187 research outputs found
Machine Learning Techniques for Ensuring the Health of Citizens and the Environmental Sustainability of Buildings
No full textQuesta tesi esplora il potenziale trasformativo della data science nell'ottimizzazione dei consumi energetici degli edifici, con l’obiettivo di migliorare il comfort degli occupanti e la qualità ambientale. Con l'evolversi degli ambienti urbani, l'integrazione di soluzioni avanzate basate sui dati negli edifici pubblici e privati sta diventando sempre più cruciale. Tali tecnologie sono fondamentali per ottimizzare il comfort termico, la qualità dell'aria e l'illuminazione, contribuendo così a creare ambienti interni che promuovono il benessere degli occupanti.
La ricerca inizia con l’implementazione di sistemi intelligenti per gli edifici, che utilizzano una varietà di sensori e meccanismi di controllo per mantenere condizioni ottimali all’interno. Questi sistemi non solo supportano il comfort degli occupanti, ma migliorano anche l’efficienza energetica, un approccio essenziale per la creazione di spazi sostenibili che bilanciano gli obiettivi ambientali e il benessere umano.
Un aspetto centrale di questo studio riguarda l'analisi delle tendenze di consumo energetico in Italia, in particolare nel settore residenziale, che riflette i dati utilizzati nella ricerca. Tale analisi sottolinea l'importanza della previsione del carico energetico, fondamentale per comprendere i consumi e orientare le pratiche sostenibili. Il consumo di energia elettrica influisce profondamente sia sugli stili di vita individuali che sullo sviluppo economico. Utilizzando i dati dell’Agenzia Internazionale per l'Energia, questo studio evidenzia i modelli di consumo energetico in Italia, con particolare attenzione al ruolo del settore residenziale nella formazione della domanda energetica nazionale. L’accurata previsione di queste tendenze è cruciale per misurare i progressi verso gli obiettivi di sostenibilità. Analizzando i consumi per settore e le tipologie di energia utilizzata, la ricerca fornisce una visione dettagliata delle dinamiche energetiche, supportando una gestione ottimale del carico che risponda alle esigenze degli occupanti.
Su questa base, la tesi esplora la relazione dinamica tra gestione dell’energia e comfort degli occupanti, focalizzandosi sul miglioramento delle capacità previsionali dei carichi energetici. È stato sviluppato un modello di simulazione per prevedere i pattern di consumo elettrico, che costituisce il nucleo di un sistema avanzato di previsione. Questo modello permette di ottimizzare l’uso dell’energia negli edifici residenziali e commerciali, bilanciando la gestione energetica con il comfort degli occupanti. Analizzando metodi esistenti, la tesi ne evidenzia punti di forza e limiti, fornendo le basi per progettare un sistema di previsione del carico più preciso e adattivo, rispondente alla variabilità dell’uso dell’energia nel mondo reale. Questo approccio non solo promuove l’efficienza energetica, ma crea anche un ambiente interno confortevole, allineando l’ottimizzazione energetica con obiettivi centrati sugli occupanti.
Il passo successivo della ricerca si concentra sull’implementazione pratica, con l’analisi dei dati provenienti dal cloud per studiare i modelli di domanda di energia in contesti reali. Una delle sfide principali è stata sviluppare un’interfaccia software sicura ed efficiente per l'accesso ai dati nel cloud, riuscendo a garantire il trasferimento dei dati per previsioni accurate della domanda di energia.This thesis explores the transformative potential of data science in optimizing building energy consumption and enhancing occupant comfort and environmental quality. As urban environments evolve, the integration of advanced data-driven solutions in public and private buildings becomes increasingly crucial. These technologies are pivotal for achieving thermal comfort, air quality, and lighting optimization, which contribute to a supportive indoor environment aligned with the well-being of occupants.
The research begins with the deployment of smart building systems incorporating a variety of sensors and control mechanisms. These systems are designed to maintain optimal indoor conditions, directly supporting occupant comfort while enhancing energy efficiency—a vital approach for creating sustainable spaces that prioritize both environmental and human-centered outcomes.
