Sökning: "Energy price forecasting"

Visar resultat 1 - 5 av 38 uppsatser innehållade orden Energy price forecasting.

1. 1. 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

3. 2. Forecasting Volatility of Electricity Intraday Log Returns with Generalized Autoregressive Score Models

   Master-uppsats, Göteborgs universitet/Graduate School

   Författare :Gustav Veres; Philip Ahlfridh; [2023-06-29]
   Nyckelord :;

   Sammanfattning : 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

4. 3. Forecasting Swedish FCR-D Prices using Penalized Multivariate Time Series Techniques

   Magister-uppsats, Lunds universitet/Nationalekonomiska institutionen; Lunds universitet/Statistiska institutionen

   Författare :Franz Lennart Wunderlich; Sebastian Brugger; [2023]
   Nyckelord :Swedish Energy Market; Multivariate Time-Series; Lasso; Forecasting; Noise Filtering; Business and Economics;

   Sammanfattning : The Swedish energy market is becoming more and more sustainable, with an increasing volume and number of diversified energy sources being continuously added to the mix. To stabilize the grid frequency, auctions are held to offer energy providers incentives to produce or consume energy on short notice. LÄS MER

5. 4. A Mixed Time-Series & Machine Learning Approach for Price Forecasting in the Swedish Ancillary Market

   Magister-uppsats, Lunds universitet/Nationalekonomiska institutionen; Lunds universitet/Statistiska institutionen

   Författare :Daniel Arredondo; [2023]
   Nyckelord :Hybrid model; Volatility model; Machine learning model; Price spike prediction; Energy trading; Time series forecasting; Ancillary service market; Business and Economics;

   Sammanfattning : This study aims to forecast the Swedish FCR-D Down A2 market prices through a hybrid model combining a volatility model and a machine learning approach, and compares its performance with a standalone machine learning model. We further examine the impact of different lag orders (1-Hr vs. 24-Hr) on volatility estimates and forecast performance. LÄS MER

7. 5. In the Core of the Storm: Revisiting Inflation Hedging Properties Within and Across Asset Classes

   D-uppsats, Handelshögskolan i Stockholm/Institutionen för finansiell ekonomi

   Författare :Jakub Kacperczyk; Moritz Kornherr; [2023]
   Nyckelord :Inflation hedging; Inflation risk premium; Inflation forecasting; Bond-Stock Correlation;

   Sammanfattning : The recent surge in inflation has reignited discussions on hedging inflation risks, forming the focal point of this study. In our paper we consider conventional asset classes from 1968 to 2023 as well as alternative assets from 2020 to 2023 and find that no asset class provides a statistically significant hedge against core inflation shocks, while commodities and currencies can hedge headline and energy inflation risk. LÄS MER