Modeling of Photovoltaic-Thermal District Heating with Dual Thermal Modes

Solar photovoltaic thermal (PVT) collectors could be a competitive addition to district heating systems, particularly in areas with high energy density since they simultaneously produce electricity and heat whilst increasing the PV efficiency through cooling. This study presents a new Modelica PVT model, which is used together with EnergyPlus in a co-simulation setup to assess the technical feasibility of solar PVT district heating in new builds. The model has been applied to a block of 12 2-bedroom terraced houses with a 184m2 PVT array on the south facing side of the roof. It was identified that well-designed seasonal PVT heating configurations and control schemes are required to maximise PVT outputs. PVT dual thermal modes occur when the PV is either connected to a load or producing at close to the maximum power point. Integrating the dual modes into a control system could be more economical if heat tariffs were higher than electrical ones when heat demand is greater than the PVT thermal output

A review of approaches and applications in building stock energy and indoor environment modelling

Current energy and climate policies are formulated and implemented to mitigate and adapt to climate change. To inform relevant building policies, two bottom-up building stock modelling approach: 1) archetype-based and 2) Building-by-building have been developed. This paper presents the main characteristics and applications of these two approaches and evaluates and compares their ability to support policy making. Because of lower data requirements and computational cost, archetype-based modelling approaches are still the mainstream approach to stock-level energy modelling, life cycle assessment, and indoor environmental quality assessment. Building-by-building approaches can better capture the heterogeneous characteristics of each building and are emerging due to the development of data acquisition and computational techniques. The model uncertainties exist in both models which may affect the reliability of outputs, while stochastic archetype models and timeless digital twin model have the potential to address the issue. System dynamics modelling approach can describe and address the dynamics and complexity of often-conflicting policies and achieve co-benefit of multiple policy objectives. This paper aims to provide comprehensive knowledge on building stock modelling for modellers and policymakers, so they could use a building stock model with an appropriate user interface without having to fully understand the underlying algorithms or complexities

UCLA Intermediate Energy Nuclear and Particle Physics Research: Final Report

This project covers the following research: (a) Investigations into the structure of the proton and neutron. This is done by investigating the different resonance states of nucleons with beams of tagged, polarized photons, linearly as well as circularly, incident on polarized hydrogen/deuterium targets and measuring the production of {pi}{sup #25;0}, 2{pi}{sup #25;}0, 3{pi}{sup #25;0}, {eta}#17;, {eta}', {omega}, etc. The principal detector is the Crystal Ball multiphoton spectrometer which has an acceptance of nearly 4#25;. It has been moved to the MAMI accelerator facility of the University of Mainz, Germany. We investigate the conversion of electromagnetic energy into mesonic matter and conversely. (b) We investigate the consequences of applying the "standard" symmetries of isospin, GâÂÂparity, charge conjugation, C, P, T, and chirality using rare and forbidden decays of light mesons such as the {eta}#17;,{eta}' and {omega}. We also investigate the consequences of these symmetries being slightly broken symmetries. We do this by studying selected meson decays using the Crystal Ball detector. (c) We determine the mass, or more precisely the mass difference of the three light quarks (which are inputs to Quantum Chromodynamics) by measuring the decay rate of specially selected {eta}#17; and {eta}' decay modes, again we use the Crystal Ball. (d)We have started a new program to search for the 33 missing cascade baryons using the CLAS detector at the Thomas Jefferson Laboratory. Cascade resonances are very special: they have double strangeness and are quite narrow. This implies that they can be discovered by the missing mass technique in photoproduction reactions such as in {gamma}p{yields}{Xi}{sup #4;âÂÂ}K{sup +}K{sup +}. The cascade program is of particular importance for the upgrade to 12 GeV of the CLAS detector and for design of the Hall D at JLab. (e) Finally, we are getting more involved in a new program to measure the hadronic matter form factor of complex nuclei, in particular the "neutron skin" of {sup 208}Pb, which is of great interest to astroparticle physics for determining the properties of neutron stars. Processes of study are coherent and nonâÂÂcoherent #25;0 photoproduction. The Crystal Ball is uniquely suited for these studies because of the large acceptance, good direction and energy resolution and it is an inclusive detector for the #25;{pi}{sup 0} final state and exclusive for background such as 2#25;{pi}{sup 0}

