1,720,996 research outputs found
Sviluppo e validazione di modelli energetici di edifici a partire dal monitoraggio dei consumi, per migliorare l’efficienza energetica, prevedere il carico e ottimizzare le utenze tecnologiche
No full textBuildings are responsible for approximately 40% of energy consumption and 36% of CO2 emissions across the European Union (EU). Given these percentages, it’s easy to imagine that energy efficiency in buildings is a crucial topic, representing a key aspect in defining current energy policies at both national and community levels.
In this context, the development of reliable energy models is a crucial resource within the activities of defining a Building Management System (BMS). A robust energy model can provide accurate estimates of consumption related to major utilities, such as Heating, Ventilating, and Air Conditioning (HVAC), which constitute a significant portion of total building energy consumption, especially in the tertiary sector. Additionally, technological, economic, and social transitions in recent years have had a significant impact on the energy landscape. Improvements in Information and Communication Technology (ICT), energy market liberalization, the emergence of active consumers and small local generation (turning consumers into “prosumers”), and increased environmental awareness have necessitated the development of new technologies and solutions for managing energy-intensive utilities.
An essential aspect of utility management lies in the advanced features available to BMS, which can support the integration of unpredictable resources (such as renewable energy production), controllable utilities (such as HVAC systems), and the use of advanced Demand Response (DR) or Demand Side Management (DSM) algorithms. These techniques have become essential in addressing the challenges of managing multiple utilities, which can be complex and create issues related to safety, reliability, and power quality within an energy system. The presence of energy storage systems and new load types, such as electric vehicles (EVs), further complicates load absorption curves.
In this context, simulation, forecasting, and automated decision-making tools derived from sophisticated data analytics algorithms are becoming crucial for designing, managing, and operating modern energy systems. The doctoral research discussed in this thesis, conducted at the IEES (Intelligent Electric Energy Systems) laboratory of the Department of Naval, Electrical, Electronic, and Telecommunication Engineering (DITEN) and funded by IESolutions – Intelligent Energy Solutions, primarily focused on studying and developing energy models for large tertiary sector buildings. The research also involved developing consumption prediction algorithms and applying them to real-world cases, with the goal of optimizing major energy systems, enhancing energy performance, and improving efficiency while maintaining user comfort.
These activities were carried out within national and European research projects, alongside the development of computerized tools for monitoring energy consumption, potentially destined for technology transfer and commercialization. In the first chapter of this thesis, the application context in which algorithms and models were developed is analyzed, and the requirements are defined.
The second chapter describes the data collection methodology from the field. Specifically, it discusses the hardware and software architecture used in an energy consumption monitoring system across various application domains, explaining the reasons for the chosen approach and the peculiarities of the considered tools.
The third chapter defines the building modeling procedure used and provides a description of the software tools employed, along with their key features. Finally, an application case of an energy model for a building owned by the Department of Educational Sciences (DISFOR) at the University of Genoa is defined and described.
In the fourth chapter, predictive algorithms are described, both concerning energy production and load curves. Different methodologies are evaluated for various scenarios, which may involve utilities in tertiary or residential buildings.
The fifth chapter discusses the application of the developed prediction and diagnostic methodologies, both in large buildings and in new scenarios and contexts that emerged during the doctoral period. Examples include energy communities.
Lastly, the thesis concludes with insights into research activities and potential developments, encompassing both research and technology transfer. Additionally, a list of projects undertaken during the doctoral years and scientific publications is presented
Electrical consumption forecasting in hospital facilities: An application case
No full textThe topic of energy efficiency applied to buildings represents one of the key aspects in today’s interna- tional energy policies. Emissions reduction and the achievement of the targets set by the Kyoto Protocol are becoming a fundamental concern in the work of engineers and technicians operating in the energy management field. Optimal energy management practices need to deal with uncertainties in generation and demand, hence the development of reliable forecasting methods is an important priority area of research in electric energy systems. This paper presents a load forecasting model and the way it was applied to a real case study, to forecast the electrical consumption of the Cellini medical clinic of Turin. The model can be easily integrated into a Building Management System or into a real time monitoring system. The load forecasting is performed through the implementation of an artificial neural network (ANN). The proposed multi-layer perceptron ANN, based on a back propagation training algorithm, is able to take as inputs: loads, data concerning the type of day (e.g. weekday/holiday), time of the day and weather data. In particular, this work focuses on providing a detailed analysis and an innovative formal procedure for the selection of all the ANN parameters
Optimization Model for Demand Flexibility Based on a Non-Intrusive Load Disaggregation Tool
No full textIn the last years there has been a growing spread of smart meters that measure and communicate residential electricity consumption, allowing the development of new energy efficiency services. An interesting application involves the disaggregation of the main home appliances from the aggregated consumption signal. This is essential, to make predictions and optimizations for the implementation of Demand Side Management (DSM) strategies, also applicable to Energy Communities perspective. This paper presents an infrastructure and a set of algorithms to collect data from Italian second-generation smart meters and break down the total power measured by them into those used by main individual appliances. By using Non-Intrusive Load Monitoring (NILM) techniques, the proposed methodology can identify when a specific appliance is operating and create an appliance's properties database through unsupervised clustering algorithms applied to the detected devices. The system is tested using data collected from three households in Italy and results are reported in the paper. As a further development of this work, a demonstration of the application of the proposed NILM algorithm outputs as inputs for the optimization of houses power demand is performed, in order to maximize the shared energy in the context of Renewable Energy Communities
Miglioramento dell’efficienza energetica in ambito ospedaliero: modello funzionale e caso studio della clinica oculistica dell’università di Genova
No full textA Non-Intrusive Load Disaggregation Tool based on Smart Meter Data for Residential Buildings
No full textIn the last years there has been a growing spread of smart meters that measure and communicate residential electricity consumption, allowing the development of new energy efficiency services. An interesting application involves the disaggregation of the main home appliances from the aggregated consumption signal. This is essential, in order to make predictions and optimizations for the implementation of Demand Side Management (DSM) strategies, also applicable to Energy Communities perspective. In this paper an infrastructure and a set of algorithms to collect data from Italian second-generation smart meters and break down the total power measured by them into those used by main individual appliances are presented. By using Non-Intrusive Load Monitoring (NILM) techniques, the proposed methodology can identify when a specific appliance is operating and then, through unsupervised clustering algorithms applied to the detected devices, create an appliance's properties database. The system is also tested using data collected from three households in Italy and results are reported in the paper
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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