Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations

Reliability Models and Failure Detection Algorithms for Wind Turbines

Durante las pasadas décadas, la industria eólica ha sufrido un crecimiento muysignificativo en Europa llevando a la generación eólica al puesto más relevanteen cuanto a producción energética mediante fuentes renovables. Sin embargo, siconsideramos los aspectos económicos, el sector eólico todavía no ha alcanzadoel nivel competitivo necesario para batir a los sistemas de generación de energíaconvencionales.Los costes principales en la explotación de parques eólicos se asignan a lasactividades relacionadas con la Operación y Mantenimiento (O&M). Esto se debeal hecho de que, en la actualidad, la Operación y Mantenimiento está basadaprincipalmente en acciones correctivas o preventivas. Por tanto, el uso de técnicaspredictivas podría reducir de forma significativa los costes relacionados con lasactividades de mantenimiento mejorando así los beneficios globales de la explotaciónde los parques eólicos.Aunque los beneficios del mantenimiento predictivo se consideran cada díamás importantes, existen todavía la necesidad de investigar y explorar dichastécnicas. Modelos de fiabilidad avanzados y algoritmos de predicción de fallospueden facilitar a los operadores la detección anticipada de fallos de componentesen los aerogeneradores y, en base a ello, adaptar sus estrategias de mantenimiento.Hasta la fecha, los modelos de fiabilidad de turbinas eólicas se basan, casiexclusivamente, en la edad de la turbina. Esto es así porque fueron desarrolladosoriginalmente para máquinas que trabajan en entornos ‘amigables’, por ejemplo, enel interior de naves industriales. Los aerogeneradores, al contrario, están expuestosa condiciones ambientales altamente variables y, por tanto, los modelos clásicosde fiabilidad no reflejan la realidad con suficiente precisión. Es necesario, portanto, desarrollar nuevos modelos de fiabilidad que sean capaces de reproducir el comportamiento de los fallos de las turbinas eólicas y sus componentes, teniendoen cuenta las condiciones meteorológicas y operacionales en su emplazamiento.La predicción de fallos se realiza habitualmente utilizando datos que se obtienendel sistema de Supervisión Control y Adquisición de Datos (SCADA) o de Sistemasde Monitorización de Condición (CMS). Cabe destacar que en turbinas eólicasmodernas conviven ambos tipos de sistemas y la fusión de ambas fuentes de datospuede mejorar significativamente la detección de fallos. Esta tesis pretende mejorarlas prácticas actuales de Operación y Mantenimiento mediante: (1) el desarrollo demodelos avanzados de fiabilidad y detección de fallos basados en datos que incluyanlas condiciones ambientales y operacionales existentes en los parques eólicos y (2)la aplicación de nuevos algoritmos de detección de fallos que usen las condicionesambientales y operacionales del emplazamiento, así como datos procedentes tantode sistemas SCADA como CMS. Estos dos objetivos se han dividido en cuatrotareas.En la primera tarea se ha realizado un análisis exhaustivo tanto de los fallosproducidos en un amplio conjunto de aerogeneradores (amplio en número de turbinasy en longitud de los registros) como de sus tiempos de parada asociados. De estaforma, se han visualizado los componentes que más fallan en función de la tecnologíadel aerogenerador, así como sus modos de fallo. Esta información es vital para eldesarrollo posterior de modelos de fiabilidad y mantenimiento.En segundo lugar, se han investigado las condiciones meteorológicas previasa sucesos con fallos de los principales componentes de los aerogeneradores. Seha desarrollado un entorno de aprendizaje basado en datos utilizando técnicas deagrupamiento ‘k-means clustering’ y reglas de asociación ‘a priori’. Este entorno escapaz de manejar grandes cantidades de datos proporcionando resultados útiles yfácilmente visualizables. Adicionalmente, se han aplicado algoritmos de detecciónde anomalías y patrones para encontrar cambios abruptos y patrones recurrentesen la serie temporal de la velocidad del viento en momentos previos a los fallosde los componentes principales de los aerogeneradores. En la tercera tarea, sepropone un nuevo modelo de fiabilidad que incorpora directamente las condicionesmeteorológicas registradas durante los dos meses previos al fallo. El modelo usados procesos estadísticos separados, uno genera los sucesos de fallos, así comoceros ocasionales mientras que el otro genera los ceros estructurales necesarios paralos algoritmos de cálculo. Los posibles efectos no observados (heterogeneidad) en el parque eólico se tienen en cuenta de forma adicional. Para evitar problemas de‘over-fitting’ y multicolinearidades, se utilizan sofisticadas técnicas de regularización.Finalmente, la capacidad del modelo se verifica usando datos históricos de fallosy lecturas meteorológicas obtenidas en los mástiles meteorológicos de los parqueseólicos.En la última tarea se han desarrollado algoritmos de predicción basados encondiciones meteorológicas y en datos operacionales y de vibraciones. Se ha‘entrenado’ una red de Bayes, para predecir los fallos de componentes en unparque eólico, basada fundamentalmente en las condiciones meteorológicas delemplazamiento. Posteriormente, se introduce una metodología para fusionar datosde vibraciones obtenidos del CMS con datos obtenidos del sistema SCADA, conel objetivo de analizar las relaciones entre ambas fuentes. Estos datos se hanutilizado para la predicción de fallos en el eje principal utilizando varios algoritmosde inteligencia artificial, ‘random forests’, ‘gradient boosting machines’, modelosgeneralizados lineales y redes neuronales artificiales. Además, se ha desarrolladouna herramienta para la evaluación on-line de los datos de vibraciones (CMS)denominada DAVE (‘Distance Based Automated Vibration Evaluation’).Los resultados de esta tesis demuestran que el comportamiento de los fallos delos componentes de aerogeneradores está altamente influenciado por las condicionesmeteorológicas del emplazamiento. El entorno de aprendizaje basado en datos escapaz de identificar las condiciones generales y temporales específicas previas alos fallos de componentes. Además, se ha demostrado que, con los modelos defiabilidad y algoritmos de detección propuestos, la Operación y Mantenimiento delas turbinas eólicas puede mejorarse significativamente. Estos modelos de fiabilidady de detección de fallos son los primeros que proporcionan una representaciónrealística y específica del emplazamiento, al considerar combinaciones complejasde las condiciones ambientales, así como indicadores operacionales y de estadode operación obtenidos a partir de la fusión de datos de vibraciones CMS y datosdel SCADA. Por tanto, este trabajo proporciona entornos prácticos, modelos yalgoritmos que se podrán aplicar en el campo del mantenimiento predictivo deturbinas eólicas.<br /

