In 2010 the housing stock was responsible for 30.5% of all energy consumed in the UK. The UK government has set a transition target to reduce the energy used from space heating in dwellings by 29% by 2020 as part of their drive to lower CO2 emissions and mitigate the risks of global climate change. Housing stock energy models have been developed as research tools to identify pathways to a low energy future. These tools use assumptions about how homes are heated that may reduce their effectiveness at making accurate energy predictions. This thesis describes the collection and analysis of temperature data from over 300 homes in Leicester to develop better understanding of how dwellings are heated. The temperature measurements were assessed for error and a final sample of 249 dwellings was established. Mean winter temperatures (December February) were found to be 18.5°C and 17.4°C for living rooms and bedrooms which are comparable with temperatures reported in previous studies. Statistically significant relationships were established between seven descriptors; three technical (house type, house age and wall type) and four social (household size, employment status, age of oldest occupants and tenure). Only 24% of the variation in mean winter temperature could be explained by these descriptors. Ten heating practice metrics were developed to give insight into how homes are heated; these included the duration of the heating period and the average temperature when heated. Statistically significant relationships were found between the heating practices and a number of technical and social household descriptors. It is concluded that the variation in heating practices which relates to social household descriptors will result in models being unable to make accurate predictions at the regional of city scale. Furthermore, this work has shown flaws in the idealised temperature profile as used in BREDEM. It is suggested that the findings of this work are considered in the development of future stock models.

In this study, linear and non-linear models were trained on real data to mimic the relationship between household electricity consumption and indoor temperature, in the rooms of an apartment in downtown Stockholm. The aim was to better understand this relationship and to distinguish any divergence between the different rooms. With data from two weeks of measurements, the models proved to perform well when tested on validation data for almost all rooms, only showing performance dips for the middle room. A noticeable correlation between the electricity consumption and the indoor temperature was observed for all rooms except the bedroom. However, the benefits of using this information to predict the indoor temperature are limited and differ between the rooms. The household electricity consumption primarily brought beneficial information to the kitchen models, where most of the heat generating appliances were located. It was found that linear models were sufficient to represent the thermal systems of the rooms, performing equally well and often better than non-linear models.

Human performance and health are one of the most relevant topics in modern society. Especially at young ages, when academic performance is indispensable. Thus, as the human being spends most of its lifetime inside a building, thermal comfort has become an essential aspect of a room. The aim of the present research is to measure and evaluate the main thermal comfort parameters such as CO2 levels, relative humidity and indoor temperature so the variation in them can be seen in the study rooms of the library of the University of Gävle as there is student use. For it, Rotroninc Measurement Solutions CL11 sensors and a Testo hot wire probe sensor have been used, as well as IDA ICE software simulations for the result validation. From the research, has been seen that even though the VAV air renewal system works as it should, the CO2 level rises up to 1000 ppm, which is not recommended by different thermal comfort ruling institutions. This way, a modification to the ventilation system control is recommended, changing it from temperature control to CO2 level and temperature control. Moreover, it is seen that during the non-opening hours of the library the ventilation systems are disconnected, generating an important energy-saving without altering the thermal comfort of the rooms at the beginning of the day.

