Machine Learning and Geographic Information Systems for large-scale mapping of renewable energy potential

A promising pathway to follow in order to reach sustainable development goals is an increased reliance on renewable sources of energy. The optimized use of these energy sources, however, requires the assessment of their potential supply, along with the demand loads in locations of interest. In particular, large-scale supply estimation studies are needed in order to evaluate areas of high potential for each type of energy source for a particular region, and allow for the elaboration of efficient global energy strategies. In Switzerland, the "Energy Strategy 2050", initiated in 2011 by the Swiss Federal Council, sets an example with the ambitious goal of reaching a 50-80% reduction of CO2 emissions by the year 2050, with a clear course of action: phasing-out nuclear power, improving energy efficiency, and greatly increasing the use of renewables.

This thesis develops a general data-driven strategy combining Geographic Information Systems and Machine Learning methods to map the large-scale energy potential for three very popular sources of decentralized energy systems: wind energy (using of horizontal axis wind turbines), geothermal energy (using very shallow ground source heat pumps) and solar energy (using photovoltaic solar panels over rooftops). For each of the three considered energy sources, an adapted methodology is suggested to assess its large-scale potential, by estimating multiple variables of interest (with a suitable time resolution, e.g. monthly or yearly), using widely available data, and combining these variables into potential values. These latter estimated variables, dictating the potential, include: (i) the monthly wind speed, and rural and urban topographic/obstacle configuration for wind energy, (ii) the ground thermal conductivity, volumetric heat capacity and monthly temperature gradient for geothermal energy, (iii) the monthly solar radiation, available area for PV panels over rooftops, geometrical characteristics of rooftops and monthly shading factors over rooftops for solar energy. The use of Machine Learning algorithms (notably Support Vector Machines and Random Forests) allows, given adequate features and training data (examples for some locations), for the prediction of the latter variables at unknown locations, along with the uncertainty attached to the predictions. In each case, the developed methodology is set-up with an aim to be applied for Switzerland, meaning that it relies on Swiss available energy-related data. Such data, however, including meteorological, topographic, ground/soil-related and building-related data, is becoming progressively available for most countries, making it possible to widely generalize the proposed methodologies.

Results show that Machine Learning is adequate for energy potential estimation, as the multiple required predictions and spatial extrapolations are achieved with reasonable accuracy. In addition, final values are validated with other existing data or studies when possible, and show general agreement. The application of the suggested potential methodologies in Switzerland outline the very significant potential for the considered renewables. In particular, there is a relatively high potential for Rooftop-Mounted solar PV panels, as it is estimated that they could generate a total electricity production of 16.3 TWh per year, which corresponds to 25.3% of the annual electricity demand in 2017.

Optimisation of Urban Form by the Evaluation of the Solar Potential

Marylène Montavon

What will the cities of tomorrow look like? This is a crucial question because in the four and a half billion years of its existence, the Earth has never been so threatened, as it is today. Since 2008 half of the world's population has lived in towns – more than 70% in rich nations. Three quarters of CO2 emissions are produced in towns, 75% of the world's energy is consumed in towns. The battle for our climate, for the continuation of our species and all other life, will be largely waged in cities. Will it be possible to create the ideal ecological town? The objective of this study is to compare actual and theoretical urban forms in order to explore the diverse effects of daylighting and solar potential on densely built-up sites. Indeed, the search for ideal solar orientations has been a persistent problem from the 19th century until the beginning of the 20th. Even today, for all our scientific knowledge and empirical experience of solar energy, making the correct choice has still not crossed the boundary from an enigmatic, tricky art into the field of an exact science. As a first step, six existing urban sites in Switzerland and Brazil, as well as two projects in the United Kingdom, a few generic models and a utopian city, were modelled and analyzed. The goal was not merely the construction of new, exemplary districts but also the transformation of existing towns. The findings have been used to assess the potential of façades and roofs located in urban areas for active and passive solar heating, photovoltaic electricity generation and daylighting. The results obtained from these different case studies revealed large variations of the potential for solar energy collection on the buildings' façades and roofs. Ideally, these investigations are expected to help architects and town planners understand how to optimise solar collection on buildings design. In addition, a study was made of the Contemporary City of Three Million Inhabitants created by Le Corbusier. According to the latter, the Contemporary City could increase the urban capacity and at the same time improve the urban environment and the efficiency of the city. Therefore the present study extracted the solar potential of this utopian city in order to assess Le Corbusier's propositions. Since 1922, Le Corbusier's studies have influenced architects and town-planners in their choice of orientations and urban forms: it was therefore crucial to discover if this reliance on Le Corbusier was justified. Solar irradiation and illuminance values obtained through numerical simulations form the core part of the method. The method adopted for the entire set of computer simulations consisted in modelling — via a computer-aided drawing piece of software, namely AutoCAD — the digital model of each building or group of buildings prior to exporting them to an urban performance analysis software (PPF/RADIANCE software package). Spatial distributions of solar irradiation and daylight fluxes over the overall building façades and roofs were calculated using ray-tracing simulation techniques to determine the appropriate placement of different solar technologies (passive and active solar, photovoltaic and daylighting). Performance indicators were used to assess the solar utilisation potential of the urban sites (e.g. statistics of sky view factors and daylight factors) in order to determine the optimal solar strategies for a given urban context. In addition, various works and sun-related recommendations that have appeared mainly since the end of the nineteenth century, together with the various scientific models and software tools pertaining to this topic, are presented in this study.

EPFL2010

Geothermal Energy and Biomass Integration in Urban Systems: a Case Study

Frédéric Louis-Pierre Raphaël Marie Amblard, Léda Gerber, François Maréchal, Stefano Moret, Emanuela Peduzzi

Heating, electricity and transportation are the three components of urban systems final energy consumption. Geothermal energy and biomass are two promising renewable energy resources that can be used for the production of heat, electricity and biofuels, thus allowing a reduction of fossil fuel consumption and of the associated greenhouse gas emissions. The goal of this paper is to assess the potential for the integration of geothermal energy combined with biomass in the energy system of a city, taking into account the consumption of fossil and renewable resources, costs and environmental impact. Several process options are available for both resources. For geothermal energy, deep aquifers and EGS (Enhanced Geothermal Systems) are considered, for both separate production of heat and electricity, and cogeneration. For biomass, besides the option of direct combustion, conversion to biofuels by a set of alternative processes (pyrolysis and gasification) is included in this study. The use of pinch analysis and optimization allows the evaluation of possible hybrid energy conversion systems integrating geothermal with biomass. The European city of Lausanne (Switzerland) is used as an example case study. The heat demand of the city is partially satisfied by a district heating network. The complete urban energy system is modeled in order to account for the potential competition and synergies between different energy carriers and technologies for the same end-uses. A set of “ad hoc” scenarios is developed with the goal of assessing the interest of geothermal and biomass within the long-term strategic energy planning of the city.

2015

Spatio-Temporal Estimation of Renewable Energy Potential in Built Environments using Big Data

Alina Ursula Rosa Walch

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 empirical models 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 data-driven methods with existing models 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 and Machine 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 by 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 south-facing surfaces 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) these fractions may be increased through seasonal regeneration, for example from space cooling or excess solar thermal generation. The proposed 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 data-driven 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.