A fundamental aspect of this study involves a detailed analysis of energy consumption trends in Italy, especially within the residential sector, reflecting the data used in this research. This analysis underscores the importance of load forecasting in understanding energy consumption and its implications for sustainable energy practices. Electricity consumption significantly influences both individual lifestyles and broader economic development. Using data from the International Energy Agency, the study identifies patterns in Italy’s electricity usage, particularly highlighting the residential sector's role in shaping national demand. As energy systems undergo significant transformations, accurately forecasting and assessing these trends is essential for measuring progress toward sustainability goals. By analyzing metrics such as sector-specific consumption and energy types, the research offers a comprehensive view of energy dynamics, supporting efficient load management in alignment with occupant needs.
Building on this foundation, the thesis explores the dynamic relationship between energy management and occupant comfort across clusters of buildings, with a focus on enhancing load forecasting capabilities. A simulation model is developed to predict electricity consumption patterns, serving as the core of a comprehensive forecasting system. This model enables optimized energy usage across residential and commercial buildings, balancing efficient energy management with occupant comfort. Through a detailed analysis of existing methods and techniques, the thesis identifies strengths and limitations, providing a foundation for designing a more accurate and adaptive load forecasting system tailored to real-world energy use variability. This approach contributes not only to energy efficiency but also supports a comfortable indoor environment, aligning energy optimization with occupant-centric goals.
The next phase of this research centers on practical implementation, beginning with data retrieval from the cloud to analyze power demand patterns in real-world settings. A primary challenge was developing a secure and efficient software interface to access and process cloud-based data, which was successfully achieved, enabling seamless data transfer for advanced power demand forecasting. Given the frequent issue of missing data in time series, this research prioritizes data imputation methods to ensure data reliability—essential for data-driven applications. Instead of relying on computationally intensive machine learning methods, this study enhances standard approaches by including temporal features, which enriches imputation accuracy while keeping the methods straightforward. Specifically, two innovative methods—Historical Data Informed Regression Technique (H-DIRT) and Seasonal K-Nearest Neighbors (SKNN)—are introduced as adaptations of established imputation techniques (linear regression and KNN) that incorporate temporal insights to better capture seasonal and time-dependent patterns
Incorporating Seasonal Features in Data Imputation Methods for Power Demand Time Series
No full textThis paper addresses the critical issue of missing data in power demand time series by emphasizing the relevance of imputation-based approaches in data-driven technologies. A comparative analysis of imputation methods is performed, where the reference from the state of the art is selected as K-Nearest Neighbors (KNN) applied in the time domain. Two innovative methods are proposed. The former method is defined as Historical Data Informed Regression Technique (H-DIRT) and is based on incorporating historical data for setting up a multivariate linear regression and then imputing through the estimated relation between the missing power demand measurement and the historical data. When the available historical data are insufficient, the algorithm proceeds by averaging or by a linear interpolation between the first available measurement before and after the missing value. The latter proposed method is defined as Seasonal KNN (SKNN) and it is based on enriching the data set with features related to yearly, seasonal, weekly and daily trends and then proceeding by baseline KNN. Experiments are set up with random and continuous data clipping, even with rather extreme pruning (up 70% of the data). The results in general demonstrate a significant improvement in imputation accuracy compared to the state of the art. The average error metrics (like Mean Absolute Error and Root Mean Square Error) for the SKNN method are in the order of respectively one third and one half those of the baseline KNN, in the cases of random and continuous data clipping. In general, the SKNN method provides more accurate results and better captures the statistical features of the data set to impute. Anyway, if the share of data to impute is not too large, the H-DIRT method provides comparable accuracy at a much lower computational cost. Hence, this study presents an easily implementable and computationally affordable approach for improving, in various contexts, the state of the art in power demand data imputation. It establishes a foundation for future exploration into trends, seasonal factors, and external variables influencing power load parameters
Value of customer loyalty programs for consumers
No full textNowadays, we are coming aware of the fact that we cannot imagine our modern world without a car. Fuel is a product that most of the car drivers need. In the competitive market, gas stations try to attract as many customers as possible. One of these attractions is a customer’s loyalty program. The goal of the thesis is to study the Russian gas station market to understand what customers value in customers loyalty programs of gas stations.
