Solar energy potential assessment on rooftops and facades in large built environments based on LiDAR data, image processing and cloud computing. Methodological background, application and validation in Geneva (solar cadaster)

The paper presents the core methodology for assessing solar radiation and energy production on building rooftops and vertical facades (still rarely considered) of the inner-city. This integrated tool is based on the use of LiDAR, 2D and 3D cadastral data. Together with solar radiation and astronomical models it calculates the global irradiance for a set of points located on roofs, ground and facades. Although the tool takes simultaneously roofs, ground and facades, different methods of shadow casting are applied. Shadow casting on rooftops is based on image processing techniques. On the other hand, the assessment on facade involves first to create and interpolate points along the facades and then to implement a point-by-point shadow casting routine. The paper is structured in five parts: (i) state of the art on the use of 3D GIS and automated processes in assessing solar radiation in the built environment, (ii) overview on the methodological framework used in the paper, (iii) detailed presentation of the method proposed for solar modelling and shadow casting, in particular by introducing an innovative approach for modelling the Sky View Factor (SVF), (iv) demonstration of the solar model introduced in this paper through applications in Geneva’s building roofs (solar cadaster) and facades, (v) validation of the solar model in some Geneva’s spots, focusing especially on two distinct comparisons: solar model versus fisheye catchments on partially inclined surfaces (roof component); solar model versus photovoltaic simulation tool PVSyst on vertical surfaces (facades). Concerning the roof component, validation results emphasize global sensitivity related to the density of light sources on the sky vault to model the SVF. The low dense sky model with 145 light sources gives satisfying results, especially when processing solar cadasters in large urban areas, thus allowing to save computation time. In the case of building facades, introducing weighting factor in SVF calculation leads to outputs close to those obtained by PVSyst. Such good validation results make the proposed model a reliable tool to: (i) automatically process solar cadaster on building rooftops and facades at large urban scales, (ii) support solar energy planning and energy transition policies

Assessment of the photovoltaic potential at urban level based on 3D city models: A case study and new methodological approach

The use of 3D city models combined with simulation functionalities allows to quantify energy demand and renewable generation for a very large set of buildings. The scope of this paper is to determine the solar photovoltaic potential at an urban and regional scale using CityGML geometry descriptions of every building. An innovative urban simulation platform is used to calculate the PV potential of the Ludwigsburg County in south-west Germany, in which every building was simulated by using 3D city models. Both technical and economic potential (considering roof area and insolation thresholds) are investigated, as well as two different PV efficiency scenarios. In this way, it was possible to determine the fraction of the electricity demand that can be covered in each municipality and the whole region, deciding the best strategy, the profitability of the investments and determining optimal locations. Additionally, another important contribution is a literature review regarding the different methods of PV potential estimation and the available roof area reduction coefficients. An economic analysis and emission assessment has also been developed. The results of the study show that it is possible to achieve high annual rates of covered electricity demand in several municipalities for some of the considered scenarios, reaching even more than 100% in some cases. The use of all available roof space (technical potential) could cover 77% of the region’s electricity consumption and 56% as an economic potential with only high irradiance roofs considered. The proposed methodological approach should contribute valuably in helping policy-making processes and communicating the advantages of distributed generation and PV systems in buildings to regulators, researchers and the general public

3-D visualization of urban environmental quality indicators: using the city GML standard

In October 2005, a survey to several potential end-users (surveyors, architects, urban planners, environmental and energy specialists, among others) of the City of Geneva (Switzerland) howed a strong interest for the integration of the third dimension in the available GIS data of the City of Geneva, mainly by the integration of new layers of information, here referred to as 3-D urban indicators [1]. In order to extract this type of indicators, different geo-referenced data of excellent quality, such as 2-D cadastral data, aerial images, LiDAR (Light Detection And Ranging) data and a 3-D vector model were used. A main focus was given to the construction of innovative 3-D Urban Environmental Quality (UEQ) indicators, which are highlighted through the 3-D visualization displays proposed here. A simple example is the exploration of the solar potential of building facades and roofs. In this case, the purpose is to evaluate the potential of buildings for the installation of solar panels (photovoltaic and thermal).Peer Reviewe

