scholarly journals A Data-Driven Energy Platform: From Energy Performance Certificates to Human-Readable Knowledge through Dynamic High-Resolution Geospatial Maps

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2132
Author(s):  
Tania Cerquitelli ◽  
Evelina Di Corso ◽  
Stefano Proto ◽  
Paolo Bethaz ◽  
Daniele Mazzarelli ◽  
...  
Keyword(s):  
Web Application ◽  
Residential Buildings ◽  
Open Data ◽  
Energy Performance ◽  
Data Driven ◽  
Energy Costs ◽  
Large Dataset ◽  
Model Generalization ◽  
Northwest Region ◽  
Temporal Granularity

The energy performance certificate (EPC) is a document that certifies the average annual energy consumption of a building in standard conditions and allows it to be classified within a so-called energy class. In a period such as this, when greenhouse gas emissions are of considerable importance and where the objective is to improve energy security and reduce energy costs in our cities, energy certification has a key role to play. The proposed work aims to model and characterize residential buildings’ energy efficiency by exploring heterogeneous, geo-referenced data with different spatial and temporal granularity. The paper presents TUCANA (TUrin Certificates ANAlysis), an innovative data mining engine able to cover the whole analytics workflow for the analysis of the energy performance certificates, including cluster analysis and a model generalization step based on a novel spatial constrained K-NN, able to automatically characterize a broad set of buildings distributed across a major city and predict different energy-related features for new unseen buildings. The energy certificates analyzed in this work have been issued by the Piedmont Region (a northwest region of Italy) through open data. The results obtained on a large dataset are displayed in novel, dynamic, and interactive geospatial maps that can be consulted on a web application integrated into the system. The visualization tool provides transparent and human-readable knowledge to various stakeholders, thus supporting the decision-making process.

scholarly journals A Data-driven Approach for Sustainable Building Retrofit—A Case Study of Different Climate Zones in China

2020 ◽  
Vol 12 (11) ◽  
pp. 4726 ◽  
Author(s):  
Qiong He ◽  
S. Thomas Ng ◽  
Md. Uzzal Hossain ◽  
Godfried L. Augenbroe
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Residential Buildings ◽  
Energy Performance ◽  
Data Driven ◽  
Total Energy Consumption ◽  
Climatic Zones ◽  
Climate Zones ◽  
Cold Zone ◽  
High Rise

This study presents a data-driven retrofitting approach by systematically analyzing the energy performance of existing high-rise residential buildings using a normative calculation logic-based simulation method. To demonstrate the practicality of the approach, typical existing buildings in five climate zones of China are analyzed based on the local building characteristics and climatic conditions. The results show that the total energy consumption is 544 kWh/m2/year in the severe cold zone, which is slightly higher than that in the cold zone (519 kWh/m2/year), but double that in the hot summer and cold winter zone, three times higher than that in the warm zone, and five times above that in the temperate zone. The dominant energy needs in different climatic zones are distinctive. The identified potentially suitable retrofitting measures are important in reducing large-scale energy consumption and can be used in supporting sustainable retrofit decisions for existing high-rise residential buildings in different climatic zones.

scholarly journals ANALYSIS OF THE AIRTIGHTNESS MEASUREMENT IN SINGLE OR DOUBLE APARTMENT HOUSE

2021 ◽  
Vol 13 (0) ◽  
pp. 1-6
Author(s):  
Rokas Klabis ◽  
Violeta Motuzienė ◽  
Rūta Mikučionienė
Keyword(s):  
Energy Efficiency ◽  
Energy Savings ◽  
Residential Buildings ◽  
Energy Performance ◽  
Energy Costs ◽  
Apartment House ◽  
Energy Saving Potential ◽  
Class D ◽  
New Buildings ◽  
Airtightness Measurement

The mandatory energy performance certification of new buildings or buildings for sale has been introduced in all Member States in order to achieve European Union’s energy efficiency goals. The certification of buildings sets mandatory requirements for higher energy efficiency buildings’ level of airtightness. However, a bigger problem lies in existing older residential buildings, which are energy inefficient and do not require certification. The unused potential for energy savings observed here is related to the airtightness of single and double apartment residential buildings and energy efficiency related to airtightness of them. Therefore, this work analyses the airtightness of energy class D and lower buildings based on actual airtightness measurements and evaluates the possible energy saving potential associated with the application of airtightness measures based on the example of one inefficient single apartment building. The results show that increase of the airtightness in such buildings to 3 h–1 enables to reduce the energy costs related to the airtightness in Lithuania over a period of 10 years by 0.17 TWh per year.

