Efficient control is often the most cost-effective option to improve on the running cost of a Three-Pipe Water Pumping System. However, the effect of changing the control strategy (i.e. on energy consumption) is usually difficult to predict. To obtain this information more easily, a new simulation tool, QUICKcontrol, was developed. This new tool was used to investigate the energy cost savings potential in a Three-Pipe Water Pumping System. The influence of pump scheduling, dam level set points, control parameters and different combinations thereof was investigated. The simulation models were firstly verified with measurements obtained from the existing system to confirm their accuracy for realistic control retrofit simulations. With the aid of the integrated simulation tool it was possible to predict savings of R 195'000 per year with an average 3.8 MW of load shifted. / Thesis (M.Ing. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2005.

Efficient control is often the most cost-effective option to improve on the running cost of a Three-Pipe Water Pumping System. However, the effect of changing the control strategy (i.e. on energy consumption) is usually difficult to predict. To obtain this information more easily, a new simulation tool, QUICKcontrol, was developed. This new tool was used to investigate the energy cost savings potential in a Three-Pipe Water Pumping System. The influence of pump scheduling, dam level set points, control parameters and different combinations thereof was investigated. The simulation models were firstly verified with measurements obtained from the existing system to confirm their accuracy for realistic control retrofit simulations. With the aid of the integrated simulation tool it was possible to predict savings of R 195'000 per year with an average 3.8 MW of load shifted. / Thesis (M.Ing. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2005.

Many scientists have accepted that human activities are the major cause of climate change and global warming. Knowledge on the effect this will have on office buildings and energy consumption in the future is essential. Thus the assessment of future building energy consumption is becoming more important especially in countries such as Malaysia where the majority of the office buildings depend on air-conditioning to maintain the occupants level of comfort. This research explores the effect of future climate change weather on the energy consumption of office buildings in Malaysia, by using simulation software. Simulated weather data sets HadCM3 were supplied by the Hadley Centre in the United Kingdom for the recent past and for the future up to 2099. Test Reference Years (TRYs) were selected from this data using the Finkelstein-Schafer Statistic (FS) method for four time slices, namely TRYs 1990-2007, 2010-2039, 2040-2069 and 2070-2099. The HadCM3 data was validated by comparing the 1990-2007 TRY with a TRY selected by the same method and period from the measured weather. The Hadley data was supplied as daily values, but the building simulation software required hourly values. Algorithms were therefore used to generate hourly values from the daily data for the relevant variables (dry bulb temperature, relative humidity, wind speed and global solar radiation) and to decompose global solar radiation into direct and diffuse radiation. Two different office building were modelled in the simulation software, one imaginary simplified typical building and one real building. The sensible and latent annual cooling loads were found for each building for each different TRY. A sensitivity analysis was also performed to investigate the effect on cooling load of changes in building design as possible ways of mitigating the effects of climate change. It was found that climate change will increases the building energy consumption by 13.6 percent in future and better understanding on building design will reduce this effect.

This thesis presents three emerging computational methods: machine learning, gradient-free optimization, and Bayesian modelling. Each method is showcased in its ability to enable energy savings in new and existing buildings when paired with dynamic energy models. Machine learning algorithms provide rapid computational speed increases when used as surrogate models, supporting early-stage designs of buildings. Genetic algorithms support the design of complex interacting systems in a reduced amount of effort. Finally, Bayesian modelling can be leveraged to incorporate uncertainty in building energy model calibration. These methods are all readily available and user-friendly, and can be incorporated into current engineering workflows. / Graduate