Extraction of Urban Environmental Quality Indicators using LiDAR-Based Digital Surface Models

The visualization of specific 3-D urban scenes can be done calling upon different techniques, from those more traditional, such as photogrammetry, to the most advanced ones, such as laser scanning that uses different techniques and algorithms of selection and modelling of 3-D point clouds. The use and utility of this kind of data for the study of urban development remain however debatable. Indeed, indicators for urban development and durability are highly necessary and the best methodology to build them is largely open. This thesis anticipates the use of 2-D and 3-D models and data for the environmental analysis of cities, aiming to provide useful tools for urban planning and design. According to end-users requirements, the extraction of urban environmental quality (UEQ) indicators from 2-D and 3-D information using innovative methods is proposed and implemented, which is based on recent research on computational algorithms for the analysis, evaluation, management and design of the urban space. Moreover, results that can be obtained with different data sources and aggregation methods are compared. In particular, the main advantages of urban models generated from LiDAR data are highlighted. In consequence, an iterative process is proposed, involving professionals of various fields, aiming at improving the utility of those indicators for the support of applied decision activities related to the sustainable development of cities. This process is sub-divided in three correlated steps: A preliminary inquiry concerning the user requirements for the implementation of a 3-D project of the State/City of Geneva was launched. Based on the obtained replies, several potential applications related to both the definition and extraction of urban indicators were identified, and also, end-users were classified into 6 different domains: 1– architecture, urbanism and territory planning; 2– urban traffic (motor vehicles, trains and airplanes); 3– environment and energy; 4– pedestrian and cyclist mobility; 5– security and emergency situations management; 6– underground information; Based on point 1. and according to the assessment of the specific needs among each of these domains, several interviews were carried out in which 25 end-users decided to focus on UEQ indicators considering three main stakes: 1– assessment of the morphological properties of the urban texture; 2– exploration of the solar potential on the urban fabric; 3– estimation of the energy demand on the urban fabric. Many empirical case-studies are emphasized, mostly for the city of Geneva, and also for the cities of Lausanne and Florence. These indicators are extracted from the segmentation of planar roof areas using classified LiDAR point clouds and the use of image processing techniques based on Digital Elevation Models (DEM) and Digital Height Models (DHM), defined in this thesis as 2.5-Digital Urban Surface Models (2.5-DUSM) and normalized 2.5-Digital Urban Surface Models (n2.5-DUSM) respectively. These models are constructed in a step by step basis, using LiDAR and 2-D and 3-D vector data, thus applying different methods of interpolation and enhancement, whose accuracy is also evaluated on a statistical basis; Finally, an inquiry on how the same group of 25 end-users mentioned in point 1. perceives and interprets the different exploratory 2-D and 3-D geo-visualizations proposed for some of the UEQ indicators is undertaken, evaluating their utility according to the requirements previously defined

