An Energy-Related CityGML ADE and Its Application for Heating Demand Calculation

2015 ◽  
Vol 4 (3) ◽  
pp. 59-77 ◽  
Author(s):  
Marcel Bruse ◽  
Romain Nouvel ◽  
Parag Wate ◽  
Volker Kraut ◽  
Volker Coors
Keyword(s):  
Energy Demand ◽  
Building Energy ◽  
Data Sets ◽  
Application Domain ◽  
Data Formats ◽  
Building Energy Systems ◽  
Domain Extension ◽  
Energy Simulations ◽  
Heating Energy Demand ◽  
City Models

Different associated properties of city models like building geometries, building energy systems, building end uses, and building occupant behavior are usually saved in different data formats and are obtained from different data sources. Experience has shown that the integration of these data sets for the purpose of energy simulation on city scale is often cumbersome and error prone. A new application domain extension for CityGML has been developed in order to integrate energy-related figures of buildings, thermal volumes, and facades with their geometric descriptions. These energy-related figures can be parameters or results of energy simulations. The applicability of the new application domain extension has been demonstrated for heating energy demand calculation.

An Energy-Related CityGML ADE and Its Application for Heating Demand Calculation

2019 ◽  
pp. 1306-1323
Author(s):  
Marcel Bruse ◽  
Romain Nouvel ◽  
Parag Wate ◽  
Volker Kraut ◽  
Volker Coors
Keyword(s):  
Energy Demand ◽  
Building Energy ◽  
Energy Simulation ◽  
Data Sets ◽  
Application Domain ◽  
Data Formats ◽  
Building Energy Systems ◽  
Domain Extension ◽  
Energy Simulations ◽  
Heating Energy Demand

Different associated properties of city models like building geometries, building energy systems, building end uses, and building occupant behavior are usually saved in different data formats and are obtained from different data sources. Experience has shown that the integration of these data sets for the purpose of energy simulation on city scale is often cumbersome and error prone. A new application domain extension for CityGML has been developed in order to integrate energy-related figures of buildings, thermal volumes, and facades with their geometric descriptions. These energy-related figures can be parameters or results of energy simulations. The applicability of the new application domain extension has been demonstrated for heating energy demand calculation.

scholarly journals Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets

2020 ◽  
Vol 12 (17) ◽  
pp. 6788 ◽  
Author(s):  
Eva Lucas Segarra ◽  
Germán Ramos Ruiz ◽  
Vicente Gutiérrez González ◽  
Antonis Peppas ◽  
Carlos Fernández Bandera
Keyword(s):  
Energy Demand ◽  
Meteorological Data ◽  
Building Energy ◽  
Third Party ◽  
Weather Data ◽  
Data Sets ◽  
Weather Station ◽  
Energy Models ◽  
The Impact ◽  
Energy Strategies

The use of building energy models (BEMs) is becoming increasingly widespread for assessing the suitability of energy strategies in building environments. The accuracy of the results depends not only on the fit of the energy model used, but also on the required external files, and the weather file is one of the most important. One of the sources for obtaining meteorological data for a certain period of time is through an on-site weather station; however, this is not always available due to the high costs and maintenance. This paper shows a methodology to analyze the impact on the simulation results when using an on-site weather station and the weather data calculated by a third-party provider with the purpose of studying if the data provided by the third-party can be used instead of the measured weather data. The methodology consists of three comparison analyses: weather data, energy demand, and indoor temperature. It is applied to four actual test sites located in three different locations. The energy study is analyzed at six different temporal resolutions in order to quantify how the variation in the energy demand increases as the time resolution decreases. The results showed differences up to 38% between annual and hourly time resolutions. Thanks to a sensitivity analysis, the influence of each weather parameter on the energy demand is studied, and which sensors are worth installing in an on-site weather station are determined. In these test sites, the wind speed and outdoor temperature were the most influential weather parameters.

scholarly journals CITYGML APPLICATION DOMAIN EXTENSION FOR 3D STRATA REPRESENTATIONS IN THE SMARTKADASTER SYSTEM: TOWARDS BEYOND CADASTRE PURPOSE IN MALAYSIA

