Database Building Research Articles

Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform

The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. However, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to collect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, monitoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an experimental electrochemical reactor that reduces carbon dioxide (CO2) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.

Identification of undocumented buildings in cadastral data using remote sensing: Construction period, morphology, and landscape

Buildings are the predominant objects that characterize the urban structure. For many cities, local governments establish building databases for administration as well as urban planning and monitoring. However, newly constructed buildings are often only included with a considerable time delay in the official digital cadastral maps due to processes in the acquisition of data, so-called undocumented buildings. In this regard, detecting undocumented buildings using remote sensing techniques would support the construction of update-to-date building databases with complementary information. In-depth studies on undocumented buildings and their number and location, however, are scarce. Therefore, we exploit a deep learning-based framework to detect undocumented buildings in remote sensing data and propose to derive 2D and 3D morphological parameters as well as landscape metrics., which are capable of depicting the physical forms and spatial structures of undocumented buildings. Furthermore, we exemplify the variabilities of undocumented buildings across space by the differences in morphology and landscape metrics between high and low building density regions. Upon analysis of undocumented buildings in 15 cities in the state of Bavaria, Germany, both state- and city-scale results reveal that most undocumented buildings are located in lower dense regions. This reveals that fragmentation of the landscape by building structures in the state of Bavaria is probably greater than official geospatial data currently documented.

Atomic and Molecular Databases Open Science for a sustainable world

AbstractThe building of online atomic and molecular databases for astrophysics and for other research fields started with the beginning of the internet. These databases have encompassed different forms: databases of individual research groups exposing their own data, databases providing collected data from the refereed literature, databases providing evaluated compilations, databases providing repositories for individuals to deposit their data, and so on. They were, and are, the replacement for literature compilations with the goal of providing more complete and in particular easily accessible data services to the users communities. Such initiatives involve not only scientific work on the data, but also the characterization of data, which comes with the “standardization” of metadata and of the relations between metadata, as recently developed in different communities. This contribution aims at providing a representative overview of the atomic and molecular databases ecosystem, which is available to the astrophysical community and addresses different issues linked to the use and management of data and databases. The information provided in this paper is related to the keynote lecture “Atomic and Molecular Databases: Open Science for better science and a sustainable world” whose slides can be found at DOI : doi.org/10.5281/zenodo.6979352 on the Zenodo repository connected to the “cb5-labastro” Zenodo Community (https://zenodo.org/communities/cb5-labastro).

SASMOTE: A Self-Attention Oversampling Method for Imbalanced CSI Fingerprints in Indoor Positioning Systems.

WiFi localization based on channel state information (CSI) fingerprints has become the mainstream method for indoor positioning due to the widespread deployment of WiFi networks, in which fingerprint database building is critical. However, issues, such as insufficient samples or missing data in the collection fingerprint database, result in unbalanced training data for the localization system during the construction of the CSI fingerprint database. To address the above issue, we propose a deep learning-based oversampling method, called Self-Attention Synthetic Minority Oversampling Technique (SASMOTE), for complementing the fingerprint database to improve localization accuracy. Specifically, a novel self-attention encoder-decoder is firstly designed to compress the original data dimensionality and extract rich features. The synthetic minority oversampling technique (SMOTE) is adopted to oversample minority class data to achieve data balance. In addition, we also construct the corresponding CSI fingerprinting dataset to train the model. Finally, extensive experiments are performed on different data to verify the performance of the proposed method. The results show that our SASMOTE method can effectively solve the data imbalance problem. Meanwhile, the improved location model, 1D-MobileNet, is tested on the balanced fingerprint database to further verify the excellent performance of our proposed methods.

Gender Representation in Orthopaedic Surgery: A Geospatial Analysis From 2015 to 2022.