Software-Defined Infrastructure for IoT-based Energy Systems

Internet of Things (IoT) devices are becoming an essential part of our everyday lives. These physical devices are connected to the internet and can measure or control the environment around us. Further, IoT devices are increasingly being used to monitor buildings, farms, health, and transportation. As these connected devices become more pervasive, these devices will generate vast amounts of data that can be used to gain insights and build intelligence into the system. At the same time, large-scale deployment of these devices will raise new challenges in efficiently managing and controlling them. In this thesis, I argue that the IoT devices need programmability and need to provide software controls in order to manage them efficiently. Further, it will need data-driven modeling techniques to process and analyze a vast amount of data from heterogeneous devices to derive actionable insights. My thesis explores the problems posed by software-defined IoT energy infrastructure. I present four techniques that use systems and machine learning principles to design, analyze and deploy the next generation of smart IoT energy systems. First, I discuss how current state-of-the-art LIDAR-based approaches in identifying ideal locations on rooftops for deploying energy systems such as solar do not scale to many regions of the world. To address the challenges, I propose DeepRoof, a data-driven approach that uses deep learning to estimate the solar potential of roofs using satellite imagery and identify ideal locations for installation. We evaluate our approach on different types of roof and show that our technique is comparable to LIDAR-based methods. Second, I study how excessive solar can cause problems in the grid and examine how programmatic control of the solar output can prevent congestion in the electric grid. Further, I present a decentralized approach that can control the solar arrays in a grid-friendly manner. Also, my approach provides flexible control of solar output, and I show that such mechanisms allow for higher solar penetration in the grid. Third, I discuss the challenges in community-owned (and shared) distributed energy resources that do not provide independent control to users. To do so, I propose vSolar, an approach to virtualize the solar arrays and energy storage that allows independent control. Further, I show how using vSolar users can exercise independent control, implement their custom energy sharing policies, and reduce energy costs through energy trading. Finally, I present the challenges, and the high throughput needs to enable a peer-to-peer energy trading platform using permissioned blockchains. I propose FabricPlus, an enhanced Hyperledger Fabric blockchain, that contains a series of optimizations to enable high throughput transactions. FabricPlus increases the transaction throughput many folds, without requiring any changes to its external interfaces. I also show considerable performance improvement over the baseline Fabric

The Application of LiDAR to Assessment of Rooftop Solar Photovoltaic Deployment Potential in a Municipal District Unit

A methodology is provided for the application of Light Detection and Ranging (LiDAR) to automated solar photovoltaic (PV) deployment analysis on the regional scale. Challenges in urban information extraction and management for solar PV deployment assessment are determined and quantitative solutions are offered. This paper provides the following contributions: (i) a methodology that is consistent with recommendations from existing literature advocating the integration of cross-disciplinary competences in remote sensing (RS), GIS, computer vision and urban environmental studies; (ii) a robust methodology that can work with low-resolution, incomprehensive data and reconstruct vegetation and building separately, but concurrently; (iii) recommendations for future generation of software. A case study is presented as an example of the methodology. Experience from the case study such as the trade-off between time consumption and data quality are discussed to highlight a need for connectivity between demographic information, electrical engineering schemes and GIS and a typical factor of solar useful roofs extracted per method. Finally, conclusions are developed to provide a final methodology to extract the most useful information from the lowest resolution and least comprehensive data to provide solar electric assessments over large areas, which can be adapted anywhere in the world