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 99-103
Author(s):  
C. B. Siew ◽  
N. Z. Abdul Halim ◽  
H. Karim ◽  
M. A. Mohd Zain ◽  
K. S. Looi
Keyword(s):  
Spatial Data ◽  
Application Domain ◽  
Kuala Lumpur ◽  
Levels Of Detail ◽  
Emerging Trends ◽  
Digital Twins ◽  
Domain Extension ◽  
Data Output ◽  
3D City Modelling ◽  
City Models

Abstract. Recent advancements in 3D city modelling and emerging trends in implementing and realising Digital Twins motivate the Department of Survey and Mapping Malaysia (JUPEM) to develop and implement SmartKADASTER (SKiP) Phase 2. SmartKADASTER Phase I was a precursor to this system, and it primarily focused on applying two-dimensional (2D) spatial data for 3D spatial analysis. CityGML was used as the data model for various Levels of Detail (LoD) in this new initiative to represent city models across the Greater Kuala Lumpur region. SmartKADASTER however, lacks strata information. Therefore, to integrate strata information into the SKiP citymodel environment, an Application Domain Extension (ADE) for CityGML has been developed to convert existing Strata XML to StrataGML, a CityGML-compliant data output format. This paper describes the purpose of the SmartKADASTER initiative in Section 1. Section 2 explains additional context for the initiative as well as some backgrounds. Section 3 discusses the conversion workflow and ADE definitions, followed by a brief discussion of visualisation in Section 4 and a project summary in Section 5.

scholarly journals Hacia un modelo dinámico para la isla de calor urbana de Madrid = Towards a Dynamic Model for the Urban Heat Island of Madrid.

2016 ◽  
Vol 2 (1) ◽  
pp. 49 ◽  
Author(s):  
Miguel Núñez Peiró ◽  
Emilia Román López ◽  
Carmen Sánchez-Guevara Sánchez ◽  
Francisco Javier Neila González
Keyword(s):  
Urban Heat Island ◽  
Predictive Model ◽  
Energy Demand ◽  
Building Energy ◽  
Energy Performance ◽  
Heat Island ◽  
Urban Heat ◽  
Weather Stations ◽  
Energy Simulations ◽  
The City

Resumen Esta investigación se enmarca dentro del proyecto MODIFICA (modelo predictivo - Edificios - Isla de Calor Urbano), financiado por el Programa de I + D + i Orientada a los Retos de la sociedad 'Retos Investigación' de 2013. Está dirigido a desarrollar un modelo predictivo de eficiencia energética para viviendas, bajo el efecto de isla de calor urbano (AUS) con el fin de ponerla en práctica en la evaluación de la demanda de energía real y el consumo en las viviendas. A pesar de los grandes avances que se han logrado durante los últimos años en el rendimiento energético de edificios, los archivos de tiempo utilizados en la construcción de simulaciones de energía se derivan generalmente de estaciones meteorológicas situadas en las afueras de la ciudad. Por lo tanto, el efecto de la Isla de Calor Urbano (ICU) no se considera en estos cálculos, lo que implica una importante falta de precisión. Centrado en explorar cómo incluir los fenómenos ICU, el presente trabajo recopila y analiza la dinámica por hora de la temperatura en diferentes lugares dentro de la ciudad de Madrid. Abstract This research is framed within the project MODIFICA (Predictive model - Buildings - Urban Heat Island), funded by Programa de I+D+i orientada a los retos de la sociedad 'Retos Investigación' 2013. It is aimed at developing a predictive model for dwelling energy performance under the Urban Heat Island (UHI) effect in order to implement it in the evaluation of real energy demand and consumption in dwellings. Despite great advances on building energy performance have been achieved during the last years, weather files used in building energy simulations are usually derived from weather stations placed in the outskirts of the city. Hence, Urban Heat Island (UHI) effect is not considered in this calculations, which implies an important lack of accuracy. Focused on exploring how to include the UHI phenomena, the present paper compiles and analyses the hourly dynamics of temperature in different locations within the city of Madrid. 

scholarly journals Grid-Aware Layout of Photovoltaic Panels in Sustainable Building Energy Systems