Climate change is a significant issue in today's society as countries work towards decarbonising the economic sectors that contribute to significant greenhouse gas emissions. The electric vehicle (EV) is proposed as a solution to reduce the level of emissions in the transport sector. However, if an EV is powered by an electrical fossil fuelled source, their penetration into the UK market will have minimal mitigating effects, as emissions will simply shift from the transport sector to the energy production sector. Limited research has evaluated the loss of propulsion energy as a result of operating on-board climate control systems, and has focused more on traction energy. Unlike conventional fossil fuelled vehicles, EVs do not produce waste heat to warm the interior space of the vehicle. The present research found that up to 30% of a vehicle's total energy consumed per trip is allocated to heating requirements, thus the present research developed a temperature predicting numerical algorithm to compute indoor cabin temperatures. The vehicle was exposed to ambient climate conditions with an auxiliary heating or cooling system to evaluate this thermal model. The numerical algorithm could predict the temperature of a cabin space under solar space heating conditions with 62% more accuracy than previously developed models when comparing the Root Mean Square Error performance indicator. The presently developed temperature prediction algorithm may be applied to a route planning application, thus indicating the electrical energy required by the vehicle's battery for users to increase or decrease the desired temperature level. Additionally, this study investigated the ability of a renewable energy resource to decarbonise the vehicle's built-in climate control system. Integrating solar panels on the roof and bonnet of an EV to power an auxiliary climate control system reduced the electrical loading required to reach the occupant's thermal comfort. By installing an auxiliary heating system to increase cabin temperature by 2 or 5°C, the present research found that energy consumption of the built-in climate control system was reduced by 22% or 57%, respectively. This illuminates the potential an auxiliary climate control system has in improving the thermal performance of EVs.

Intresset kring lågenergibyggnader blir allt större och så kallade passivhus, med god isolering, hög lufttäthet och värmeåtervinning, byggs i allt större utsträckning i Sverige och andra europeiska länder. Vissa frågetecken har dock uppkommit kring inomhusklimatet i husen och risken för både under- och övertemperaturer. En annan viktig aspekt är hur husens egenskaper påverkar värmelasterna och hur detta i sin tur påverkar energiförsörjningssystemet. I detta examensarbete undersöks dessa båda aspekter – värmelastegenskaper och innetemperaturer – i fyra likadana nybyggda flerbostadshus i Falkenberg. Mätvärden från husens tekniska system, inklusive lufttemperaturmätningar i samtliga lägenheter, analyseras. Husen består av totalt 108 lägenheter, värms med fjärrvärme och använder ca 50 kWh/m2 Atemp årligen. Värmelastegenskaperna kännetecknas framförallt av låga effektbehov för både uppvärmning och varmvatten samt relativt stora svängningar över dygnet. Husen tycks vara känsligare för variationer i interntillskott än konventionella hus, vilket leder till regelbundna svängningar i effektbehovet för uppvärmning, med en topp under natten och morgonen. Detta leder till förstärkta svängningar i fjärrvärmenätet, vilket skulle kunna undvikas med hjälp av effektstyrning. Passivhusens stora termiska tröghet talar för en sådan möjlighet. Även innetemperaturen uppvisar regelbundna dygnsvariationer, som kan vara något större än i vanliga hus. Låga temperaturer förekommer tidvis under vintern, men resultaten pekar inte på några brister i de tekniska systemen. Temperaturen på sommaren är inte högre än i andra hus, och övertemperaturer tycks inte vara något stort problem i dessa hus. Det kan dock finnas en möjlighet att minska risken för övertemperaturer ytterligare genom användning av nattkyla under varma perioder. Denna möjlighet kan vara ett sätt att utnyttja passivhusens speciella egenskaper för att ytterligare förbättra konceptet. / There is today an increasing interest in low-energy buildings, and the so-called “passive houses” are becoming increasingly popular in Sweden and other European countries. There are however some concerns regarding the indoor climate in these houses, notably the risk of too low indoor temperatures in the winter and too high temperatures in the summer. Another issue is the heat load characteristics of this type of buildings, and how these affect the energy systems. In this thesis, these two aspects – heat load characteristics and indoor temperatures – are analysed in four identical multi-family buildings in Falkenberg, Sweden, using measured values from the buildings’ technical systems and measured indoor air temperatures in all dwellings. The buildings are heated with district heating, comprise 108 dwellings in total and use approximately 50 kWh/m2 annually (space heating, hot water and electricity for building services). The heat loads are mainly characterised by low but varying heat demands for space heating and hot water. The buildings appear to have a higher than usual sensitivity for variations in heat from internal sources and solar gains, leading to regular variations in the heat demand for space heating and fluctuations in the total heat demand. These fluctuations, which may negatively affect the energy system, could be avoided by actively controlling the heat demand for space heating. There are also daily fluctuations in the indoor temperatures, but the buildings perform well in this aspect, with temperatures that stay within the acceptable range most of the time. The summer indoor temperatures do not get higher than in other buildings. A possibility for further improvement within this area could be the use of forced ventilation during the night in hot periods, and hence utilising the buildings’ well insulated and airtight building envelope to keep heat out during the day. This possibility might further improve the passive house concept.