Assessment of Renewable Energy Resources with Remote Sensing

The development of renewable energy sources plays a fundamental role in the transition towards a low carbon economy. Considering that renewable energy resources have an intrinsic relationship with meteorological conditions and climate patterns, methodologies based on the remote sensing of the atmosphere are fundamental sources of information to support the energy sector in planning and operation procedures. This Special Issue is intended to provide a highly recognized international forum to present recent advances in remote sensing to data acquisition required by the energy sector. After a review, a total of eleven papers were accepted for publication. The contributions focus on solar, wind, and geothermal energy resource. This editorial presents a brief overview of each contribution.About the Editor .............................................. vii Fernando Ramos Martins Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing Reprinted from: Remote Sens. 2020, 12, 3748, doi:10.3390/rs12223748 ................. 1 André R. Gonçalves, Arcilan T. Assireu, Fernando R. Martins, Madeleine S. G. Casagrande, Enrique V. Mattos, Rodrigo S. Costa, Robson B. Passos, Silvia V. Pereira, Marcelo P. Pes, Francisco J. L. Lima and Enio B. Pereira Enhancement of Cloudless Skies Frequency over a Large Tropical Reservoir in Brazil Reprinted from: Remote Sens. 2020, 12, 2793, doi:10.3390/rs12172793 ................. 7 Anders V. Lindfors, Axel Hertsberg, Aku Riihelä, Thomas Carlund, Jörg Trentmann and Richard Müller On the Land-Sea Contrast in the Surface Solar Radiation (SSR) in the Baltic Region Reprinted from: Remote Sens. 2020, 12, 3509, doi:10.3390/rs12213509 ................. 33 Joaquín Alonso-Montesinos Real-Time Automatic Cloud Detection Using a Low-Cost Sky Camera Reprinted from: Remote Sens. 2020, 12, 1382, doi:10.3390/rs12091382 ................. 43 Román Mondragón, Joaquín Alonso-Montesinos, David Riveros-Rosas, Mauro Valdés, Héctor Estévez, Adriana E. González-Cabrera and Wolfgang Stremme Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area Reprinted from: Remote Sens. 2020, 12, 1212, doi:10.3390/rs12071212 ................. 61 Jinwoong Park, Jihoon Moon, Seungmin Jung and Eenjun Hwang Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island Reprinted from: Remote Sens. 2020, 12, 2271, doi:10.3390/rs12142271 ................. 79 Guojiang Xiong, Jing Zhang, Dongyuan Shi, Lin Zhu, Xufeng Yuan and Gang Yao Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models Reprinted from: Remote Sens. 2019, 11, 2795, doi:10.3390/rs11232795 ................. 101 Alexandra I. Khalyasmaa, Stanislav A. Eroshenko, Valeriy A. Tashchilin, Hariprakash Ramachandran, Teja Piepur Chakravarthi and Denis N. Butusov Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning Reprinted from: Remote Sens. 2020, 12, 3420, doi:10.3390/rs12203420 ................. 125 Ian R. Young, Ebru Kirezci and Agustinus Ribal The Global Wind Resource Observed by Scatterometer Reprinted from: Remote Sens. 2020, 12, 2920, doi:10.3390/rs12182920 ................. 147 Susumu Shimada, Jay Prakash Goit, Teruo Ohsawa, Tetsuya Kogaki and Satoshi Nakamura Coastal Wind Measurements Using a Single Scanning LiDAR Reprinted from: Remote Sens. 2020, 12, 1347, doi:10.3390/rs12081347 ................. 165 Cristina Sáez Blázquez, Pedro Carrasco García, Ignacio Martín Nieto, MiguelAngel ´ Maté-González, Arturo Farfán Martín and Diego González-Aguilera Characterizing Geological Heterogeneities for Geothermal Purposes through Combined Geophysical Prospecting Methods Reprinted from: Remote Sens. 2020, 12, 1948, doi:10.3390/rs12121948 ................. 189 Miktha Farid Alkadri, Francesco De Luca, Michela Turrin and Sevil Sariyildiz A Computational Workflow for Generating A Voxel-Based Design Approach Based on Subtractive Shading Envelopes and Attribute Information of Point Cloud Data Reprinted from: Remote Sens. 2020, 12, 2561, doi:10.3390/rs12162561 ................. 207Instituto do Ma

GIS-based assessment for the potential of implementation of food-energy-water systems on building rooftops at the urban level

This research develops a bottom-up procedure to assess the potential of food-energy-water (FEW) systems on the rooftops of buildings in an urban district in Spain considering the urban morphology of the built environment and obtains accurate assessments of production and developmental patterns. A multicriteria decision-making technique implemented in a geographical information system (GIS) environment was used to extract suitable rooftop areas. To implement this method, the slope (tilt), aspect (azimuth), shading, and solar radiation of the rooftops were calculated using LiDAR (Light Detection and Ranging) data and building footprints. The potential of FEW system implementation was analysed at the building and morphology levels. The results showed several differences between residential and non-residential urban morphologies. Industrial areas contained the highest productivity for FEW systems. The production was 2.51 kg of tomatoes/m2, 48 kWh of photovoltaic energy/m2, and 0.16 l of rainwater/m2. Regarding the residential urban morphologies, the more compact tents resulted in better performance. Among the FEW systems, although water could best benefit from the features of the entire roof surface, the best production results were achieved by energy. The food system is less efficient in the built environment since it requires flat roofs. The methodology presented can be applied in any city, and it is considered optimal in the European context for the development of self-production strategies for urban environments

