Sökning: "Heating technology"

Visar resultat 1 - 5 av 2499 uppsatser innehållade orden Heating technology.

1. 1. Prestandajämförelse mellan krypterade och okrypterade tidsseriedatabaser med IoT-baserad temperatur- och geopositionsdata

   M1-uppsats, KTH/Hälsoinformatik och logistik

   Författare :Sinem Uzunel; Joanna Xu; [2024]
   Nyckelord :AWS Timestream; InfluxDB Cloud; Performance Testing; Time Series; Time Series databases; Encryption; Database Query; Internet of Things IoT ; Performance Analysis; AWS Timestream; InfluxDB Cloud; Prestandatest; Tidsserier; Tidsseriedatabas; Kryptering; Databasfråga; Internet of Things IoT ; Prestandaanalys;

   Sammanfattning : Internet of Things (IoT) är en växande teknologi som spelar en allt större roll i samhället. Den innefattar ett nätverk av internetanslutna enheter som samlar in och utbyter data. Samtidigt som IoT växer uppstår utmaningar kring hantering av stora datamängder och säkerhetsaspekter. LÄS MER

3. 2. The Role of Technology Shifts in Urban Decarbonization Modelling : Scenario creation and implementation

   Master-uppsats, KTH/Energisystem

   Författare :Batiste Fourniols; [2024]
   Nyckelord :Urban area; Decarbonization policies; Scenario modeling; District heating; Gas distribution network; Geospatial resolution; Stadsområde; utfasning av fossila bränslen; scenariomodellering; fjärrvärme; gasdistributionsnät; geospatial upplösning;

   Sammanfattning : This work includes modelling of decarbonization scenarios at the scale of an urban area, providing policy insights and a methodology focusing on introducing district heating and maintaining the existing gas distribution network in a case study. With a focus on reducing gas consumption in the residential and tertiary sectors, the research integrates scenario developments giving a methodology to develop district heating, requiring a careful balance in selecting the optimal scale for city-wide analysis. LÄS MER

4. 3. Deep reinforcement learning for automated building climate control

   Master-uppsats, Linköpings universitet/Institutionen för datavetenskap

   Författare :Erik Snällfot; Martin Hörnberg; [2024]
   Nyckelord :Machine Learning; Reinforcement Learning; Deep Learning; Deep Reinforcement Learning; Building Control; Control System;

   Sammanfattning : The building sector is the single largest contributor to greenhouse gas emissions, making it a natural focal point for reducing energy consumption. More efficient use of energy is also becoming increasingly important for property managers as global energy prices are skyrocketing. LÄS MER

5. 4. Improvement of anautomatic networkdrawing algorithm in thecontext of utility networks

   Master-uppsats, KTH/Skolan för industriell teknik och management (ITM)

   Författare :Hyacinthe Ménard; [2024]
   Nyckelord :District Heating Network; Mathematical Optimization; Python; Fjärrvärme; Optimeringslära; Python;

   Sammanfattning : The European Union’s ambitious climate targets necessitate substantial reductions in greenhouse gas emissions, particularly within the heating and cooling sector, which accounts for a significant portion of energy consumption. District Heating and Cooling (DHC) systems emerge as a key solution for decarbonizing this sector by enabling high efficiency heat production and the integration of renewable and carbon-neutral energy sources. LÄS MER

7. 5. Long-term Forecasting Heat Use in Sweden's Residential Sector using Genetic Algorithms and Neural Network

   Master-uppsats, Högskolan i Halmstad/Akademin för företagande, innovation och hållbarhet

   Författare :Alireza Momtaz; Mohammad Befkin; [2024]
   Nyckelord :Genetic Algorithm; Neural Network; Forecasting; Heat Use;

   Sammanfattning : In this study, the parameters of population, gross domestic product (GDP), heat price, U-value, and temperature have been used to predict heat consumption for Sweden till 2050. It should be noted that the heat consumption has been considered for multi-family houses. Most multi-family houses (MFH) get their primary heat from district heating (DH). LÄS MER