Characterising the English school stock using a unified national on-site survey and energy database

The recent commitment towards a net-zero target by 2050 will require considerable improvement to the UK’s building stock. Accounting for over 10% of the services energy consumption of the United Kingdom, the education sector will play an important role. This study aims to improve the understanding of English primary and secondary schools, using national on-site survey data with several large-scale disaggregate data sources. Property Data Survey Programme (PDSP) data on 18,970 schools collected between 2012 and 2014, Display Energy Certificate (DEC) and school census data from the same period were linked and processed to form a unified schools dataset. Statistical analyses were undertaken on 10,392 schools, with a focus on energy performance, and the relationship to several building and system characteristics. The analyses may point to the possibility of assessing operational energy use of schools in a more disaggregate manner. New datasets with detailed and accurate disaggregate information on characteristics of buildings, such as those used in this study, provide opportunities to develop more robust models of the building stock. Such data would provide an opportunity to identify pathways for reducing carbon emissions effectively and provide lessons for other organisations seeking to achieve significant reductions for achieving climate change goals

Developing a 3D geometry for Urban energy modelling of Indian cities

The advancement in the field of Urban Building Energy Modelling (UBEM) is assisting urban planners and managers to design and operate cities to meet environmental emission targets. The usefulness of the UBEM depends upon the quality and level of details (LoD) of the inputs to the model. The inadequacy and quality of relevant input data pose challenges. This paper analyses the usefulness of different methodologies for developing a 3D building stock model of Ahmedabad, India, recognizing data gaps and heterogenous development of the city over time. It evaluates the potentials, limitations, and challenges of remote sensing techniques namely (a) Satellite imagery (b) LiDAR and (c) Photogrammetry for this application. Further, the details and benefits of data capturing through UAV assisted Photogrammetry technique for the development of the 3D city model are discussed. The research develops potential techniques for feature detection and model reconstruction using Computer vision on the Photogrammetry reality mesh. Preliminary results indicate that the use of supervised learning for Image based segmentation on the reality mesh detects building footprints with higher accuracy as compared to geometrybased segmentation of the point cloud. This methodology has the potential to detect complex building features and remove redundant objects to develop the semantic model at different LoDs for urban simulations. The framework deployed and demonstrated for the part of Ahmedabad has a potential for scaling up to other parts of the city and other Indian cities having similar urban morphology and no previous data for developing a UBEM

Developing a Data-driven School Building Stock Energy and Indoor Environmental Quality Modelling Method