A Data-Driven Approach for Generating Vortex Shedding Regime Maps for an Oscillating Cylinder

Recent developments in wind energy extraction methods from vortex-induced vibration (VIV) have fueled the research into vortex shedding behaviour. The vortex shedding map is vital for the consistent use of normalized amplitude and wavelength to validate the predicting power of forced vibration experiments. However, there is a lack of demonstrated methods of generating this map at Reynolds numbers feasible for energy generation due to the high computational cost and complex dynamics. Leveraging data-driven methods addresses the limitations of the traditional experimental vortex shedding map generation, which requires large amounts of data and intensive supervision that is unsuitable for many applications and Reynolds numbers. This thesis presents a data-driven approach for generating vortex shedding maps of a cylinder undergoing forced vibration that requires less data and supervision while accurately extracting the underlying vortex structure patterns. The quantitative analysis in this dissertation requires the univariate time series signatures of local fluid flow measurements in the wake of an oscillating cylinder experiencing forced vibration. The datasets were extracted from a 2-dimensional computational fluid dynamic (CFD) simulation of a cylinder oscillating at various normalized amplitude and wavelength parameters conducted at two discrete Reynolds numbers of 4000 and 10,000. First, the validity of clustering local flow measurements was demonstrated by proposing a vortex shedding mode classification strategy using supervised machine learning models of random forest and -nearest neighbour models, which achieved 99.3% and 99.8% classification accuracy using the velocity sensors orientated transverse to the pre-dominant flow (), respectively. Next, the dataset of local flow measurement of the -component of velocity was used to develop the procedure of generating vortex shedding maps using unsupervised clustering techniques. The clustering task was conducted on subsequences of repeated patterns from the whole time series extracted using the novel matrix profile method. The vortex shedding map was validated by reproducing a benchmark map produced at a low Reynolds number. The method was extended to a higher Reynolds number case of vortex shedding and demonstrated the insight gained into the underlying dynamical regimes of the physical system. The proposed multi-step clustering methods denoted Hybrid Method B, combining Density-Based Clustering Based on Connected Regions with High Density (DBSCAN) and Agglomerative algorithms, and Hybrid Method C, combining -Means and Agglomerative algorithms demonstrated the ability to extract meaningful clusters from more complex vortex structures that become increasingly indistinguishable. The data-driven methods yield exceptional performance and versatility, which significantly improves the map generation method while reducing the data input and supervision required

