Sökning: "Load Forecast"
Visar resultat 1 - 5 av 60 uppsatser innehållade orden Load Forecast.
1. Forecasting Volatility of Electricity Intraday Log Returns with Generalized Autoregressive Score Models
Master-uppsats, Göteborgs universitet/Graduate SchoolSammanfattning : We forecast volatility of electricity intraday log returns with Generalized Autoregressive Score (GAS) models. We extend our GAS models with variables representing the difference between the public’s expectation of weather and energy load and the actual outcome using a restricted ARMA(4,4) model. LÄS MER
2. Påverkan av elbilar på Sveriges transmissionsnät 2030
M1-uppsats, Linköpings universitet/FordonssystemSammanfattning : Antal nyregistrerade laddbara personbilarbilar ökar märkbart för varje år.Enligt en prognos kommer cirka 2.5 miljoner laddbara bilar vara i trafik iSverige år 2030. Detta motsvarar hälften av personbilsflottan idag. LÄS MER
3. Dimensioning Microservices on Kubernetes Platforms Using Machine Learning Techniques
Master-uppsats, Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)Sammanfattning : In recent years, cloud computing and containerization have become increasingly popular for various applications. However, optimizing resource usage and minimizing costs while providing reliable and efficient service to users can be a challenge. One such challenge is scaling containers according to the current system load. LÄS MER
4. Utvärdering av lastprognoser : En undersökning om lastprognoserna skapade till flexibilitetsmarknaden CoordiNet i Uppsala
Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/ElektricitetsläraSammanfattning : The aim of the thesis was to evaluate the load forecasts used in the flexibility market CoordiNet, more specific the load forecasted used for Uppsala during the winters 2020-2021 and 2021-2022. The load forecasts are based on machine learning and to answer the aim of the thesis the following three research questions have been studied: what factors affect the load forecasting errors, which factors are important to increase the reliability of the load forecasts and how can Vattenfall Eldistribution evaluate load forecasts in the future. LÄS MER
5. Design and Implementation of Cellular Network Hotspot Forecast Using Graph Convolutional Neural Networks
Master-uppsats, Uppsala universitet/Institutionen för informationsteknologiSammanfattning : This project proposes a type of (Recurrent) Graph Neural Network (RGNN) to predict future hotspots in cellular network data. Current state-of-the-art algorithms process each antenna (celldata )’s in isolation, ignoring the performance of nearby cells and cell locations. LÄS MER