2021 ◽  
Vol 8 ◽  
Author(s):  
Luise Middelhauve ◽  
Francesco Baldi ◽  
Paul Stadler ◽  
François Maréchal
Keyword(s):  
Energy Demand ◽  
Power Grid ◽  
Renewable Energy Sources ◽  
Energy Systems ◽  
Energy Generation ◽  
Building Energy ◽  
Solar Irradiation ◽  
Mixed Integer ◽  
Photovoltaic Panels ◽  
Building Energy Systems

In the context of increasing concern for anthropogenic CO2 emissions, the residential building sector still represents a major contributor to energy demand. The integration of renewable energy sources, and particularly of photovoltaic (PV) panels, is becoming an increasingly widespread solution for reducing the carbon footprint of building energy systems (BES). However, the volatility of the energy generation and its mismatch with the typical demand patterns are cause for concern, particularly from the viewpoint of the management of the power grid. This paper aims to show the influence of the orientation of photovoltaic panels in designing new BES and to provide support to the decision making process of optimal PV placing. The subject is addressed with a mixed integer linear optimization problem, with costs as objectives and the installation, tilt, and azimuth of PV panels as the main decision variables. Compared with existing BES optimization approaches reported in literature, the contribution of PV panels is modeled in more detail, including a more accurate solar irradiation model and the shading effect among panels. Compared with existing studies in PV modeling, the interaction between the PV panels and the remaining units of the BES, including the effects of optimal, scheduling is considered. The study is based on data from a residential district with 40 buildings in western Switzerland. The results confirm the relevant influence of PV panels’ azimuth and tilt on the performance of BES. Whereas south-orientation remains the most preferred choice, west-orientationed panels better match the demand when compared with east-orientationed panels. Apart from the benefits for individual buildings, an appropriate choice of orientation was shown to benefit the grid: rotating the panels 20° westwards can, together with an appropriate scheduling of the BES, reduce the peak power of the exchange with the power grid by 50% while increasing total cost by only 8.3%. Including the more detailed modeling of the PV energy generation demonstrated that assuming horizontal surfaces can lead to inaccuracies of up to 20% when calculating operating expenses and electricity generated, particularly for high levels of PV penetration.

scholarly journals Coupling simulation with artificial neural networks for the optimisation of HVAC controls in manufacturing environments

2020 ◽  
Author(s):  
Victoria Jayne Mawson ◽  
Ben Richard Hughes
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Energy Demand ◽  
Large Data ◽  
Electrical Energy ◽  
Building Energy ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Unseen Data ◽  
Manufacturing Environments

Abstract Manufacturing remains one of the most energy intensive sectors, additionally, the energy used within buildings for heating, ventilation and air conditioning (HVAC) is responsible for almost half of the UK’s energy demand. Commonly, these are analysed in isolation from one another. Use of machine learning is gaining popularity due to its ability to solve non-linear problems with large data sets and little knowledge about relationships between parameters. Such models use relationships between inputs and outputs to make further predictions on unseen data, without requiring any understanding regarding the system, making them highly suited to dealing with the stochastic data sets found in a manufacturing environment. This has been seen in literature for determining electrical energy demand for residential or commercial buildings, rather than manufacturing environments. This study proposes a novel method of coupling simulation with machine learning to predict indoor workshop conditions and building energy demand, in response to production schedules, outdoor conditions, building behaviour and use. Such predictions can subsequently allow for more efficient management of HVAC systems. Based upon predicted energy consumption, potential spikes were identified and manufacturing schedules subsequently optimised to reduce peak energy demand. Coupling simulation techniques with machine learning algorithms eliminates the requirement for costly and intrusive methods of data collection, providing a method of predicting and optimising building energy consumption in the manufacturing sector.

scholarly journals An energy-economic analysis of real-world hybrid building energy systems

2021 ◽  
Vol 2042 (1) ◽  
pp. 012095
Author(s):  
Parantapa Sawant ◽  
Christian Braasch ◽  
Manuel Koch ◽  
Adrian Bürger ◽  
Sonja Kallio
Keyword(s):  
Control Strategies ◽  
Energy Systems ◽  
Building Energy ◽  
Primary Energy ◽  
Economic Feasibility ◽  
Data Sets ◽  
Hybrid Energy ◽  
Operation Conditions ◽  
Energy Factors ◽  
Building Energy Systems