In recent times data-driven approaches to parametrically optimize and explore building geometry has been proven to be a powerful tool that can replace computationally expensive and time-consuming simulations for energy prediction in the early design process. In this research, we explore the use of surrogate models, i.e. efficient statistical approximations of expensive physics-based building simulation models, to lower the computational burden of large-scale building geometry analysis. We try different approaches and techniques to train a machine learning model using multiple datasets to analyze the impact of geometry and envelope features on the energy efficiency of buildings. These contributions are presented in the form of two conference papers and one journal paper (being prepared for submission) that iteratively build up the underlying methodology. The first conference paper contains preliminary experiments using 4 manually generated building geometries for office buildings. Data were generated by simulating various building samples in EnergyPlus for different geometries. We used the generated data to train a machine learning model using support vector regression. We trained two separate models for predicting heating and cooling loads. The lesson learned from this first experiment was that the prediction of the models was not great due to insufficient geometric features explaining the variability in geometry and the lack of sufficient data for varied geometries. The second conference paper developed a novel dataset of 38,000 building energy models for varied geometry using 2D images of real-world residences. We developed a workflow in the Grasshopper/Rhino environment which can convert 2D images of a floor plan into a vector format then into a building energy model ready to be simulated in EnergyPlus. The workflow can also extract up to 20 geometric features from the model, to be used as features in the machine learning process. We used these features and the simulation results to train a neural network-based surrogate model. A sensitivity analysis was performed to understand the impact and importance of each feature to the energy use of the building. From the results of the experiment, we found that off-the-shelf neural network-based surrogates provided with engineered features can very well emulate the desired simulation outputs. We also repeated the experiment for 6 different climatic zones across Canada to understand the impact of geometric features across various climates; these findings are presented in an appendix. iv In the journal paper, we explored two different methodologies to train surrogate models: monolithic and component-based. We explored the component-based modeling technique as it allows the model to be more versatile if we need to add more components to it, ultimately increasing the usability of the model. We conducted further experiments by adding complexity to the geometry surrogate model. We introduced 10 envelope features as an input to the surrogate along with the 20 geometric features. We trained 6 different surrogate models using different datasets by varying geometric and envelope features. From the results of the experiment, we found that the monolithic model performs the best but the component-based surrogate also falls into an acceptable range of accuracy. From the overall results across the three papers, we see that simple neural network-based surrogate models perform really well to emulate simulation outcomes over a wide variety of geometries and envelope features / Graduate

Building energy efficiency is progressively becoming a crucial topic in the architecture, engineering, and construction (AEC) sector. Energy management tools have been developed to promise appropriate energy savings. Building energy simulation (BES) is a tool mainly used to analyze and compare the energy consumption of various design/operation scenarios, while building automation systems (BAS) works as another energy management tool to monitor, measure and collect operational data, all in an effort to optimize energy consumption. By integrating the energy simulated data and actual operational data, the accuracy of a building energy model can be increased while the calibrated energy model can be applied as a benchmark for guiding the operational strategies. This research predicted that building information modeling (BIM) would link BES and BAS by acting as a visual model and a database throughout the lifecycle of a building. The intent of the research was to use BIM to document energy-related information and to allow its exchange between BES and BAS. Thus, the energy-related data exchange process would be simplified, and the productive efficiency of facility management processes would increase. A systematic literature review has been conducted in investigating the most popular used data formats and data exchange methods for the integration of BIM/BES and BAS, the results showed the industry foundation classes (IFC) was the most common choice for BIM tools mainly and database is a key solution for managing huge actual operational datasets, which was a reference for the next step in research. Then a BIM-based framework was proposed to supporting the data exchange process among BIM/BES/BAS. 4 modules including BIM Module, Operational Data Module, Energy Simulation Module and Analysis and Visualization Module with an interface were designed in the framework to document energy-related information and to allow its exchange between BES and BAS. A prototype of the framework was developed as a platform and a case study of an entire office suite was conducted using the platform to validate this framework. The results showed that the proposed framework enables automated or semi-automated multiple-model development and data analytics processes. In addition, the research explored how BIM can enhance the application of energy modeling during building operation processes as a means to improve overall energy performance and facility management productivity. / Doctor of Philosophy / Building energy efficiency is progressively becoming a crucial topic in the architecture, engineering, and construction (AEC) sector, promising appropriate energy savings can be achieved over the life cycle of buildings through proper design, construction, and operation. Energy management tools have been developed towards this end. Building energy simulation (BES) is a tool mainly used to analyze and compare the energy consumption of various design/operation scenarios. These instances include the selection of both new and retrofit designs and for building codes, building commissioning, and real-time optimal control, among others. The main challenge surrounding BES is the discrepancy between quantitative results and actual performance data. Building automation systems (BAS), or a part of BAS which is often referred to as building energy management systems (BEMS), works as another energy management tool to monitor, measure and collect operational data, all in an effort to optimize energy consumption. The key disadvantage to the more general tool of BAS in energy management is that the data sets collected by BAS are typically too large to be analyzed effectively. One potential solution to the lack of effective energy management analysis may lie in the integration of BES and BAS. Actual operational data can be compared with simulation results in assessing the accuracy of an energy model while the energy model can be applied as a benchmark for evaluating the actual energy consumption and optimizing control strategies. The presented research predicted that building information modeling (BIM) would link BES and BAS by acting as a visual model and a database throughout the lifecycle of a building. The intent of the research was to use BIM to document energy-related information and to allow its exchange between BES and BAS. Thus, the energy-related data exchange process would be simplified, and the productive efficiency of facility management processes would increase. More specifically, this research posits the framework of integrating BIM, BES, and BAS to produce a seamless and real-time energy-related information exchange system. The proposed framework enables automated or semi-automated multiple-model development and data analytics processes. In addition, the research explored how BIM can enhance the application of energy modeling during building operation processes as a means to improve overall energy performance and facility management productivity.