In the theoretical part of the research, the author examines and discusses the customers’ loyalty programs, types of loyalty programs, analyses the key factors what influence customer loyalty, reasons why companies need loyalty programs, how should the effective loyalty program look like and how to create them.
The author proceeds inductively and uses a qualitative research approach. Sеcondary dаta is collected from vаrious reliable sourcеs of literature, including books, artiсles, and Internet sоurces. The secondary data is supрorted with primаry data, which is collected by interviewing fifty persons who are customers of gas stations.
The research findings indicate that customers value mostly points and discounts in the loyalty programs and the quality of products that could be purchased for these points. However, to become a loyal customer, the customer should be attracted by the quality of goods and services of the company. Otherwise, he/she will not come back again
The identification of the person of False Dmitrii I in works by M. I. Kostomarov
No full textСтаттю присвячено спробі видатного українського історика
М. І. Костомарова ідентифікувати таємничу особу Лжедмитрія І –
людини, яка прийшла до влади в Росії у 1604 р. Автор виділяє три
основні версії Миколи Костомарова стосовно визначення особи Лжедмитрія І: про царське походження цієї людини, про те, що він був
російським самозванцем Григорієм Отреп’євим і що то був дійсно
самозванець, але не мав нічого спільного з біглим ченцем Григорієм. На
думку автора, М. І. Костомаров зробив цілком правильний висновок
про те, що Лжедмитрій І був не царським сином, а російським самозванцем, проте – не Григорієм Отреп’євим.This article is dedicated to the attempts of outstanding Ukrainian
historian M. I. Kostomarov to identify the mysterious person of False
Dmitrii I – the man who came to power in Russia in 1605. The author
presents the three main versions as to the real person of False Dmitrii I that
were considered by Mykola Kostomarov – about the tsarist origin of this
man, about his having been Russian impostor Grigorii Otrepiev and about
his having been an impostor who had nothing in common with runaway
monk Grigorii. The author believes that M. I. Kostomarov arrived at a quite
correct conclusion that False Dmitrij had been not a tsar’s son, but an
impostor who was not Grigorii Otrepiev
‘Extractivism as Rebordering: Dmitrii Savochkin’s Mark Sheider, Russo-Ukrainian Mining Literature and the Fragmentation of Post-Soviet Ukraine’
No full textThis essay examines the Russian-language novel Mark Sheider (2009) by the Ukrainian author Dmitrii Savochkin in the context of the classical American and European (Émile Zola, Upton Sinclair, George Orwell), as well as Russo-Ukrainian (Aleksandr Kuprin, Larisa Reisner, Vasilii Grossman, Boris Gorbatov, Fridrikh Gorenshtein) writing about mining. It identifies some topoi common to mining fiction and non-fiction. It also considers the Russo-Ukrainian versions of such topoi, with a special focus on extractivism represented as a form of rebordering. Wolfgang Iser’s concept of fictional representation provides the article with the principal theoretical framework for the analysis
«There is Nothing There». Dmitrii Danilov’s Travel Writing and the Lure of the Russian Provinces
No full textDrawing on Michel de Certeau’s seminal study The Practice of Everyday Life, the author argues that Dmitrii Danilov’s travel writing (Twenty Cities, 2007-2009) reimagines Russia’s symbolic geography by destabilizing the traditional opposition centre – periphery. Rather than depicting the provincial world as either an absurd and horrid world, or as a repository of “true Russianness”, Danilov provides a “decentred” perspective on the provinces that asserts the uniqueness of each city he visits. The novel Description of a City (2012), however, resurrects the more traditional view of the provinces as a world of boredom and cultural lack. To analyse this development the article looks at the central figure of the sluggish traveller-narrator, the employment of “camera-eye narration” and other, mainly linguistic, devices that reaffirm the notion of the provincial city’s “namelessness” as one of its most defining characteristics. Drawing on Michel de Certeau’s seminal study The Practice of Everyday Life, the author argues that Dmitrii Danilov’s travel writing (Twenty Cities, 2007-2009) reimagines Russia’s symbolic geography by destabilizing the traditional opposition