IntroductionThe gender disparity in orthopaedic surgery is well-established. According to our analysis, only 7.4% of practicing orthopaedic surgeons in the US are female in 2022. While there are several theories attempting to explain this gender gap, our eight years of data show that limited female representation is a self-perpetuating cycle as areas without female representation almost never improve in that regard. It appears that existing female mentorship is critical to the growth of a female orthopaedic presence in an area. In the present work, we aim to describe how gender diversity in orthopaedic surgery differs across the country, how this diversity is changing over time, and how surgeon gender diversity may be affected by the sociodemographic characteristics making up the counties where orthopaedic surgery is practiced.MethodsA retrospective study was conducted using publicly available National Provider Identifier (NPI) data from 2015 to 2022. Orthopaedic surgeons and their genders were identified using the Provider Type and Gender data elements associated with an individual NPI. Rural-urban and metro characters were defined using the USDA Economic Research Reserve’s rural-urban continuum codes. Python was used for database building and data cleaning. GeoDa, a statistical map-based graphing software, was used to plot and assess demographic, geographic, and socioeconomic trends. Trends in gender diversity from 2015 to 2019 were analyzed for each individual year as well as the time period as an aggregate. Cluster analysis was performed to assess complex spatial patterns of variables that could not be condensed linearly or logarithmically. Moran’s I was used to measure the similarity of a Federal Information Processing System (FIPS) area code to its neighbors. Within the clustering analysis, spatial clusters were broken down into four groups of spatial outliers (High-High, High-Low, Low-High, and Low-Low) referencing a given area’s relationship with its neighbors. Factorial ANOVA between each of the four cluster types was performed using the variables provided in the article to identify significant demographic variables within the cluster analysis.ResultsThere are relative hotspots of gender diversity in the Northwest, Northeast, and Southwest with relative coldspots in the Midwest and Southern US. In counties that are considered gender diversity hotspots, the total population of orthopaedic surgeons increases by 0.94 each year while the population of female orthopaedic surgeons increases by 0.2, suggesting that in areas with high gender diversity, 4.7 male orthopaedic surgeons are joining practices for every 1.0 female. In areas with low gender diversity, the population of orthopaedic surgeons increases by 0.11 surgeons each year while the slope for an increase in female orthopaedic surgeons is 0.Conclusions Orthopaedic surgery lags behind other male-dominated surgical specialties in gender parity. Our analysis demonstrates that certain areas of the country including the Northwest, Northeast, and Arizona have improved gender diversity compared to the rest of the country. We also see that the rate of increase of female orthopaedic surgeons in the past seven years is highest in areas with more preexisting female orthopaedic surgeons, suggesting the importance of a “trailblazer” phenomenon in recruiting female surgeons.

On the Digital Path of the Protection of Folk Houses in Qiang Nationality Villages from the Perspective of Digital Media Technology

In order to better adapt to the development nationalities of the digital media era and promote the inheritance path innovation of traditional nationality cultural resources, this study takes Qiang nationality village houses as the research object and takes the digital media era as the research background to deeply analyze the inheritance and protection path of Qiang nationality village house culture in the digital era. Therefore, this paper proposes a method of digital protection path of minority residential buildings based on point cloud technology and BIM Technology. This method constructs the digital resource database of village residential buildings by constructing the BIM model and the three-dimensional model of point cloud technology and then constructs the building information model based on traditional residential buildings from the data of building model, graphic influence, and cultural attributes, so as to provide technical support for the protection, repair, and reconstruction of ethnic minority buildings and the inheritance of ethnic culture.

A Myopic Normative Database for Retinal Nerve Fiber Layer Thickness Using Optical Coherence Tomography.