Energianvändningen i världen växer för varje år, vilket i sin tur bidrar med ökade mängder utsläpp av växthusgaser till atmosfären. På grund av den ökade energianvändningen blir intresset för energisnåla byggnader allt högre med tiden. I detta projekt har en nordligt placerad fastighet med en Atemp på 716 m2 i Gnarp simulerats med hjälp av IDA Indoor Climate and Energy (IDA ICE) samt WINSUN. Fastigheten har två våningar med åtta lägenheter totalt och är uppvärmd med bergvärme då fjärrvärmenätet ligger för långt ifrån området. Tanken med byggnaden är att den ska uppnå kriterierna för ett plusenergihus, vilket innebär att fastigheten ska generera mer energi än vad den gör av med via en solcellsanläggning som monteras på taket. Enligt de teoretiska resultat som simuleringen visar så kan fastigheten klassas som ett plusenergihus, då solcellsanläggningen på taket producerar mer energi än vad som används årligen. Det innebär att möjligheterna för byggnation av ett plusenergihus i nordligare områden i Sverige finns, där temperaturer varierar kraftigt under årets gång och kan gå lägre än -30°C under vintertid. Den årliga elproduktionen ligger över 26 700 kWh/år och elanvändningen hamnar på 16 400 kWh/år, där tappvarmvattnet står för den största delen använt el. Det innebär att den genererar ungefär 10 300 kWh/år i överskott relativt till inköpt el-energi. Huset är välisolerat och har smart placerade glasytor för värmeinsläpp. Det inkluderar en effektiv värmepump, ett FTX-System för värmeåtervinning via ventilationssystemet samt ett solcellssystem på taket som i sin tur bidrar till möjligheten för en energiproducering som är högre än energianvändningen, därav en plusenergihus-klassning. Under energianvändningsprocessen så har den årliga uppvärmningen, tappvarmvattnet samt fastighetselen tagits till godo i beräkningarna för bedömning av byggnadens energiprestanda. Eftersom solcellerna producerar mer energi under sommaren, vilket medföljer att överskott på elproduktionen uppstår under vissa perioder av året, så innebär det att el kan säljas via elnätet till en elhandlare. / The interest in low-energy-houses has risen in recent years as the energy usage around the globe is constantly increasing, resulting in ever-increasing amounts of greenhouse gases in the atmosphere. In this project, the energy consumption of a building in a northern area of Sweden, Gnarp, with an Atemp of 716m2 was simulated using IDA indoor Climate and Energy (IDA ICE) and WINSUN. The building has two floors and is comprised of eight apartments. It is heated using geothermal heating since it is not located close enough to a district heating area. The goal of the simulation was to determine if this building is an energy-plus-house, whereby a PV-system mounted on the rooftop allows for the energy production-value of the building to be higher than the energy-usage. The theoretical results of the simulation show that this building is indeed an energy-plus-house since the PV-system is generating more energy than the yearly usage of the building. This simulation shows that it is possible to build an energy-plus-house in northern areas of Sweden where temperatures are highly variable and can go below -30°C during winter season. It indicates a yearly electricity-production of over 26 700 kWh/year and a usage of approximately 16 400 kWh/year, where the domestic hot water accounts for the highest usage of electricity. This means that the building generates an electricity surplus of approximately 10 300 kWh/year. The building is well isolated and has well placed windows for heat generation via the sun. It includes an effective heat pump, an FTX-system, and solar panels on the roof which gives the opportunity for an energy-production that is larger than the energy-usage, which in turn gives the opportunity for an energy-plus-house classification. The heating, domestic hot water, and the building electricity were all considered when calculating the estimation of the energy-quality of the property. The PV-system generates more energy during the summer, which results in an overproduction of electricity at certain times of the year. The extra electricity produced can be sold to the electric utility.

