741 research outputs found

    Weather and climate data for energy applications

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    Weather information plays a critical role in energy applications — from designing and planning to the management and maintenance of building energy systems, renewable energy applications, and smart utility grids. This research examines weather and climate data for energy applications, covering their sources, generation, implementation, and forecasting. Drivers for the use of weather data, data acquisition methods, and parameter characteristics, as well as their impact on energy applications, are critically reviewed. The study also analyses weather data availability from 32 commonly used online sources, considering their cost, features, and resolution. A comprehensive weather data classification is developed based on measurement type, information period, data resolution, and time horizon. The findings indicate that real-time local weather data with high temporal resolution is crucial for optimal energy management and accurate forecasting of energy and environmental behaviours. However, limitations and uncertainties exist in weather data from online sources, particularly for developing countries, due to the limited spatio-temporal coverage

    An ensemble model for predictive energy performance:Closing the gap between actual and predicted energy use in residential buildings

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    The design stage of a building plays a pivotal role in influencing its life cycle and overall performance. Accurate predictions of a building's performance are crucial for informed decision-making, particularly in terms of energy performance, given the escalating global awareness of climate change and the imperative to enhance energy efficiency in buildings. However, a well-documented energy performance gap persists between actual and predicted energy consumption, primarily attributed to the unpredictable nature of occupant behavior.Existing methodologies for predicting and simulating occupant behavior in buildings frequently neglect or exclusively concentrate on particular behaviors, resulting in uncertainties in energy performance predictions. Machine learning approaches have exhibited increased accuracy in predicting occupant energy behavior, yet the majority of extant studies focus on specific behavior types rather than investigating the interactions among all contributing factors. This dissertation delves into the building energy performance gap, with a particular emphasis on the influence of occupants on energy performance. A comprehensive literature review scrutinizes machine learning models employed for predicting occupants' behavior in buildings and assesses their performance. The review uncovers knowledge gaps, as most studies are case-specific and lack a consolidated database to examine diverse behaviors across various building types.An ensemble model integrating occupant behavior parameters is devised to enhance the accuracy of energy performance predictions in residential buildings. Multiple algorithms are examined, with the selection of algorithms contingent upon evaluation metrics. The ensemble model is validated through a case study that compares actual energy consumption with the predictions of the ensemble model and an EnergyPlus simulation that takes occupant behavior factors into account.The findings demonstrate that the ensemble model provides considerably more accurate predictions of actual energy consumption compared to the EnergyPlus simulation. This dissertation also addresses the research limitations, including the reusability of the model and the requirement for additional datasets to bolster confidence in the model's applicability across diverse building types and occupant behavior patterns.In summary, this dissertation presents an ensemble model that endeavors to bridge the gap between actual and predicted energy usage in residential buildings by incorporating occupant behavior parameters, leading to more precise energy performance predictions and promoting superior energy management strategies

    Federated learning framework and energy disaggregation techniques for residential energy management

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    Residential energy use is a significant part of total power usage in developed countries. To reduce overall energy use and save funds, these countries need solutions that help them keep track of how different appliances are used at residences. Non-Intrusive Load Monitoring (NILM) or energy disaggregation is a method for calculating individual appliance power consumption from a single meter tracking the aggregated power of several appliances. To implement any NILM approach in the real world, it is necessary to collect massive amounts of data from individual residences and transfer them to centralized servers, where they will undergo extensive analysis. The centralized fashion of this procedure makes it time-consuming and costly since transferring the data from thousands of residences to the central server takes a lot of time and storage. This thesis proposes utilizing Federated Learning (FL) framework for NILM in order to make the entire system cost-effective and efficient. Rather than collecting data from all clients (residences) and sending it back to the central server, local models are generated on each client’s end and trained on local data in FL. This allows FL to respond more quickly to changes in the environment and handle data locally in a single household, increasing the system’s speed. On top of that, without any data transfer, FL prevents data leakage and preserves the clients’ privacy, leading to a safe and trustworthy system. For the first time, in this work, the performance of deploying FL in NILM was investigated with two different energy disaggregation models: Short Sequence-to-Point (Seq2Point) and Variational Auto-Encoder (VAE). Short Seq2Point with fewer samples as input window for each appliance, tries to simulate the real-time energy disaggregation for the different appliances. Despite having a light-weighted model, Short Seq2Point lacks generalizability and might confront some challenges while disaggregating multi-state appliances

    Innovation in Energy Security and Long-Term Energy Efficiency Ⅱ

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    The sustainable development of our planet depends on the use of energy. The increasing world population inevitably causes an increase in the demand for energy, which, on the one hand, threatens us with the potential to encounter a shortage of energy supply, and, on the other hand, causes the deterioration of the environment. Therefore, our task is to reduce this demand through different innovative solutions (i.e., both technological and social). Social marketing and economic policies can also play their role by affecting the behavior of households and companies and by causing behavioral change oriented to energy stewardship, with an overall switch to renewable energy resources. This reprint provides a platform for the exchange of a wide range of ideas, which, ultimately, would facilitate driving societies toward long-term energy efficiency