Fractal Analysis and Chaos in Geosciences

The fractal analysis is becoming a very useful tool to process obtained data from chaotic systems in geosciences. It can be used to resolve many ambiguities in this domain. This book contains eight chapters showing the recent applications of the fractal/mutifractal analysis in geosciences. Two chapters are devoted to applications of the fractal analysis in climatology, two of them to data of cosmic and solar geomagnetic data from observatories. Four chapters of the book contain some applications of the (multi-) fractal analysis in exploration geophysics. I believe that the current book is an important source for researchers and students from universities

Forest genomics and biotechnology

This Research Topic addresses research in genomics and biotechnology to improve the growth and quality of forest trees for wood, pulp, biorefineries and carbon capture. Forests are the world’s greatest repository of terrestrial biomass and biodiversity. Forests serve critical ecological services, supporting the preservation of fauna and flora, and water resources. Planted forests also offer a renewable source of timber, for pulp and paper production, and the biorefinery. Despite their fundamental role for society, thousands of hectares of forests are lost annually due to deforestation, pests and pathogens and urban development. As a consequence, there is an increasing need to develop trees that are more productive under lower inputs, while understanding how they adapt to the environment and respond to biotic and abiotic stress. Forest genomics and biotechnology, disciplines that study the genetic composition of trees and the methods required to modify them, began over a quarter of a century ago with the development of the first genetic maps and establishment of early methods of genetic transformation. Since then, genomics and biotechnology have impacted all research areas of forestry. Genome analyses of tree populations have uncovered genes involved in adaptation and response to biotic and abiotic stress. Genes that regulate growth and development have been identified, and in many cases their mechanisms of action have been described. Genetic transformation is now widely used to understand the roles of genes and to develop germplasm that is more suitable for commercial tree plantations. However, in contrast to many annual crops that have benefited from centuries of domestication and extensive genomic and biotechnology research, in forestry the field is still in its infancy. Thus, tremendous opportunities remain unexplored. This Research Topic aims to briefly summarize recent findings, to discuss long-term goals and to think ahead about future developments and how this can be applied to improve growth and quality of forest trees. Mini-review articles are sought in forest genomics and biotechnology, with a focus on future directions applied to (1) genetic engineering, (2) adaptation, (3) genomics of conifers and hardwoods, (4) cell wall and wood formation, (5) development (6) metabolic engineering (7) biotic and abiotic resistance and (8) the biorefinery

Exploring natural and engineered resistance to potyviruses

Viruses are ubiquitous in natural growth environments and cause severe losses to crop yields, globally. Approximately 30% of plant viruses described to date are grouped within the family Potyviridae, making it one of the largest plant virus families. Furthermore, certain potyvirus species can cause devastating diseases in several agriculturally and economically important crops. Hence, gaining insight into potyvirus resistance and recovery mechanisms in plants is an important research focus. This thesis firstly explores how environmental cues can modulate the activity of a central form of viral defence, namely RNA silencing. Specifically, high temperatures and low light intensities were found to increase the efficacy of viral RNA silencing in Arabidopsis, resulting in recovery from infection by Turnip Mosaic Virus. The biological context and potential for agricultural exploitation of these phenomena are discussed. Secondly, this thesis explores the ability to engineer resistance alleles using the latest genome editing techniques. Specifically, resistance to Turnip Mosaic Virus was successfully engineered in Arabidopsis by CRISPR/Cas9-induced deletion of a known susceptibility factor eIF(iso)4E. Biotechnological methods to implement this proof of concept research in crop species were also investigated

Ecology, culture and cognition: a text book on the principles of environmental design