<p>This paper aims to present a novel approach to real-time indoor temperature forecasting to meet energy consumption constraints in buildings, utilizing computing resources available at the edge of a network, close to data sources. This work was inspired by the irreversible effects of global warming accelerated by greenhouse gas emissions from burning fossil fuels. As much as human activities have heavy impacts on global energy use, it is of utmost importance to reduce the amount of energy consumed in every possible scenario where humans are involved. According to the US Environmental Protection Agency (EPA), one of the biggest greenhouse gas sources is commercial and residential buildings, which took up 13 percent of 2019 greenhouse gas emissions in the United States. In this context, it is assumed that information of the building environment such as indoor temperature and indoor humidity, and predictions based on the information can contribute to more accurate and efficient regulation of indoor heating and cooling systems. When it comes to indoor temperature, distributed IoT devices in buildings can enable more accurate temperature forecasting and eventually help to build administrators in regulating the temperature in an energy-efficient way, but without damaging the indoor environment quality. While the IoT technology shows potential as a complement to HVAC control systems, the majority of existing IoT systems integrate a remote cloud to transfer and process all data from IoT sensors. Instead, the proposed IoT system incorporates the concept of edge computing by utilizing small computer power in close proximity to sensors where the data are generated, to overcome problems of the traditional cloud-centric IoT architecture. In addition, as the microcontroller at the edge supports computing, the machine learning-based prediction of indoor temperature is performed on the microcomputer and transferred to the cloud for further processing. The machine learning algorithm used for prediction, ANN (Artificial Neural Network) is evaluated based on error metrics and compared with simple prediction models.</p>

No description available.

Along with the increasing use of intermittent electricity sources, such as wind and sun, comes a growing demand for user flexibility. This has paved the way for a new market of services that provide electricity customers with energy saving solutions. These include a variety of techniques ranging from sophisticated control of the customers’ home equipment to information on how to adjust their consumption behavior in order to save energy. This master thesis work contributes further to this field by investigating an additional incentive; predictions of future energy savings related to indoor temperature. Five different machine learning models have been tuned and used to predict monthly heating energy consumption for a given set of homes. The model tuning process and performance evaluation were performed using 10-fold cross validation. The best performing model was then used to predict how much heating energy each individual household could save by decreasing their indoor temperature by 1°C during the heating season. The highest prediction accuracy (of about 78%) is achieved with support vector regression (SVR), closely followed by neural networks (NN). The simpler regression models that have been implemented are, however, not far behind. According to the SVR model, the average household is expected to lower their heating energy consumption by approximately 3% if the indoor temperature is decreased by 1°C.

