced through district heating networks (DHN).
These findings have several implications for the Swiss Energy Transition for 2050: (i) To achieve
the national target of 34 TWh from PV systems, 50-55% of all nearly flat (< 20° tilt) or southfacing
surfaces (90° east to 90° west) must be exploited, whereby roofs with a high annual
yield should be prioritised; (ii) GSHPs may deliver 30-70% of the heat demand of the targeted
1.5 million heat pumps and contribute significantly to the expansion of DHN in urban areas;
iii
Abstract
(iii) these fractions may be increased through seasonal regeneration, for example from space
cooling or excess solar thermal generation. The Big Data approaches are transferable to RE
potential assessments in other regions or countries, in particular by adapting them to open
and cross-country input data, as well as to related RE technologies. The proposed methods
and the publicly available datasets can be used by researchers, practitioners and policy makers
to assess future decarbonisation pathways. I hope that this work will contribute to achieving
national CO2 emission targets, moving one step closer to the goals of the Paris Agreement.