445 research outputs found
On time-alignment of weather data in Building Performance Simulation
Full text linkWhile simulating complex systems, information exchange among components is one of the most important aspects. A specific kind of information is that related to weather data. The format of
the climatic data diffusely used in Building Performance Simulation tools (BPSts) contains information about weather variables which are different from each other as far as concern their nature and timing. They have a statistical origin and, in the majority of the cases, are provided on an hourly basis. Given this inhomogeneity and hourly time base, care had been taken to manage their timing and different approaches are today’s in use by BPSts. Furthermore, when the building involves complex components and control strategies, sub-hourly simulation are needed to understand the efficiency of the enquired system. This necessity has led to the implementation of even more different interpolation routines. The capability of these interpolation routines to represent weather conditions that change much more frequently than shown on an hourly basis is here investigated. Besides, BPSts are today used also at operational time, as predictive tools for control strategies and/or Fault Detection and Diagnosis. In this scenario, the statistical validity of climatic data is not anymore sufficient, while their variability profile, recorded with high frequency, and their correct interpretation/synchronization (integral values vs instantaneous values), might became relevant. In this article will be presented a review of the choices implemented by two well-known software, such as TRNSYS 17 and EnergyPlus 8.4.0, to handle weather data and further considerations will be made upon possibilities offered or denied by this choices when different components are involved in the simulation
Automatic Detection and Diagnosis of faults in Sensors used in EMS
Full text linkA much occurring problem in the Energy Management Systems of existing buildings and HVAC services is that the measurements are unreliable. In this article a methodology is described which can be used to determine the presence of errors in energy monitoring, caused by faulty measurements. These errors can be detected and subsequently diagnosed. Detection of monitoring errors is done based on occurring symptoms. Determination of these symptoms is done using the laws of conservation of energy, mass and pressure. The diagnosis is done by using a statistical method based on Bayesian theory in which the chance of an error occurring is determined based on ( combinations of) the symptoms. The method is built in a Bayesian Belief Network (BBN) software tool. The advantage of BBN is that it is consistent with the working methods of experts in installation technology
Near zero, zero and plus energy buildings: revised definitions
Full text linkHere a survey of current definitions is the starting point to underline inconsistencies and critical
issues, and to identify weak points. From these, distinguishing between energy and primary
energy, with all its attributes, and between energy sources and energy carriers, a proposal of
revised definitions of near zero, zero and plus energy buildings is formulated. This analysis is based on the use of the classic energy balance, but taking into consideration that a building is always a net energy consumer (it always produce entropy or destroy exergy). Special attention is then paid in clearly defining primary energy factors for energy carriers produced from renewable energy sources on site, nearby or far. Although the primary energy factors values have been fixed sometime by political reasons, a clear scientific definition is limiting them to a reasonable range these values, which at least do not violate the basic principles of thermodynamics. Finally, to clarify that a “plus” building cannot create energy but can just contribute to the local or regional electrical energy production by feeding the grid, a complementary energy index is then proposed beyond than required by the EPBD. This can overcome the questioning on the “negative” primary energy index that can be achieved by such building using some of current net ZEB definition. In this way is possible to spit the main function (and its quality) of a building from the secondary function (and quality) of being a distributed electric generator for the grid without losing any values and complying with the nearly Zero Energy Building definition of EPBD
Occupant behaviour related to energy use in the residential sector: results from the Ecommon monitoring campaign
No full textBuildings in Europe are the largest end use sector and especially residential buildings account for two thirds of this energy use. Despite the fact that building characteristics play a major role in a dwelling’s energy consumption, occupant characteristics and behaviour significantly affect this energy use as well. The Ecommon campaign monitored 32 residential dwellings for 6 months in the Netherlands, capturing quantitative (temperature, CO2, humidity, movement, boiler and ventilation electricity consumption, real time and total electricity and gas consumption on the meter) and quantitative data (comfort perception, actions taken like closing and opening windows, thermostat use, use and type of clothes, and metabolic activity). Additionally in the beginning of the campaign a survey was given to the tenants with questions on income, gender, education level, thermostat and ventilation preferences, bathing patterns and other related data. This paper describes the experimental set up of the campaign, the temperature and occupancy profiles for each type of room for the 32 dwellings and the findings on the clothing patterns and metabolic activity. Temperature profiles show that these dwellings have higher temperatures through the whole day than the common assumption of the daily average of 18 o C suggested for the calculations of the national simulation software. A method is demonstrated on how a combination of motion detection and CO2 can lead to reliable occupancy profiles.OLD Housing Quality and Process Innovatio
Addressing Different Approaches for Evaluating Low-Exergy Communities