Zahlreiche Studien den Stand der Forschung in Bezug auf die Ökosystemdienstleistungen untersucht. Dennoch wurde die Dimension „Volumen und Höhe“, d.h. die dritte Dimension städtischer Systeme, in den Studien zu Ökosystemdienstleistungen in städtischen Gebieten ignoriert. Die Forschungsziele und Fragestellungen dieser Dissertation lauten: i) Stand der aktuellen Forschung zur dritten Dimension von Ökosystemdienstleistungen im städtischen Raum, ii) Beurteilung des Zusammenhangs von urbanen mehrdimensionalen Indikatoren (zwei- und dreidimensionalen Indikatoren) für die Oberflächentemperatur in der Stadt und iii) Unterschiede zwischen Innen- und Außentemperaturen in urbanen Räumen. Diese Dissertation ist in vier Kapitel gegliedert. Im ersten und zweiten Kapitel werden die Forschungslücken und das Ziel der vorliegenden Untersuchung erläutert. Kapitel 3 enthält die veröffentlichten Artikel. Das letzte Kapitel behandelt die Ergebnisse der veröffentlichten Artikel. Diese Dissertation betont die Bedeutung von dreidimensionalen Studien in urbanen Ökosystemen, um das Konzept der Nachhaltigkeit in Städten voranzutreiben. Deshalb werden kontinentübergreifende Forschungen für weitere Studien empfohlen, die die dreidimensionale Struktur aller städtischen Komponenten und ihre Auswirkungen auf die Außen- und Innentemperatur berücksichtigen. / Among the studies on ecosystem services undertaken in urban areas, a ‎dimension ‘volume and height’, i.e., the third-dimension of urban environment is largely ignored. More specific, three-dimensional spatial models will increase the knowledge of how complex environment ‎shape the micro-climate in urban ‎environment. The research objectives and questions of this dissertation is: i) the status of the current research addressing the third-dimension of ‎ecosystem services in urban area, ii) assessing the association of urban multi-dimensional (two- and three- ‎dimensional) indicators on urban surface temperature and iii) variation of indoor and outdoor urban temperature pattern. This dissertation is organized into four chapters. The ‎first and second chapter explain the gaps in literature and the aim of this research. Chapter 3 holds the published articles. The last chapter discusses the results of the published articles. This dissertation emphasizes the importance of three-dimensional studies in urban ecosystems to advance the concept of sustainability in cities. Therefore, cross-continental studies that consider the three-dimensional structure of all the urban components and its impact on outdoor and indoor temperature is recommended for future research. / به جرات می توان گفت که در مطالعات خدمات اکوسیستم، بخصوص خدمات اکوسیستم شهری ، بعد سوم که شامل "ارتفاع و حجم" می باشد اصلا مورد توجه قرار نگرفته است. هدف از این پایان نامه، تلفیق مفهوم بعد سوم در خدمات اکوسیستم شهری و استفاده از فواید آن می باشد. مطالعه بعد سوم دانش ما را در نحوه شکل گیری اقلیم خُرد شهری افزایش می دهد. هدف این پروژه دکتری پاسخ به سوالات ذیل می باشد: 1) سطح آگاهی تحقیقات از بعد سوم خدمات اکوسیستم شهری، 2) ارزیابی ارتباط شاخص های چندبعدی (دو و سه بعدی) با دمای سطح و 3) ارزیابی الگوی دمای درونی و بیرونی در شهر. جهت پاسخ دادن به سوال های مطرح شده، این پژوهش به چهار فصل تقسیم شده است. فصل اول و دوم، که جایگاه خدمات اکوسیستم را در مطالعات شهری بررسی و جای خالی مفهوم بعد سوم در مطالعات خدمات اکوسیستم شهری را جستجو می کند. فصل سوم، شامل سه مقاله چاپ شده در راستای این پروژه دکتری می باشد. فصل چهارم، که نتایج بدست آمده را تجزیه و تحلیل می کند. نتایج بدست آمده نشان می دهد که مطالعات خدمات اکوسیستم شهری از معنی کلی و بنیادی به سمت سازش پذیری شهرها با پدیده تغییر اقلیم در حال تغییر است. همچنین نتایج نشان می دهد که ساختار متفاوت شهری بر شکل گیری الگوی دمای بیرون و داخل ساختمان ها موثر می باشد. استنتاج نتایج بدست آمده از این پایان نامه دو مورد را پیشنهاد می کند. اول، بررسی نقش ساختار های دو بعدی و سه بعدی بر روی دیگر شهر ها و تاثیر آن بر شکل گیری دمای بیرون و درونی ساختمان ها.