centre – periphery. Rather than depicting the provincial world as either an absurd and horrid world, or as a repository of ‘true Russianness’, Danilov provides a ‘decentred’ perspective on the provinces that asserts the uniqueness of each city he visits. The novel Description of a City (2012), however, resurrects the more traditional view of the provinces as a world of boredom and cultural lack. To analyse this development the article looks at the central figure of the sluggish traveller-narrator, the employment of ‘camera-eye narration’ and other, mainly linguistic, devices that reaffirm the notion of the provincial city’s ‘namelessness’ as one of its most defining characteristics.Опираясь на фундаментальное исследование Michel de Certeau "Практика повседневности", автор утверждает, что очерки Дмитрия Данилова ("Двадцать городов", 2007-2009) переосмысливает символическую географию России, дестабилизируя традиционный оппозиционный центр - периферию. Вместо того, чтобы изображать провинциальный мир либо абсурдным и ужасным, либо хранилищем "истинной русскости", Данилов дает "де-централизованный" взгляд на провинцию, утверждающий уникальность каждого города, который он посещает. Однако роман "Описание города" (2012) воскрешает более традиционный взгляд на провинцию как на мир скуки и культурного дефицита. Для анализа этого вектора развития в статье рассматривается центральная фигура вялотекущего путешественника-расказчика, использование "повествования камеры-ока" (camera-eye narration) и других, в основном лингвистических, приёмов, подтверждающих понятие провинциального города "безымянность" как одной из его наиболее определяющих черт
Electronic Governance and Open Society: Challenges in Eurasia: 8th International Conference, EGOSE 2021
No full textGreen Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Information and Communication Technolog
Unraveling the Social-Technical Complexity of Dashboards for Transformation
No full textThe need for standardized and visualized performance monitoring on a wide range of topics has become apparent in recent years. In the public sector, there has been an increase in the number of dashboards to create transparency into the progress. Yet, the design of dashboards encounters many challenges ranging from technical to social. The goal of this research is to unravel the social-technical complexity of dashboards and outline their basic requirements and a process for creating dashboards. In addition to explicit project milestones, these also visualize digital implementation programs at the policy level.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Information and Communication Technolog
Delivery of the malware : Developing the virus scanner for images
No full textSchool of Business and
Administration
Bachelor of Business
Administration, Information
Technology
Abstract of Thesis
Author Dmitrii Maltsev Year 2015
Supervisor Yrjö Koskenniemi
Title of Thesis Delivery of the Malware - Developing the Virus Scanner
for images
No. of pages + app. 42 + 5
The appearing of the vulnerabilities for private data has been always there since the internet was born. The measures of protection started to grow because of this fact. The new and creative ways of malicious software delivery have been
intensified, as well. Today, no one is surprised by standard methods of delivering the virus - banners, spam, suspicious links, downloading files from untrusted resources. Some people even know about images, which contains executable scripts. But it is almost impossible to detect such viruses, even if antivirus is installed. Due to these facts this thesis has the objective to make a
research about modern Information Security, especially about methods of virus delivery. However, main objective is to develop the application which scans the images revealing hidden malicious software.
Action research and case study methodologies were used for this thesis. They were chosen because these methods allow reaching the objectives of the thesis.
As a result, this thesis contains the information concerning the malware and methods of its delivery. The methods of protection are also added to the thesis. Scanning applications for images is implemented.
Most applicable result is an application, which could be useful for users, who need to make sure, that their images do no contain malware. In addition, companies could use this application for selling, and therefore earning money
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