The purpose of this study was to determine changes in optical coherence tomography (OCT) color codes after applying a myopic normative database. The diagnostic performance of the retinal nerve fiber layer analysis improved with the use of this database. To evaluate the peripapillary retinal nerve fiber layer (pRNFL) OCT color codes based on a newly generated myopic normative database in comparison to the built-in normative database. A total of 371 subjects were included in this validation study in an attempt to generate a myopic normative database. Eighty myopic glaucomatous and 80 myopic healthy eyes were evaluated to determine the diagnostic performance of this database. The distribution of the color codes was investigated among the groups with reference to the built-in and myopic normative databases, and the 2 databases were compared in terms of abnormal color code frequency. The diagnostic performance of the myopic database was presented with sensitivity, specificity and area under the receiver operating characteristic curve values. The agreement between the databases decreased with increasing myopia degree. The distribution of the color codes of the built-in software significantly differed among the study groups in all sectors ( P =0.009 for the temporal sector and P <0.001 for the remaining sectors). When the myopic database was used, there were no longer significant differences among the groups for the temporosuperior, temporoinferior, temporal, and nasal sectors ( P =0.561, 0.299, 0.201, and 0.089, respectively). After applying the myopic normative database, the specificity of the pRNFL color codes increased from 70.1% to 90.2%, and the area under the receiver operating characteristic curve value from 0.851 to 0.945. The use of a myopic normative database for pRNFL using SD-OCT significantly decreased differences among myopia severity groups, and may help to more reliably assess glaucoma in myopic eyes.

Analysis of the sidechain structures of amino acids and peptides and a deduced method for the efficient search of peptide conformations

To reduce the dimensionality of the potential energy surface (PES) necessary for finding the low energy conformations (LECs) of peptides, the sidechain dihedral angles (DAs) of 3548 LECs of 17 amino acids (AAs) and related peptides are examined. The analysis reveals that most DAs can be discretized into 3 states. Moreover, the fact that many DA combinations are absent in the LECs is utilized to construct a new conformational search method named as the TM method. For a peptide with N AA residues, the TM method is 1.5N times faster than considering all DA combinations, and 4.7N times faster than the conventional Newman projection method. The reliability of the TM results is confirmed through testing on 11 dipeptides and 2 tetrapeptides. The much reduced PES size of the TM method allows the database building of diverse peptide structures, and enhances the reliability of various stochastic sampling based methods.

Building clustering for regional seismic response and damage analysis

This article presents a framework to cluster buildings into typologically similar groups and select indicator buildings for regional seismic response and damage analysis. The framework requires a robust database of buildings to provide high-level structural and site information of buildings. Here, a database of 234 reinforced concrete buildings with five or more above-ground stories in the central business district of Wellington, New Zealand, has been selected as the case study of this research. First, key structural and site parameters that contribute to the seismic demand, response, and damage of each building are extracted from the database. Extracted parameters comprise three numerical and five categorical attributes of each building, including the year of construction, height, period, lateral load resisting system, floor system, site subsoil class, importance level, and strong motion station. Next, two prominent unsupervised machine learning clustering approaches are utilized to cluster the mixed categorical and numerical building database: k-prototype on the mixed numerical and categorical database and k-means on principal components numerical subspace adopted from factor analysis of mixed data (FAMD). A novel autoencoder deep learning neural network is also designed and trained to convert the mixed data into a low-dimensional subspace called latent space and feed this into k-means for clustering. The proposed autoencoder method is demonstrated to be more effective at clustering buildings into useful typological clusters for seismic response and damage analysis based on multiple criteria from both data-science and engineering perspectives. The details of selected indicator buildings for each similar seismic vulnerability cluster are also represented.

(Invited) Accelerating Material Design for Aqueous Organic Redox Flow Batteries

Aqueous soluble organic (ASO) redox-active materials have recently shown great promise as alternatives to transition metal ions to be employed as energy-bearing active materials in redox flow batteries for large-scale energy storage because of their structural tunability, cost-effectiveness, availability, and safety features. Development so far however has been limited to a small palette of organics that are aqueous soluble. How to quickly identify and design organic molecules for the targeted properties became a critical challenge in accelerating the aqueous organic flow battery development.In this presentation, a data-driven material design process is discussed. This process incorporates database building, machine learning modeling for properties prediction, and the subsequent material synthesis and testing. Examples including Fluorenone and Phenizane derivative development for the flow battery application will be discussed.Reference: Feng et al., Science 372, 836–840 (2021)

Tablet inscription recognition based on the convolutional neural network

Text image recognition has opened up a new path for intelligent text sorting and research. The number of epitaph inscriptions is very large and of great value. Due to the limitation of objective conditions, there are many deficiencies in the study of its text arrangement and it is urgent to adopt new methods and new technologies to overcome them. This paper attaches great importance to the design of an automatic recognition algorithm for tablets based on the convolutional neural network. It also describes the application prospects of using the algorithm for the reading and distinguishing of inscriptions, the sorting of inscriptions, and the building of intelligent databases.