Increased energy performance of the building stock of European Union is seen as an important measure towards mitigating climate change, increasing resource utilisation efficiency and energy supply security. Whether to improve the supply-side, the demand-side or both is an open issue. This conflict is even more apparent in countries such as Sweden with a high penetration of district heating (DH). Many Swedish DH systems have high share of secondary energy resources such as forest industry residuals, waste material incineration and waste heat; and resource efficient cogeneration of electricity in combined heat and power (CHP) plants. When implementing an energy conservation measure (ECM) in a DH connected building stock, it will affect the operation of the whole DH system. If there are CHP plants and the cogeneration of electricity decreases due to an ECM, and this electricity is valued higher than the fuel savings, the consequences of the ECM would be negative.  These complex relationships are investigated by conducting a case study on the Eskilstuna DH system, a renewable energy supply system with relatively high share of cogenerated electricity. Heat demand profiles of ECMs are determined by building energy simulation, using recently deep energy retrofitted multifamily buildings of the “Million Programme”-era in Eskilstuna as model basis. How implementing ECMs impact on the DH system’s heat and electricity production under different electricity revenue scenarios has been computed and evaluated in terms of resource efficiency and CO2 emissions.  The results show that different ECMs in the buildings impact differently on the DH system. Measures such as improved insulation level of the building’s envelope, that decrease the heat demand’s dependence to outdoor temperature, increase the amount of cogenerated electricity. While measures such as thermal solar panels, which save heat during summer, affects the absolute amount of cogenerated electricity negatively. Revenues from cogenerated electricity influence the amount of cost-effectively produced electricity much more than the impact from ECMs. Environmental benefits of the ECMs, measured in CO2 emissions and primary energy consumption, are quite small in DH systems that have high share of forest residual fuels and electricity cogeneration. The consequences can even be negative if ECMs lead to increased need of imported electricity that is produced resource inefficiently or/and by fossil fuels. However, all studied ECMs increase the relative amount of cogenerated electricity, the ratio between amount of cogenerated electricity and the heat load. This implied that all ECMs increase the overall efficiency of the Eskilstuna DH system.

This study seeks to expand the range of climates and conditions in which free cooling from an economizer can replace air conditioning power consumption in residential applications. To explore this issue, we first discretize a simple building model in space and in time. We then solve the associated energy and mass balances for the estimated hourly heating and cooling loads and humidity conditions with respect to an annual climate profile. We propose a forecast-based algorithm to control the rate of outdoor airflow brought in by an economizer, in response to the upcoming cooling load to be experienced by the interior airspace. The algorithm takes advantage of a range of acceptable temperatures for thermal comfort by precooling the envelope overnight to delay the onset of cooling demand during the day. In order to consider the highest potential benefit from such an algorithm, we bypass the considerable problem of forecast accuracy by basing the inputs on the upcoming cooling load according to an initial simulation of the full year. On the whole, even with the forecast-based control, the results of the study have much in common with previous findings in the literature. Precooling works better to reduce cooling load in cases of higher thermal and moisture mass, but a humid climate severely restricts when free cooling is beneficial. For the example house considered here with the Austin climate and other assumptions, the effect of the proposed forecast-based economizer control was to greatly reduce the indoor air cooling load while greatly increasing the number of annual hours of unacceptably high indoor humidity. When we adjusted the forecast-based algorithm to avoid the excess humidity, the remaining reduction in cooling load was not significant. To investigate further how a forecast-based economizer could reduce cooling load in humid climates, the prinicipal task should be to extend the control algorithm to forecast and manage upcoming indoor humidity levels in the same fashion as was done in this study for indoor air temperature. / text