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    A systematic method of retrofit of urban residential buildings for design, decision and policymaking in Chongqing, China

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    Energy consumption and carbon emission have become a challenge in old residences in China. Old residential buildings remain dilapidated with their poorly insulated building façade and significant potential in energy and carbon conservation. Yet the current circumstances are found very complicated due to the unclear reality and complex nature of the diversities of building conditions, resident preferences, economy, and social factors of coordination and motivation. Retrofit of urban residential buildings (RURB) is treated not as a systematic research topic, but rather as a project-based construction process in current knowledge. This linear thinking has been argued which caused empiricism problems since interconnections between retrofit system participants are notably neglected. To achieve the research objectives and justify the system thinking perspective, this thesis explores and understands the system cognition and interactions of the Retrofit of Urban Residential Buildings (RURB). The adopted system thinking method, interviews, and questionnaire survey methods are used based on causal layered analysis for the comprehensive system definition, boundary, variables, and participants. System interactions between system players and variables are also analysed by using the causal loop diagram method, hence systemic problems can be discovered. With this theoretical basis, a case study of the Chongqing city zone is selected to support the system theory with urban retrofit effects. Results of retrofitting benefits and costs are obtained through modelling, field survey, energy simulation, and calculation approaches. They are evaluated as five retrofit criteria of energy, cost, comfort, function, and safety improvements. A total of the six retrofit scenarios with four referencing building types are applied to multi-criteria decision-making analysis, as the analytic network process for the weighted values of retrofit benefits and costs. The retrofitting potential, solutions for systemic problems, and hence the suggestions for future policymaking are found and discussed. This research develops an innovative and coherent method to support future RURB decision-making and policymaking by providing both a theoretical basis and reliable data source, which can become a generalised approach to RURB analysis in other city conditions

    Structural optimization in steel structures, algorithms and applications

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    L'abstract Ăš presente nell'allegato / the abstract is in the attachmen

    An investigation into the environmental sustainability of the South African ornamental horticultural industry

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    The ornamental horticultural industry makes use of natural resources to grow plants and produce allied products to sell to consumers, landscapers, retail garden centres, hardware stores, supermarkets, and government, but at what cost to the environment? The aim of this work was to determine the current environmental awareness of growers and garden centre retailers within the ornamental horticultural industry in South Africa. Followed by an investigation into the current business practices that promote sustainable natural resource use and management as well as the obstacles and challenges that the industry faces with implementing legislation and recommendations of best practices. The study was conducted over an 18-month period and 41 growers and retail garden centres in eight of the provinces in South Africa (Appendix 10) participated in research. In each case, the study participant was asked to complete the questionnaire and where possible, a site visit was conducted and / or a semi-structured interview as well as participatory observations followed to give a comprehensive overview of the sustainability practices of the businesses. These results were then compared to international best practices and similar research conducted globally by the ornamental horticultural industry. A review of international best practices in the ornamental horticultural industry showed six environmental resources namely soil, water, fertilizers, pesticides, energy, and waste. This was seen to be common to most studies involved in the production, growth, maintenance and sales of plants and allied products. This information was used to compile a best management practice manual for South African ornamental horticulture with guidelines and practical examples for conserving and managing natural resource usage and reducing the environmental impacts of the industry. Much research has been done on the exploitation and degradation of resources due to urbanisation, industrial activities, and agricultural practices. The resources are essential to the ornamental horticultural industry but if exploited or misused, can have detrimental effects on the environmental productivity of the industry and ultimately the “Sustainable Development Goals” prescribed by the United Nations. The linking of the relevant sustainable development goals to the 9 key factors of the green economy strategized by the South African government will enable the ornamental horticultural industry to play a greater part in the green and circular economy by providing nature-based solutions to environmental problems that it is facing such as climate change and pollution.Environmental SciencesD. Phil. (Environmental Management