In dieser Arbeit wird die Entwicklung eines Gebäudeenergiemodells (BEM) und eines Schemas für die mittlere Strahlungstemperatur ($T_mrt$) vorgestellt, das in das Doppel-Canyon basierte städtische Bestandsschichtsschema (DCEP) integriert ist. Das erweiterte DCEP-BEM Modell zielt darauf ab, eine Verbindung zwischen anthropogener Wärme und dem Stadtklima herzustellen, indem Gebäude in Straßenschluchten einbezogen werden, um die Energieflüsse auf städtischen Oberflächen, die Auswirkungen der anthropogenen Wärme auf die Atmosphäre, die Innenraumlufttemperatur und die Abwärme von Klimaanlagen zu untersuchen. Das DCEP-BEM wird mit dem mesoskaligen Klimamodell COSMO-CLM (COnsortium for Small-scale MOdelling in CLimate Mode, im Folgenden CCLM) gekoppelt und zur Simulation des Winters und Sommers 2018 in Berlin. Die Auswertung der Wintersimulationen zeigt, dass CCLM/DCEP-BEM den mittleren Tagesverlauf der gemessenen turbulenten Wärmeströme gut reproduziert und die simulierte 2-m-Lufttemperatur und den städtischen Wärmeinseleffekt (UHI) verbessert. Im Sommer bildet das CCLM/DCEP-BEM die Innenraumlufttemperatur richtig ab und verbessert die Ergebnisse für die 2-m-Lufttemperatur und die UHI leicht. Außerdem wird das CCLM/DCEP-BEM angewendet, um die Abwärmeemissionen von Klimaanlagen im Sommer zu untersuchen. Die Abwärmeemissionen der Klimaanlagen erhöhen die Lufttemperatur in Oberflächennähe erheblich. Der Anstieg ist in der Nacht und in hochurbanisierten Gebieten stärker ausgeprägt. Es werden zwei Standorte für die AC-Außengeräte betrachtet: entweder an der Wand eines Gebäudes (VerAC) oder auf dem Dach eines Gebäudes (HorAC). Die Auswirkung von HorAC ist im Vergleich zu VerAC insgesamt geringer, was darauf hindeutet, dass HorAC einen kleineren Einfluss auf die oberflächennahe Lufttemperatur und den UHI hat. Ein Schema für $T_mrt$ wird für das CCLM/DCEP-BEM entwickelt und umfassend validiert. Es wird gezeigt, dass dieses Schema eine zuverlässige Darstellung von $T_mrt$ bietet. / This work presents the development of a building energy model (BEM) and a mean radiant temperature ($T_mrt$) scheme integrated in the urban canopy scheme Double Canyon Effect Parametrization (DCEP). The extended DCEP-BEM model aims to establish a link between anthropogenic heat emissions and urban climate by including the interior of buildings in urban street canyons to investigate the energy fluxes on urban surfaces, the effects of anthropogenic heat on the atmosphere, the evolution of indoor air temperature, and waste heat from air conditioning (AC) systems. DCEP-BEM is coupled with the mesoscale climate model COSMO-CLM (COnsortium for Small-scale MOdelling in CLimate Mode, hereafter CCLM) and applied to simulate the winter and summer 2018 of Berlin. The evaluation for winter simulations indicates that CCLM/DCEP-BEM reproduces well the average diurnal characteristics of the measured turbulent heat fluxes and considerably improves the simulated 2-m air temperature and urban heat island (UHI). In summer, CCLM/DCEP-BEM accurately reproduces the indoor air temperature, and slightly improves the performance of the 2-m air temperature and the UHI effect. Furthermore, CCLM/DCEP-BEM is applied to explore the waste heat emissions from AC systems in summer. AC waste heat emissions considerably increase the near-surface sensible heat flux and air temperature. The increase is more pronounced during the night and in highly urbanised areas. Two locations for the AC outdoor units are considered: either on the wall of a building (VerAC) or on the rooftop of a building (HorAC). The effect of HorAC is overall smaller compared to VerAC, indicating that HorAC has a smaller impact on the near-surface air temperature and the UHI effect. A $T_mrt$ scheme is developed for CCLM/DCEP-BEM and extensively evaluated. It is shown that this scheme provides a reliable representation of $T_mrt$.