A geometrical approach to matching raster & vector databases of buildings

ABSTRACT This study outlines a new approach to matching raster and vector datasets using 3D geometries . The method includes several procedures based on the similarity of selected pixels of an image to 3D polygons. The method provides four results of matching for each pixel with respect to the polygons The results of the application to a set of buildings included in a DSM and polygons digitised from a city model have demonstrated the viability of the method for matching features. The results have shown that the method is efficient for conflating tasks such as integrating data, updating and change detection.

Tall Buildings

This article aims to identify different terms and concepts regarding tall buildings and evaluate the presence and hierarchy these buildings have on a global level. The methodology used considered the global database of tall buildings published by the Council on Tall Buildings and Urban Habitat. Six essential regions of the world were analyzed and classified as clusters. Forty-eight buildings were quantitatively described. As a result, thirteen terms and eighteen different concepts were found. The spatial configurations in terms of height and year of construction of each building, defined the population trends in the cities that make up each cluster. This research confirms a significant diversity of criteria for naming buildings in height. Tall buildings occupied by offices predominate in the clusters evaluated. Residential usage is not the most common occupation.

Portable Digital Linear Ion Trap Mass Spectrometer Based on Separate-Region Corona Discharge Ionization Source for On-Site Rapid Detection of Illegal Drugs.

As narcotic control has become worse in the past decade and the death toll of drug abuse hits a record high, there is an increasing demand for on-site rapid detection of illegal drugs. This work developed a portable digital linear ion trap mass spectrometer based on separate-region corona discharge ionization source to meet this need. A separate design of discharge and reaction regions was adopted with filter air as both carrier gas for the analyte and protection of the corona discharge needle. The linear ion trap was driven by a digital waveform with a low voltage (±100 V) to cover a mass range of 50–500 Da with a unit resolution at a scan rate of 10,000 Da/s. Eighteen representative drugs were analyzed, demonstrating excellent qualitative analysis capability. Tandem mass spectrometry (MS/MS) was also performed by ion isolation and collision-induced dissociation (CID) with air as a buffer gas. With cocaine as an example, over two orders of magnitude dynamic range and 10 pg of detection limit were achieved. A single analysis time of less than 10 s was obtained by comparing the information of characteristic ions and product ions with the built-in database. Analysis of a real-world sample further validated the feasibility of the instrument, with the results benchmarked by GC-MS. The developed system has powerful analytical capability without using consumables including solvent and inert gas, meeting the requirements of on-site rapid detection applications.

PDAUG: a Galaxy based toolset for peptide library analysis, visualization, and machine learning modeling

BackgroundComputational methods based on initial screening and prediction of peptides for desired functions have proven to be effective alternatives to lengthy and expensive biochemical experimental methods traditionally utilized in peptide research, thus saving time and effort. However, for many researchers, the lack of expertise in utilizing programming libraries, access to computational resources, and flexible pipelines are big hurdles to adopting these advanced methods.ResultsTo address the above mentioned barriers, we have implemented the peptide design and analysis under Galaxy (PDAUG) package, a Galaxy-based Python powered collection of tools, workflows, and datasets for rapid in-silico peptide library analysis. In contrast to existing methods like standard programming libraries or rigid single-function web-based tools, PDAUG offers an integrated GUI-based toolset, providing flexibility to build and distribute reproducible pipelines and workflows without programming expertise. Finally, we demonstrate the usability of PDAUG in predicting anticancer properties of peptides using four different feature sets and assess the suitability of various ML algorithms.ConclusionPDAUG offers tools for peptide library generation, data visualization, built-in and public database peptide sequence retrieval, peptide feature calculation, and machine learning (ML) modeling. Additionally, this toolset facilitates researchers to combine PDAUG with hundreds of compatible existing Galaxy tools for limitless analytic strategies.