Photovoltaic potential in building façades

Tese de doutoramento, Sistemas Sustentáveis de Energia, Universidade de Lisboa, Faculdade de Ciências, 2018Consistent reductions in the costs of photovoltaic (PV) systems have prompted interest in applications with less-than-optimum inclinations and orientations. That is the case of building façades, with plenty of free area for the deployment of solar systems. Lower sun heights benefit vertical façades, whereas rooftops are favoured when the sun is near the zenith, therefore the PV potential in urban environments can increase twofold when the contribution from building façades is added to that of the rooftops. This complementarity between façades and rooftops is helpful for a better match between electricity demand and supply. This thesis focuses on: i) the modelling of façade PV potential; ii) the optimization of façade PV yields; and iii) underlining the overall role that building façades will play in future solar cities. Digital surface and solar radiation modelling methodologies were reviewed. Special focus is given to the 3D LiDAR-based model SOL and the CAD/plugin models DIVA and LadyBug. Model SOL was validated against measurements from the BIPV system in the façade of the Solar XXI building (Lisbon), and used to evaluate façade PV potential in different urban sites in Lisbon and Geneva. The plugins DIVA and LadyBug helped assessing the potential for PV glare from façade integrated photovoltaics in distinct urban blocks. Technologies for PV integration in façades were also reviewed. Alternative façade designs, including louvers, geometric forms and balconies, were explored and optimized for the maximization of annual solar irradiation using DIVA. Partial shading impacts on rooftops and façades were addressed through SOL simulations and the interconnections between PV modules were optimized using a custom Multi-Objective Genetic Algorithm. The contribution of PV façades to the solar potential of two dissimilar neighbourhoods in Lisbon was quantified using SOL, considering local electricity consumption. Cost-efficient rooftop/façade PV mixes are proposed based on combined payback times. Impacts of larger scale PV deployment on the spare capacity of power distribution transformers were studied through LadyBug and SolarAnalyst simulations. A new empirical solar factor was proposed to account for PV potential in future upgrade interventions. The combined effect of aggregating building demand, photovoltaic generation and storage on the self-consumption of PV and net load variance was analysed using irradiation results from DIVA, metered distribution transformer loads and custom optimization algorithms. SOL is shown to be an accurate LiDAR-based model (nMBE ranging from around 7% to 51%, nMAE from 20% to 58% and nRMSE from 29% to 81%), being the isotropic diffuse radiation algorithm its current main limitation. In addition, building surface material properties should be regarded when handling façades, for both irradiance simulation and PV glare evaluation. The latter appears to be negligible in comparison to glare from typical glaze/mirror skins used in high-rises. Irradiation levels in the more sunlit façades reach about 50-60% of the rooftop levels. Latitude biases the potential towards the vertical surfaces, which can be enhanced when the proportion of diffuse radiation is high. Façade PV potential can be increased in about 30% if horizontal folded louvers becomes a more common design and in another 6 to 24% if the interconnection of PV modules are optimized. In 2030, a mix of PV systems featuring around 40% façade and 60% rooftop occupation is shown to comprehend a combined financial payback time of 10 years, if conventional module efficiencies reach 20%. This will trigger large-scale PV deployment that might overwhelm current grid assets and lead to electricity grid instability. This challenge can be resolved if the placement of PV modules is optimized to increase self-sufficiency while keeping low net load variance. Aggregated storage within solar communities might help resolving the conflicting interests between prosumers and grid, although the former can achieve self-sufficiency levels above 50% with storage capacities as small as 0.25kWh/kWpv. Business models ought to adapt in order to create conditions for both parts to share the added value of peak power reduction due to optimized solar façades.Fundação para a Ciência e a Tecnologia (FCT), SFRH/BD/52363/201