scholarly journals Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany

2021 ◽  
Author(s):  
Simon Wenninger ◽  
Christian Wiethe
Keyword(s):  
Support Vector Regression ◽  
Prediction Accuracy ◽  
Residential Buildings ◽  
Energy Performance ◽  
Engineering Method ◽  
Data Driven ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting ◽  
Artificial Neural

AbstractTo achieve ambitious climate goals, it is necessary to increase the rate of purposeful retrofit measures in the building sector. As a result, Energy Performance Certificates have been designed as important evaluation and rating criterion to increase the retrofit rate in the EU and Germany. Yet, today’s most frequently used and legally required methods to quantify building energy performance show low prediction accuracy, as recent research reveals. To enhance prediction accuracy, the research community introduced data-driven methods which obtained promising results. However, there are no insights in how far Energy Quantification Methods are particularly suited for energy performance prediction. In this research article the data-driven methods Artificial Neural Network, D-vine copula quantile regression, Extreme Gradient Boosting, Random Forest, and Support Vector Regression are compared with and validated by real-world Energy Performance Certificates of German residential buildings issued by qualified auditors using the engineering method required by law. The results, tested for robustness and systematic bias, show that all data-driven methods exceed the engineering method by almost 50% in terms of prediction accuracy. In contrast to existing literature favoring Artificial Neural Networks and Support Vector Regression, all tested methods show similar prediction accuracy with marginal advantages for Extreme Gradient Boosting and Support Vector Regression in terms of prediction accuracy. Given the higher prediction accuracy of data-driven methods, it seems appropriate to revise the current legislation prescribing engineering methods. In addition, data-driven methods could support different organizations, e.g., asset management, in decision-making in order to reduce financial risk and to cut expenses.

scholarly journals Developing the Gaois Linguistic Database of Irish-language Surnames

Names ◽  
2021 ◽  
Vol 69 (1) ◽  
pp. 10-19
Author(s):  
Brian Ó Raghallaigh ◽  
Michal Boleslav Měchura ◽  
Aengus Ó Fionnagáin ◽  
Sophie Osborne
Keyword(s):  
Data Structure ◽  
Web Application ◽  
Query Expansion ◽  
Open Data ◽  
Data Driven ◽  
Irish Language ◽  
Public Spheres ◽  
Authority Control ◽  
Online Resource ◽  
The Web

It is now commonplace to see surnames written in the Irish language in Ireland, yet there is no online resource for checking the standard spelling and grammar of Irish-language surnames. We propose a data structure for handling Irish-language surnames which comprises bilingual (Irish–English) clusters of surname forms. We present the first open, data-driven linguistic database of common Irish-language surnames, containing 664 surname clusters, and a method for deriving Irish-language inflected forms. Unlike other Irish surname dictionaries, our aim is not to list variants or explain origins, but rather to provide standard Irish-language surname forms via the web for use in the educational, cultural, and public spheres, as well as in the library and information sciences. The database can be queried via a web application, and the dataset is available to download under an open licence. The web application uses a comprehensive list of surname forms for query expansion. We envisage the database being applied to name authority control in Irish libraries to provide for bilingual access points.

scholarly journals Testing a Gypsum Composite Based on Raw Gypsum with a Direct Admixture of Paraffin and Polymer to Improve Thermal Properties

Materials ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3241
Author(s):  
Krzysztof Powała ◽  
Andrzej Obraniak ◽  
Dariusz Heim
Keyword(s):  
Thermal Conductivity ◽  
Heat Capacity ◽  
Thermal Properties ◽  
Phase Change ◽  
Large Scale ◽  
Building Materials ◽  
Residential Buildings ◽  
Energy Performance ◽  
Polymer Content ◽  
Volumetric Heat Capacity

