Essays about: "Energy price forecasting"
Showing result 1 - 5 of 36 essays containing the words Energy price forecasting.
-
1. Long-term Forecasting Heat Use in Sweden's Residential Sector using Genetic Algorithms and Neural Network
University essay from Högskolan i Halmstad/Akademin för företagande, innovation och hållbarhetAbstract : 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). READ MORE
-
2. Forecasting Volatility of Electricity Intraday Log Returns with Generalized Autoregressive Score Models
University essay from Göteborgs universitet/Graduate SchoolAbstract : 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. READ MORE
-
3. Forecasting Swedish FCR-D Prices using Penalized Multivariate Time Series Techniques
University essay from Lunds universitet/Nationalekonomiska institutionen; Lunds universitet/Statistiska institutionenAbstract : 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. READ MORE
-
4. A Mixed Time-Series & Machine Learning Approach for Price Forecasting in the Swedish Ancillary Market
University essay from Lunds universitet/Nationalekonomiska institutionen; Lunds universitet/Statistiska institutionenAbstract : 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. READ MORE
-
5. In the Core of the Storm: Revisiting Inflation Hedging Properties Within and Across Asset Classes
University essay from Handelshögskolan i Stockholm/Institutionen för finansiell ekonomiAbstract : 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. READ MORE
