scholarly journals Comparative Analysis of the Dynamic Building Energy Simulation Tools

2017 ◽  
Vol 9 (4) ◽  
pp. 442-450 ◽  
Author(s):  
Vytautas Pajaujis ◽  
Violeta Motuzienė
Keyword(s):  
Energy Demand ◽  
Building Energy ◽  
Cooling Capacity ◽  
Energy Simulation ◽  
Simulation Tools ◽  
Building Model ◽  
Heating And Cooling ◽  
Energy Demands ◽  
Dynamic Energy Simulation ◽  
Heating Up

There are a lot of methodologies and simulation tools in the world to assess the energy demand of a building. The results of simulation tools often differ, but the causes are not analysed in more detail. The article compares the results of two most widely used dynamic energy simulation tools – DesignBuilder and IES-VE, when simulation of identical building model with the same assumptions in both programs is performed. In addition, for comparison, calculations are performed with the PHPP program, as well as using STR2.09.04:2008 methodology. The tools compare the heating, cooling capacity, energy consumption of the building for heating and cooling the building during the simulation. Following differences comparing energy demands gained with two different simulation tools are defined: ventilation – up to 11%, cooling – up to 9%, heating – up to 5%.

Building Energy Simulation with On-Site Weather Station

2016 ◽  
Vol 859 ◽  
pp. 88-92 ◽  
Author(s):  
Radu Manescu ◽  
Ioan Valentin Sita ◽  
Petru Dobra
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Building Energy ◽  
Energy Simulation ◽  
Weather Data ◽  
Building Energy Simulation ◽  
Existing Buildings ◽  
Existing Building ◽  
Simulation Tools ◽  
Heating And Cooling

Energy consumption awareness and reducing consumption are popular topics. Building energy consumption counts for almost a third of the global energy consumption and most of that is used for building heating and cooling. Building energy simulation tools are currently gaining attention and are used for optimizing the design for new and existing buildings. For O&M phase in existing buildings, the multiannual average weather data used in the simulation tools is not suitable for evaluating the performance of the building. In this study an existing building was modeled in EnergyPlus. Real on-site weather data was used for the dynamic simulation for the heating energy demand with the aim of comparing the measured energy consumption with the simulated one. The aim is to develop an early fault detection tool for building management.

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 Updated Typical Weather Years for the Energy Simulation of Buildings in Mediterranean Climate. A Case Study for Sicily

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4115 ◽  
Author(s):  
Vincenzo Costanzo ◽  
Gianpiero Evola ◽  
Marco Infantone ◽  
Luigi Marletta
Keyword(s):  
Residential Building ◽  
Building Energy ◽  
Energy Simulation ◽  
Weather Data ◽  
Weather Station ◽  
Simulation Tools ◽  
The Difference ◽  
Energy Simulations

Building energy simulations are normally run through Typical Weather Years (TWYs) that reflect the average trend of local long-term weather data. This paper presents a research aimed at generating updated typical weather files for the city of Catania (Italy), based on 18 years of records (2002–2019) from a local weather station. The paper reports on the statistical analysis of the main recorded variables, and discusses the difference with the data included in a weather file currently available for the same location based on measurements taken before the 1970s but still used in dynamic energy simulation tools. The discussion also includes a further weather file, made available by the Italian Thermotechnical Committee (CTI) in 2015 and built upon the data registered by the same weather station but covering a much shorter period. Three new TWYs are then developed starting from the recent data, according to well-established procedures reported by ASHRAE and ISO standards. The paper discusses the influence of the updated TWYs on the results of building energy simulations for a typical residential building, showing that the cooling and heating demand can differ by 50% or even 65% from the simulations based on the outdated weather file.

scholarly journals A European Database of Building Energy Profiles to Support the Design of Ground Source Heat Pumps

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2496 ◽  
Author(s):  
Laura Carnieletto ◽  
Borja Badenes ◽  
Marco Belliardi ◽  
Adriana Bernardi ◽  
Samantha Graci ◽  
...  
Keyword(s):  
Heat Pump ◽  
Energy Demand ◽  
Residential Buildings ◽  
Building Energy ◽  
Heat Pumps ◽  
Climatic Conditions ◽  
Ground Source Heat Pumps ◽  
Heating And Cooling ◽  
Energy Profiles ◽  
Ground Source

The design of ground source heat pumps is a fundamental step to ensure the high energy efficiency of heat pump systems throughout their operating years. To enhance the diffusion of ground source heat pump systems, two different tools are developed in the H2020 research project named, “Cheap GSHPs”: A design tool and a decision support system. In both cases, the energy demand of the buildings may not be calculated by the user. The main input data, to evaluate the size of the borehole heat exchangers, is the building energy demand. This paper presents a methodology to correlate energy demand, building typologies, and climatic conditions for different types of residential buildings. Rather than envelope properties, three insulation levels have been considered in different climatic conditions to set up a database of energy profiles. Analyzing European climatic test reference years, 23 locations have been considered. For each location, the overall energy and the mean hourly monthly energy profiles for heating and cooling have been calculated. Pre-calculated profiles are needed to size generation systems and, in particular, ground source heat pumps. For this reason, correlations based on the degree days for heating and cooling demand have been found in order to generalize the results for different buildings. These correlations depend on the Köppen–Geiger climate scale.

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