Att kunna exportera färdiga modeller direkt från Revit till IDA är av stor betydelse för att kunna jobba effektivt med energisimuleringar. Denna rapport ger en bakgrundsbeskrivning till hur IFC har uppkommit och är uppbyggt. Dock ligger huvudfokus på hur Revit kan implementeras i arbetsflödet, där både hur IFC-filer överförs mellan programmen och felsökning av problem med befintliga Revitmodeller beskrivs. IFC-filer är dagens standard för att överföra data mellan olika instanser under byggprocessen, det är ett programoberoende format som möjliggör samarbete mellan olika programvaror. Dagens standard är IFC4, men det vanligaste formatet idag är IFC2x3 då det nya formatet ännu inte har implementerats i alla programvaror. Vilken geometri och information som ska exporteras från Revit till IDA är ett omfattande arbete att ta reda på och ställa in. Därför innehåller denna rapport både en lathund för exportering, men även flera filer för automatisk inladdning av korrekta exporteringsinställningar i Revit. För att kunna genomföra en energisimulering krävs det att rummen är förslutna för att undvika extrema köldbryggor som kan uppstå om väggarna inte är ordentligt anslutna med taket och golvet. Därför finns flera åtgärder beskrivna för att undvika fel i simuleringen, bland annat kring problem med fönster, dörrar och problem med lagerhantering. Beskrivningen utgår ifrån att användaren inte har tidigare erfarenhet av Revit. Guiderna har testats på en gymnasieklass i ämnet CAD där resultatet visade att en stor del av lathunden kan användas av en nybörjare. Dessutom studeras skillnader i Revits verktyg för simulering mot IDA. Där samma modell simulerades på åtta olika platser och standardavvikelsen mellan resultatet beräknades, detta gav ett koefficientintervall på ± 4 %, dvs. att en simulering i IDA inte bör avvika med mer än ± 4 % i jämförelse med den som gjordes i Revit. Liknande resultat uppmättes när simuleringsskillnader i dörrar och fönster simulerades. Där en stor glasyta på en vägg kommer generera ett koefficientintervall på 3 %. Om fönstren ersätts av dörrar kommer avvikelsen att minska något till strax under 2 % sett till alla väderstreck. / It is extremely important to be able to export models directly from Revit to IDA for the energy simulations to be efficient. This report has a background description about how the IFC-format has been developed and how it is structured. However, the focus in the report is on how Revit can be implanted in the workflow, where both export and troubleshooting from Revit are described. IFC is the standard today to export data between various parts during the construction process and it is a format that is program independent, which means that it works across different applications. The current version of IFC is IFC4, but IFC2x3 is still the most used version since not every program has support for the new IFC4 format. It is extensive to figure out which data IDA requires for energy simulations, therefore this report contains both a tutorial for export settings and a file for automatic set correct export settings in Revit. To succeed with the energy simulations a requirement is that the space in the building is enclosed with walls, floor and roof, otherwise extreme thermal bridges will cause incorrect simulations. Therefore, there are several workarounds described in this report to correct these problems, including problems with windows, doors and problems with storage facilities . The description in this report is written with the assumption that the user do not have any experience with Revit. The guides were tested on a class of high school students that are studying the course CADCAD02. The result showed that when a problem occurs with a model it can easily be solved with the support from this report. Further differences between simulations in Revit and IDA were tested. The same model was simulated in eight different places and the standard deviation was calculated, this gave the coefficient range ±4 %, i.e. a simulation in IDA should not differ more than 4 % from the simulation in Revit. Similar results were found with the simulations of windows and doors, even though the difference was slightly lower.