Abstract A coordinated operation of decentralised micro-scale hybrid energy systems within a locally managed network such as a district or neighbourhood will play a significant role in the sector-coupled energy grid of the future. A quantitative analysis of the effects of the primary energy factors, energy conversion efficiencies, load profiles, and control strategies on their energy-economic balance can aid in identifying important trends concerning their deployment within such a network. In this contribution, an analysis of the operational data from five energy laboratories in the trinational Upper-Rhine region is evaluated and a comparison to a conventional reference system is presented. Ten exemplary data-sets representing typical operation conditions for the laboratories in different seasons and the latest information on their national energy strategies are used to evaluate the primary energy consumption, CO2 emissions, and demand-related costs. Various conclusions on the ecologic and economic feasibility of hybrid building energy systems are drawn to provide a toe-hold to the engineering community in their planning and development.

scholarly journals VOLUMINATOR 2.0 – SPEEDING UP THE APPROXIMATION OF THE VOLUME OF DEFECTIVE 3D BUILDING MODELS

2016 ◽  
Vol III-2 ◽  
pp. 29-36 ◽  
Author(s):  
M. Sindram ◽  
T. Machl ◽  
H. Steuer ◽  
M. Pültz ◽  
T. H. Kolbe
Keyword(s):  
Energy Demand ◽  
Computation Time ◽  
Building Energy ◽  
Common Information ◽  
Topological Relationships ◽  
Building Models ◽  
Thematic Information ◽  
Data Source ◽  
Topological Errors ◽  
City Models

Semantic 3D city models are increasingly used as a data source in planning and analyzing processes of cities. They represent a virtual copy of the reality and are a common information base and source of information for examining urban questions. A significant advantage of virtual city models is that important indicators such as the volume of buildings, topological relationships between objects and other geometric as well as thematic information can be derived. Knowledge about the exact building volume is an essential base for estimating the building energy demand. In order to determine the volume of buildings with conventional algorithms and tools, the buildings may not contain any topological and geometrical errors. The reality, however, shows that city models very often contain errors such as missing surfaces, duplicated faces and misclosures. To overcome these errors (Steuer et al., 2015) have presented a robust method for approximating the volume of building models. For this purpose, a bounding box of the building is divided into a regular grid of voxels and it is determined which voxels are inside the building. The regular arrangement of the voxels leads to a high number of topological tests and prevents the application of this method using very high resolutions. In this paper we present an extension of the algorithm using an octree approach limiting the subdivision of space to regions around surfaces of the building models and to regions where, in the case of defective models, the topological tests are inconclusive. We show that the computation time can be significantly reduced, while preserving the robustness against geometrical and topological errors.

scholarly journals Development of a Modelica-based simplified building model for district energy simulations

2021 ◽  
Vol 2042 (1) ◽  
pp. 012078
Author(s):  
Alessandro Maccarini ◽  
Enrico Prataviera ◽  
Angelo Zarrella ◽  
Alireza Afshari
Keyword(s):  
Urban Areas ◽  
Energy Demand ◽  
Building Energy ◽  
District Energy ◽  
Building Model ◽  
Computational Speed ◽  
Urban Simulation ◽  
Energy Simulations ◽  
Building Behaviour ◽  
Development And Validation

Abstract Urban Building Energy Simulation (UBES) is an efficient tool to investigate and subsequently reduce energy demand of urban areas. Nevertheless, UBES has always been a challenging task due the trade-off between accuracy, computational speed and parametrization. In order to reduce these computation and parameterization requirements, model reduction and simplification methods aim at representing building behaviour with an acceptable accuracy, but using less equations and input parameters. This paper presents the development and validation results of a simplified urban simulation model based on the ISO 13790 Standard and written in the Modelica language. The model describes the thermo-physical behaviour of buildings by means of an equivalent electric network consisting of five resistances and one capacitance. The validation of the model was carried out using four cases of the ANSI/ASHRAE Standard 140. In general, the model shows good accuracy and the validation provided values within the acceptable ranges.

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