    A strategic turnaround model for distressed properties

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    The importance of commercial real estate is clearly shown by the role it plays, worldwide, in the sustainability of economic activities, with a substantial global impact when measured in monetary terms. This study responds to an important gap in the built environment and turnaround literature relating to the likelihood of a successful distressed commercial property financial recovery. The present research effort addressed the absence of empirical evidence by identifying a number of important factors that influence the likelihood of a successful distressed, commercial property financial recovery. Once the important factors that increase the likelihood of recovery have been determined, the results can be used as a basis for turnaround strategies concerning property investors who invest in distressed opportunities. A theoretical turnaround model concerning properties in distress, would be of interest to ‘opportunistic investing’ yield-hungry investors targeting real estate transactions involving ‘turnaround’ potential. Against this background, the main research problem investigated in the present research effort was as follows: Determine the important factors that would increase the likelihood of a successful distressed commercial property financial recovery. A proposed theoretical model was constructed and empirically tested through a questionnaire distributed physically and electronically to a sample of real estate practitioners from across the globe, and who had all been involved, directly or indirectly, with reviving distressed properties. An explanation was provided to respondents of how the questionnaire was developed and how it would be administered. The demographic information pertaining to the 391 respondents was analysed and summarised. The statistical analysis performed to ensure the validity and reliability of the results, was explained to respondents, together with a detailed description of the covariance structural equation modelling method used to verify the proposed theoretical conceptual model. vi The independent variables of the present research effort comprised; Obsolescence Identification, Capital Improvements Feasibility, Tenant Mix, Triple Net Leases, Concessions, Property Management, Contracts, Business Analysis, Debt Renegotiation, Cost-Cutting, Market Analysis, Strategic Planning and Demography, while the dependent variable was The Perceived Likelihood of a Distressed Commercial Property Financial Recovery. After analysis of the findings, a revised model was then proposed and assessed. Both validity and reliability were assessed and resulted in the following factors that potentially influence the dependent variables; Strategy, Concessions, Tenant Mix, Debt Restructuring, Demography, Analyse Alternatives, Capital Improvements Feasibility, Property Management and Net Leases while, after analysis, the dependent variable was replaced by two dependent variables; The Likelihood of a Distressed Property Turnaround and The Likelihood of a Distressed Property Financial Recovery. The results showed that Strategy (comprising of items from Strategic Planning, Business Analysis, Obsolescence Identification and Property Management) and Concessions (comprising of items from Concessions and Triple Net Leases) had a positive influence on both the dependent variables. Property Management (comprising of items from Business Analysis, Property Management, Capital Improvements Feasibility and Tenant Mix) had a positive influence on Financial Turnaround variable while Capital Improvements Feasibility (comprising of items from Capital Improvements Feasibility, Obsolescence Identification and Property Management) had a negative influence on both. Demography (comprising of items only from Demography) had a negative influence on the Financial Recovery variable. The balance of the relationships were depicted as non-significant. The present research effort presents important actions that can be used to influence the turnaround and recovery of distressed real estate. The literature had indicated reasons to recover distressed properties as having wide-ranging economic consequences for the broader communities and the countries in which they reside. The turnaround of distressed properties will not only present financial rewards for opportunistic investors but will have positive effects on the greater community and economy and, thus, social and economic stability. Vii With the emergence of the COVID-19 pandemic crisis, issues with climate change and sustainability, global demographic shifts, changing user requirements, shifts in technology, the threat of obsolescence, urbanisation, globalisation, geo-political tensions, shifting global order, new trends and different generational expectations, it is becoming more apparent that the threat of distressed, abandoned and derelict properties is here to stay, and which will present future opportunities for turnaround, distressed property owners, as well as future worries for urban authorities and municipalities dealing with urban decay. The study concluded with an examination of the perceived limitations of the study as well as presenting a comprehensive range of suggestions for further research.Thesis (PhD) -- Faculty of Engineering, Built Environment and Information Technology, School of the built Environment, 202

    Understanding building and urban environment interactions: An integrated framework for building occupancy modelling

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    Improving building energy efficiency requires accurate modelling and a comprehensive understanding of how occupants use building space. This thesis focuses on modelling building occupancy to enhance the predictive accuracy of occupancy patterns and gain a better understanding of the causal reasons for occupancy behaviour. A conceptual framework is proposed to relax the restriction of isolated building analysis, which accounts for interactions between buildings, its occupants, and other urban systems, such as the effects of transport incidents on occupancy and circulation in buildings. This thesis also presents a counterpart mapping of the framework that elaborates the links between modelling of transport and building systems. To operationalise the proposed framework, a novel modelling approach which has not been used in the current context, called the hazard-based model, is applied to model occupancy from a single building up to a district area. The proposed framework is further adapted to integrate more readily with transport models, to ensure that arrivals and departures to and from the building are consistent with the situation of the surrounding transport systems. The proposed framework and occupancy models are calibrated and validated using Wi-Fi data and other variables, such as transport and weather parameters, harvested from the South Kensington campus of Imperial College London. In addition to calibrating the occupancy model, integrating a travel simulator produces synthetic arrivals into or around the campus, which are further distributed over campus buildings via an adapted technique and feed the occupancy simulations. The model estimation results reveal the causal reasons for or exogenous effects on individual occupancy states. The validation results confirm the ability of the proposed models to predict building occupancy accurately both on average and day by day across the future dataset. Finally, evaluating occupancy simulations for various hypothetical scenarios provides valuable suggestions for efficient building design and facility operation.Open Acces
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