The school building sector has a pivotal role to play in the transition to a low carbon UK economy. School buildings are responsible for 15% of the country’s public sector carbon emissions, with space heating currently making up the largest proportion of energy use and associated costs in schools. Children spend a large part of their waking life in school buildings. There is substantial evidence that poor indoor air quality and thermal discomfort can have detrimental impacts on the performance, wellbeing and health of schoolchildren and school staff. Maintaining high indoor environmental quality whilst reducing energy demand and carbon emissions in schools is challenging due to the unique operational characteristics of school environments, e.g. high and intermittent occupancy densities or changes in occupancy patterns throughout the year. Furthermore, existing data show that 81% of the school building stock in England was constructed before 1976. Challenges facing the ageing school building stock may be exacerbated in the context of ongoing and future climate change. In recent decades, building stock modelling has been widely used to quantify and evaluate the current and future energy and indoor environmental quality performance of large numbers of buildings at the neighbourhood, city, regional or national level. Building stock models commonly use building archetypes, which aim to represent the diversity of building stocks through frequently occurring building typologies. The aim of this paper is to introduce the Data dRiven Engine for Archetype Models of Schools (DREAMS), a novel, data-driven, archetype-based school building stock modelling framework. DREAMS enables the detailed representation of the school building stock in England through the statistical analysis of two large scale and highly detailed databases provided by the UK Government: (i) the Property Data Survey Programme (PDSP) from the Department for Education (DfE), and (ii) Display Energy Certificates (DEC). In this paper, the development of 168 building archetypes representing 9,551 primary schools in England is presented. The energy consumption of the English primary school building stock was modelled for a typical year under the current climate using the widely tested and applied building performance software EnergyPlus. For the purposes of modelling validation, the DREAMS space heating demand predictions were compared against average measured energy consumption of the schools that were represented by each archetype. It was demonstrated that the simulated fossil-thermal energy consumption of a typical primary school in England was only 7% higher than measured energy consumption (139 kWh/m2/y simulated, compared to 130 kWh/m2/y measured). The building stock model performs better at predicting the energy performance of naturally ventilated buildings,which constitute 97% of the stock, than that of mechanically ventilated ones. The framework has also shown capabilities in predicting energy consumption on a more localised scale. The London primary school building stock was examined as a case study. School building stock modelling frameworks such as DREAMS can be powerful tools that aid decision-makers to quantify and evaluate the impact of a wide range of building stock-level policies, energy efficiency interventions and climate change scenarios on school energy and indoor environmental performance

Modelling platform for schools (MPS): The development of an automated One-By-One framework for the generation of dynamic thermal simulation models of schools

The UK Government has recently committed to achieve net zero carbon status by year 2050. Schools are responsible for around 2% of the UK’s total energy consumption, and around 15% of the UK public sector’s carbon emissions. A detailed analysis of the English school building stock’s performance can help policymakers improve its energy efficiency and indoor environmental quality. Building stock modelling is a technique commonly used to quantify current and future energy demand or indoor environmental quality performance of large numbers of buildings at the neighbourhood, city, regional or national level. ‘Building-by-building’ stock modelling is a modelling technique whereby individual buildings within the stock are modelled and simulated, and performance results are aggregated and analysed at stock level. This paper presents the development of the Modelling Platform for Schools (MPS) – an automated generation of one-by-one thermal models of schools in England through the analysis and integration of a range of data (geometry, size, number of buildings within a school premises etc.) from multiple databases and tools (Edubase/Get Information About Schools, Property Data Survey Programme, Ordanance Survey and others). The study then presents an initial assessment and evaluation of the modelling procedure of the proposed platform. The model evaluation has shown that out of 15,245 schools for which sufficient data were available, nearly 50% can be modelled in an automated manner having a high level of confidence of similarity with the actual buildings. Visual comparison between automatically-generated models and actual buildings has shown that around 70% of the models were, indeed, geometrically accurate

Indoor Air Quality and Overheating in UK Classrooms – an Archetype Stock Modelling Approach

Children spend a large part of their waking lives in school buildings. There is substantial evidence that poor indoor air quality (IAQ) and thermal discomfort can have detrimental impacts on the performance, wellbeing and health of schoolchildren and staff. Maintaining good IAQ while avoiding overheating in classrooms is challenging due to the unique occupancy patterns and heat properties of schools. Building stock modelling has been extensively used in recent years to quantify and evaluate performance of large numbers of buildings at various scales. This paper builds on an archetype stock modelling approach which represents the diversity of the school stock in England through an analysis of The Property Data Survey Programme (PDSP) and the Display Energy Certificates (DEC) databases. The model was used for simulating Indoor-to-Outdoor pollution ratios to estimate indoor air pollution levels (NO2, PM2.5 and CO2) and thermal comfort (overheating) in two climate areas in England: London and the West Pennines. analysis highlighted variations in classrooms' indoor CO2 levels in different seasons and explored the risk of overheating in relation to a classroom's orientation