No full textThe IEA Annex 64 focusing on low-ex communities aims at the improvement of energy conversion chains on a community scale, using exergy analysis as the primary evaluation mode. Within this Annex the participants discuss important aspects and available methods for energy and exergy assessment as well as the added value of aiming for low exergy (LowEx) communities. The reason to exploit the exergy approach is that it provides critical insight into how the maximum potential of energy resources can be used, resulting in a reduced need for high quality energy sources. This insight cannot be obtained with energy analysis. However, other aspects play a role when designing an optimal energy system, such as costs or CO2 emissions. There can be reasons that justify exergy destruction. To address these issues the working definition for the annex is that “a LowEx community is a community for which the energy system is designed in such a way that exergy destruction is minimized, or that all exergy destruction is justified by other reasons (e.g. economic / social, other sustainability reasons)”. This paper gives more background on the definition and presents a general overview of exergy analysis of energy systems in the built environment. Different approaches and opinions are discussed, including how these affect the results. The aim is to create a common ground for consideration low exergy systems at the community scale by setting clear precedents for defining evaluation methods, system boundaries, and input classification.Building Service
Bayesian Belief Networks (BBN) and Expert Systems for supporting model based sensor fault detection analysis of smart building systems
No full textThe Hague University in Delft uses an advanced climate control system. All sensors and actuators are monitored and deviations from the sensor data are reported daily. The building manager will have to combine the information from the sensor data in order to draw the right conclusions. In this paper, two possible solutions are described for analyzing the data by a computer program. The first solution is by means of a rule-based program, in which predetermined situations have been defined. The data from the sensors are fed into the program and the program checks whether it matches any of the situations. The second solution is to make use of a Bayesian Belief Network. This is a mathematical model that describes the symptoms and causes of a particular problem. With imported sensor data a computer program calculates the likelihood of particular causes of data symptoms.OLD Housing Quality and Process Innovatio
How to use Building Information Systems for a transition towards Sustainable Building Operation
No full textBIM Building Information Model or Modelling connects many different informationsystems from various actors during the building construction process with eachother in one easily accessible and understandable model. BIM assures an effectiveand efficient building construction process by reducing failing cost and reduces theuse of materials by so called clash-controls. More and more buildings andinfrastructural works are completed with help of BIM and materials, energy, timeand money are saved by doing so. But why not use BIM for the exploitation phase ofboth new and existing buildings? By connecting the BIM model with the otherexploitation information systems as Facility Management Information Systems(FMIS), and Building Information Systems it’s possible to create an easilyaccessible and understandable building and operating information managementtool. Furthermore using BIM during the exploitation will increase the BIM market.This paper makes clear that BIM an make a big difference in the quality of theexploitation and operation of buildings, by helping creating a better andcomfortable indoor climate while reducing energy losses and costs. FacilityManagers should be the owners of this “exploitation and operation BIM” and have to know which information they must extract from the BIM and how to manage this information system. Another benefit will be time savings, and thereby money savings, because searching, reconstruction and updating building information again and again is not needed anymore. The paper also discuss some of the problems with the implementation and use of such a BIM.OLD Housing Quality and Process Innovatio
The effects of vegetation on indoor thermal comfort:The application of a multi-scale simulation methodology on a residential neighborhood renovation case study
No full textHighlights•A multi-scale simulation methodology to assess the effects of vegetation on thermal comfort is used.•It application is shown on a case of urban and building retrofit intervention.•The effect of plants on the microclimate and indoor environment is assessed.•A decrease of up to 4.8 °C in indoor temperature is registered.•The final impact on the indoor thermal comfort based on the adaptive model is determined
Introduction to an in-situ method for rapid measurement of the walls’ thermal resistance in existing buildings
No full textLarge deviations observed between the actual and theoretical gas consumption in Dutch dwellings, cast a shadow of doubt on the accuracy of the energy labeling method. In this sense, the accuracy of the calculation methods as well as the inputs being fed, fall under the question. According to several studies, the significance of wall’s thermal resistance as one of the most sensitive inputs has become clear. From the lack of sufficient information regarding the exact construction of the existing walls, arises a necessity for in-situ measurements. However, such measurements are generally not being performed because the existing methods demand very long monitoring periods. In this research, a rapid transient in-situ thermal resistance measurement technique, Excitation Pulse Method (EPM), has been introduced, experimentally applied to a case study, and compared to the existing international standard method ISO 9869, showing a very good agreement. EPM is based on the theory of thermal response factors. It allows in-situ determination of the walls’ thermal resistance and the average volumetric heating capacity in a couple of hours. The method is therefore believed to aid in better and much quicker estimation of theOLD Housing Quality and Process InnovationEngineering Thermodynamic
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