The implemented new legal regulations regarding thermal comfort, the energy performance of residential buildings, and proecological requirements require the design of new building materials, the use of which will improve the thermal efficiency of newly built and renovated buildings. Therefore, many companies producing building materials strive to improve the properties of their products by reducing the weight of the materials, increasing their mechanical properties, and improving their insulating properties. Currently, there are solutions in phase-change materials (PCM) production technology, such as microencapsulation, but its application on a large scale is extremely costly. This paper presents a solution to the abovementioned problem through the creation and testing of a composite, i.e., a new mixture of gypsum, paraffin, and polymer, which can be used in the production of plasterboard. The presented solution uses a material (PCM) which improves the thermal properties of the composite by taking advantage of the phase-change phenomenon. The study analyzes the influence of polymer content in the total mass of a composite in relation to its thermal conductivity, volumetric heat capacity, and diffusivity. Based on the results contained in this article, the best solution appears to be a mixture with 0.1% polymer content. It is definitely visible in the tests which use drying, hardening time, and paraffin absorption. It differs slightly from the best result in the thermal conductivity test, while it is comparable in terms of volumetric heat capacity and differs slightly from the best result in the thermal diffusivity test.

scholarly journals Life Cycle GHG Emissions of Residential Buildings in Humid Subtropical and Tropical Climates: Systematic Review and Analysis

Buildings ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 6
Author(s):  
Daniel Satola ◽  
Martin Röck ◽  
Aoife Houlihan-Wiberg ◽  
Arild Gustavsen
Keyword(s):  
Systematic Review ◽  
Life Cycle ◽  
Residential Buildings ◽  
Construction Materials ◽  
Ghg Emissions ◽  
Energy Performance ◽  
Cycle Performance ◽  
Tropical Climates ◽  
Building Life Cycle ◽  
Total Life

Improving the environmental life cycle performance of buildings by focusing on the reduction of greenhouse gas (GHG) emissions along the building life cycle is considered a crucial step in achieving global climate targets. This paper provides a systematic review and analysis of 75 residential case studies in humid subtropical and tropical climates. The study investigates GHG emissions across the building life cycle, i.e., it analyses both embodied and operational GHG emissions. Furthermore, the influence of various parameters, such as building location, typology, construction materials and energy performance, as well as methodological aspects are investigated. Through comparative analysis, the study identifies promising design strategies for reducing life cycle-related GHG emissions of buildings operating in subtropical and tropical climate zones. The results show that life cycle GHG emissions in the analysed studies are mostly dominated by operational emissions and are the highest for energy-intensive multi-family buildings. Buildings following low or net-zero energy performance targets show potential reductions of 50–80% for total life cycle GHG emissions, compared to buildings with conventional energy performance. Implementation of on-site photovoltaic (PV) systems provides the highest reduction potential for both operational and total life cycle GHG emissions, with potential reductions of 92% to 100% and 48% to 66%, respectively. Strategies related to increased use of timber and other bio-based materials present the highest potential for reduction of embodied GHG emissions, with reductions of 9% to 73%.

scholarly journals Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland

2021 ◽  
Vol 13 (4) ◽  
pp. 1595
Author(s):  
Valeria Todeschi ◽  
Roberto Boghetti ◽  
Jérôme H. Kämpf ◽  
Guglielmina Mutani
Keyword(s):  
Machine Learning ◽  
Energy Use ◽  
Residential Buildings ◽  
Building Energy ◽  
Energy Performance ◽  
Space Heating ◽  
Engineering Model ◽  
Building Energy Use ◽  
The Impact ◽  
The City

Building energy-use models and tools can simulate and represent the distribution of energy consumption of buildings located in an urban area. The aim of these models is to simulate the energy performance of buildings at multiple temporal and spatial scales, taking into account both the building shape and the surrounding urban context. This paper investigates existing models by simulating the hourly space heating consumption of residential buildings in an urban environment. Existing bottom-up urban-energy models were applied to the city of Fribourg in order to evaluate the accuracy and flexibility of energy simulations. Two common energy-use models—a machine learning model and a GIS-based engineering model—were compared and evaluated against anonymized monitoring data. The study shows that the simulations were quite precise with an annual mean absolute percentage error of 12.8 and 19.3% for the machine learning and the GIS-based engineering model, respectively, on residential buildings built in different periods of construction. Moreover, a sensitivity analysis using the Morris method was carried out on the GIS-based engineering model in order to assess the impact of input variables on space heating consumption and to identify possible optimization opportunities of the existing model.

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