[This] study aims to explore the notion that human achievements, i.e., cultural, technological, architectural, etc., are an outcome of the interaction between ecology, culture and cognitive structure. Such interaction is thought to set out a condition of stability, compatibility and fitness which characterises various vernacular cultures. These notions ought to be investigated and hence utilised in design ideas and design processes. To illustrate the various aspects of this interaction, the thesis has adopted a holistic view which incorporates many elements that underly the environmental phenomena; its structure, its laws of evolution and its adaptive processes. The following is a brief summary of each chapter of the thesis.Chapter One: In any design research it is more important to arrive at appropriate identification of a problem before being preoccupied with 'assumptions' to solve that problem on the basis of its 'external' appearance. Each environment has a specific structure which accommodates in a certain pattern its various components such as the social boundaries of interaction, the particular physical structure, building patterns, behaviour, mode of thought, economic system and so on. It is only by tracing the history of development of each of these components within this structure that a solution can be fitting and relevant.The chapter reviews some problems and controversies raised by adopting a misfit technology and its implication on various cultures as well as on Architecture.Chapter Two: This chapter suggests a general theoretical framework which rejects the harmful and unifying effects of those 'fragmented' approaches within design disciplines. In fact they came as an outcome of the passion for misfit technologies, the non - environmental views of culture and ideologies normally associated with them. It is hence the interplay of the three elements of Ecology, Culture and Cognition that result in architectural quality most fit to its context. The objectives of such a framework are: the protection of the natural ecosystems and their manifestations in design; the establishment of a self - sustaining way of life; and finally, setting policies that give priority to bettering the ecological qualities as a basis for improving other aspects.Chapter Three: In this chapter a broadening perspective is introduced to define ecology according to its concerns for the conditions and interactions that determine the distribution and abundance of organism in a certain setting. The perspective includes culture as well as the other biological and physical factors on the basis of considering culture as a manifestation of man's adaptation to that setting. It is very important to consider the role of ecology in differentiating various societies; their cultures and architectural forms.Chapter Four: The second element, culture, according to the school of cultural- ecology, is made up of the modes of thought, the ideologies, energy systems, artifacts, the organisation of social relations, norms and beliefs and the total range of customary behaviour, all of which have been influenced by the physical setting. The concept of 'cultural core', introduced by J. Steward, is adopted for its importance in distinguishing cultural features in terms of their physical belonging. It helps, hence, to advocate solutions more fitting to their 'authentic context' in the face of the bustling, overlapping and usually more abstract cultural features of the external phase (secondary features).Chapter Five: Knowledge is the central element in design, and cognition has been defined as the activity of knowing: the acquisition, organisation, and use of knowledge. The human cognitive structure selects and interprets environmental information in the construction of its own knowledge, rather than passively copying the information. The mind does this to make the environment 'then' fit in with its own existing mental framework.Chapter Six: Because man and nature form two elements in one system, man has accumulated a profound knowledge of the various elements in nature including natural materials. This knowledge is x embeded so deeply in his psychological structure that his innate disposition towards natural elements has been extended to include all interactional modes, subsystems and visual structures which they initiate.The concept of schemata was introduced within cognitive psychology to explain some controversial issues in the field of accepting, restoring and processing information. Schema is defined generally as a data structure for representing the generic concepts stored in memory. There are schemata representing our knowledge about events, actions, objects, etc. They also contain the network of interrelations between these concepts. It has been suggested that the source of this knowledge which schema represents comes from one of two resources; 1) immediate information of the physical objects, 2) the innate and stored knowledge in the human mind. Both resources, however, can provide information to what the study calls experiential schemata.The important contribution the study offers is the concept of the cosmocognitive schemata. They are the schemata that represent the point where both organism and the universe meet and represent, man's extension in space and time. With these schemata we can explain many phenomena in which people of totally different cultures, different experiential schemata, respond and react similarly. In other words, the various authentic capacities of objects, their various properties and potential dispositions towards interactions are all taking precedence in the organism's neural system.The concluding notion of this important chapter is that man has been vividly and maybe unself- consciously utilising the 'cosmocognitive' knowledge in the adaptational processes, blended with activities of the experiential knowledge, in the elaboration of the various architectural forms and patterns. Therefore, it is suggested that it is extremely important to establish a theory of environmental quality based on cognitive knowledge.Chapter Seven and Chapter Eight: In these two chapters, the study introduces the most influential factors which define the ecological setting in general. These factors are considered as being the permanent constructs of human cognitive knowledge and hence have to be well studied before making any decision concerning the nature of the design solution proposed to any society.Chapter Nine: It is suggested that the influence of ecology and nature on human beings takes place and is utilised over long processes of adaptation. The mechanism and other elements of these processes are explicitly demonstrated through a model that the study elaborates. The main idea this model presents is that man, during the emergence of his settlement, initially responds to nature and the physical properties of that setting. He first develops prototypical patterns to embody their impact, according to which he then develops his social and behavioural patterns. Out of the interaction of these components and their various elements, and by reference to his experiential and innate knowledge, he then establishes his traditional culture of which architectural phenomena is the most conspicuous feature.Chapter Ten: Beyond the aesthetic values of architecture: decorative form and ornaments, and beyond the persistance of architectural pattern and activity types lie empirical, structural, functional and practical principles. The basic aim of arriving at a concrete understanding of what underlies the aesthetic characteristics is that once such an understanding becomes possible, designers would be able to manipulate their design ideas following the same principles of authenticity and purposefulness rather than attempting further implication or inventing more fantasies.The title implies that material's authentic properties, architectural and structural elements and activities have cognitive values which are represented in certain characteristics. And it is these values that a designer whould, in fact, search for, if satisfying people's real preferences is one of his interests.Chapter Eleven: The outcome of the interaction between ecological /cultural variables and cognitive structure consists of several components. These have to be carefully matched in setting design criteria within any context: They can be referred to in any judgement over the fitness and appropriateness of any design idea in hand