Hemostatic efficacy of tranexamic acid and ε-aminocaproic acid in hip and knee arthroplasty:a Meta-analysis

To systematically evaluate the hemostatic efficacy of tranexamic acid and ε-aminocaproic acid in total hip arthroplasty (THA) and total knee arthroplasty (TKA). Randomized controlled trials (RCT) and retrospective case-control studies about tranexamic acid and ε-aminocaproic acid for the comparison of THA or TKA were searched electronically in PubMed, EMbase, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang, VIP from the time of building databases to July 2020. Two investigators carried out literature screening and data extraction according to the inclusion and exclusion criteria respectively. The methodological quality of the included randomized controlled studies was evaluated through the Cochrane Handbook, and the methodological quality of the included retrospective case-control studies was evaluated through the NOS scale. Blood loss, the incidence of thrombosis complications, per capita input of hemoglobin were Meta-analyzed by Review Manager 5.3 software. A total of 6 articles were included, including 4 RCTs and 2 retrospective case-control studies. A total of 3 174 patients, including 1 353 in the tranexamic acid group and 1 821 in the ε-aminocaproic acid group. Meta-analysis results showed that there were no difference statistical significance in blood loss [MD=-88.60, 95%CI(-260.30, 83.10), P=0.31], blood transfusion rate [OR=1.48, 95%CI(0.96, 2.27), P=0.08], thrombotic complications [OR=0.80, 95%CI(0.07, 8.83), P=0.85], per capita hemoglobin input [MD=0.04, 95%CI(-0.02, 0.10), P=0.18] between tranexamic acid group and ε-aminocaproic acid group during THA. While in TKA, the blood loss of the tranexamic acid group was less than that of the ε-aminocaproic acid group [MD=-147.13, 95%CI(-216.52, -77.74), P<0.0001], the difference was statistically significant. The blood transfusion rate [OR=1.30, 95%CI(0.74, 2.28), P=0.37], thrombotic complications [OR=0.95, 95%CI(0.38, 2.36), P=0.92], per capita hemoglobin input [MD=-0.00, 95%CI(-0.05, 0.06), P=0.48], tourniquet time [MD=1.54, 95%CI(-2.07, 5.14), P=0.40] were similar between two groups, the difference was not statistically significant. In THA, tranexamic acid and ε-aminocaproic acid have similar hemostatic effects, while in TKA, tranexamic acid can effectively reduce the patient's blood loss and has a better hemostatic effect. Tranexamic acid is recommended as one of the first choice hemostatic drugs for TKA.

Towards a global One Health index: a potential assessment tool for One Health performance

BackgroundA One Health approach has been increasingly mainstreamed by the international community, as it provides for holistic thinking in recognizing the close links and inter-dependence of the health of humans, animals and the environment. However, the dearth of real-world evidence has hampered application of a One Health approach in shaping policies and practice. This study proposes the development of a potential evaluation tool for One Health performance, in order to contribute to the scientific measurement of One Health approach and the identification of gaps where One Health capacity building is most urgently needed.MethodsWe describe five steps towards a global One Health index (GOHI), including (i) framework formulation; (ii) indicator selection; (iii) database building; (iv) weight determination; and (v) GOHI scores calculation. A cell-like framework for GOHI is proposed, which comprises an external drivers index (EDI), an intrinsic drivers index (IDI) and a core drivers index (CDI). We construct the indicator scheme for GOHI based on this framework after multiple rounds of panel discussions with our expert advisory committee. A fuzzy analytical hierarchy process is adopted to determine the weights for each of the indicators.ResultsThe weighted indicator scheme of GOHI comprises three first-level indicators, 13 second-level indicators, and 57 third-level indicators. According to the pilot analysis based on the data from more than 200 countries/territories the GOHI scores overall are far from ideal (the highest score of 65.0 out of a maximum score of 100), and we found considerable variations among different countries/territories (31.8–65.0). The results from the pilot analysis are consistent with the results from a literature review, which suggests that a GOHI as a potential tool for the assessment of One Health performance might be feasible.ConclusionsGOHI—subject to rigorous validation—would represent the world’s first evaluation tool that constructs the conceptual framework from a holistic perspective of One Health. Future application of GOHI might promote a common understanding of a strong One Health approach and provide reference for promoting effective measures to strengthen One Health capacity building. With further adaptations under various scenarios, GOHI, along with its technical protocols and databases, will be updated regularly to address current technical limitations, and capture new knowledge.Graphical

Learning From Success, Not Catastrophe: Using Counterfactual Analysis to Highlight Successful Disaster Risk Reduction Interventions

In the aftermath of a disaster, news and research attention is focused almost entirely on catastrophic narratives and the various drivers that may have led to the disaster. Learning from failure is essential to preventing future disasters. However, hyperfixation on the catastrophe obscures potential successes at the local scale, which could serve as important examples and learning resources in effective risk mitigation. To highlight effective risk mitigation actions that would otherwise remain unnoticed, we propose the use of probabilistic downward counterfactual analysis. This approach uses counterfactual modelling of a past hazard event with consequences made worse (i.e. downward counterfactual) by the absence of the mitigation intervention. The approach follows probabilistic risk analysis procedures where uncertainties in the simulated events and outcomes are accounted for and propagated. We demonstrate the method using a case study of Nepal’s School Earthquake Safety Program, implemented before the 2015 Mw 7.8 Gorkha earthquake. Using a school building database for Kathmandu Valley, Nepal, we present two applications: 1) the quantification of lives saved during the Gorkha earthquake as a result of the retrofitting of schools in Kathmandu Valley since 1997, 2) the quantification of the annual expected lives saved if the pilot retrofitting program was extended to all school buildings in Kathmandu Valley based on a probabilistic seismic hazard model. The shift in focus from realised outcome to counterfactual alternative enables the quantification of the benefits of risk reduction programs amidst disaster, or for a hazard that has yet to unfold. Such quantified counterfactual analysis can be used to celebrate successful risk reduction interventions, providing important positive reinforcement to decision-makers with political bravery to commit to the implementation of effective measures.

Data‐Driven Approach to Designing 2D Van der Waals Heterostructures: Misjudgment of Band Alignment Type and Its Mechanism

Data‐driven approach, including database building and high‐throughput design, is rapidly applied to various fields, ranging from physics to materials. Predicting the band alignment type at interfaces, which is based on Anderson's rule (AR), is crucial for implementing the data‐driven approach in the field of 2D van der Waals heterostructures (vdWHs). The validity of the AR‐based data‐driven approach, however, is ambiguous, particularly when it involves band alignment. Based on a large‐scale calculation (1231 heterostructures), it is explicitly demonstrated that the current AR‐based data‐driven approach have prediction errors for certain band alignment type, including the inability to predict type‐other band alignment and the partial misclassification of type II band alignment as type III. Such prediction errors are quantitatively explained by geometric deformation, interlayer orbital coupling, and charge redistribution effects. As a result, the current data‐driven design approach is insufficiently reliable for type II, type III, and type‐other heterostructures, which may result in inaccurate or incomplete design results. Herein, a flaw in the current AR‐based data‐driven approach is elucidated as well as important information for reasonably designing 2D vdWHs is provided in general.

Designing a District Energy Infrastructure - a Case-Study in Lisbon

The paper describes a case study on the design of district energy infrastructure for the green-field project of the Vale de Santo António (Lisbon, Portugal). The methodology is based on a novel framework built on the sequential integration of three software tools: QGIS, City Energy Analyst (CEA) and Urbio. QGIS is used to define the building database (construction solutions, uses and schedules); CEA is used to model the neighborhood energy services (heating, cooling, domestic hot water and electricity, including for EVs); and Urbio is used to design the energy infrastructure to supply the neighborhood in an optimal manner. The study illustrates the high impact of building renovation on heat demand and the existing potential for a two-layout network to minimize pumping demand and heat distribution loss. Moreover, a sensitivity analysis with regard to future carbon tax changes and electricity greening concludes that an electricity-based heating mix is optimal.