Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
An empirical evidence for generalized shrinkage methods: application of bagging in day-ahead electricity price forecasting and factor augmentation .
Date
2020
Author
Özen, Kadir
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
258
views
0
downloads
Cite This
Fundamental dynamics behind electricity prices are multi-dimensional and elaborate. A popular approach to forecasting electricity price is to utilize large number of predictors. In this study, using the day-ahead electricity price data from commonly studied markets of five major series and GEFCom2014 data, a variant of shrinkage method, Bootstrap Aggregation (bagging) is proposed to incorporate information from available predictors. Bagging manifests itself as a computationally simpler alternative to commonly used Least Absolute Shrinkage and Selection Operator (lasso) in multivariate EPF context and even shows superior forecasting ability in some markets. Moreover, considering the significant dependence of intra-day electricity prices, we also propose factor augmentation to exploit this dependence. The inclusion of latent factors, selected via Bayesian Information Criterion, improves ability to forecast in multivariate modeling framework and in some cases even outperform sophisticated shrinkage methods as measured by the Diebold-Mariano test.
Subject Keywords
Econometrics.
,
Shrinkage methods
,
Electricity price forecasting
,
Factor models
URI
http://etd.lib.metu.edu.tr/upload/12625400/index.pdf
https://hdl.handle.net/11511/45626
Collections
Graduate School of Social Sciences, Thesis
Suggestions
OpenMETU
Core
Hydro-Optimization-Based Medium-Term Price Forecasting Considering Demand and Supply Uncertainty
İLSEVEN, Engin; Göl, Murat (2018-07-01)
This paper proposes an electricity market model of Turkish electricity market for monthly and yearly electricity price forecasting in medium-term by means of supply and demand dynamics formed via a theoretical approach. The electricity market model created within this scope consists of three main components related to electricity demand, supply, and price segments along with hydro optimization submodel, which takes into account the nonlinear relation between supply and price. Electricity price is determined...
Application of bagging in day-ahead electricity price forecasting and factor augmentation
Özen, Kadir; Yıldırım Kasap, Dilem (2021-11-01)
The electricity price forecasting (EPF) is a challenging task not only because of the uncommon characteristics of electricity but also because of the existence of many potential predictors with changing predictive abilities over time. In such an environment, how to account for all available factors and extract as much information as possible is the key to the production of accurate forecasts. To address this long-standing issue in a way that balances complexity and forecasting accuracy while facilitating th...
A novel methodology for medıum and long-term electricity market modeling
İlseven, Engin; Göl, Murat; Department of Electrical and Electronics Engineering (2020-11-15)
In the electricity market, there is a considerable degree of uncertainty in electricity demand, supply, and price due to the uncertainty in parameters such as economic growth, weather conditions, fuel prices, and timing of new investments, etc. These factors in return affect the predictability of the electricity market. This thesis aims to increase the predictability and observability of the electricity market by means of a suitable and validated electricity market modeling methodology designed for medium a...
Data-driven modeling using deep neural networks for power systems demand and locational marginal price forecasting
Jimu, Honest; Fahrioğlu, Murat; Electrical and Electronics Engineering (2022-9)
Forecasting electricity demand and locational marginal prices (LMPs) have become critical components for power system security and management. Electricity Demand Forecasting (EDF) aids the utility in maximizing the use of power-generation plants and scheduling them for both reliability and cost-effectiveness. In this thesis, a novel Deep Neural Network Long Short-Term Memory (DNN-LSTM) forecasting model is suggested to improve accuracy and robustness for predicting hourly day ahead power system load and LMP...
A soft computing approach to projecting locational marginal price
Nwulu, Nnamdi I.; Fahrioglu, Murat (2013-05-01)
The increased deregulation of electricity markets in most nations of the world in recent years has made it imperative that electricity utilities design accurate and efficient mechanisms for determining locational marginal price (LMP) in power systems. This paper presents a comparison of two soft computing-based schemes: Artificial neural networks and support vector machines for the projection of LMP. Our system has useful power system parameters as inputs and the LMP as output. Experimental results obtained...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
K. Özen, “An empirical evidence for generalized shrinkage methods: application of bagging in day-ahead electricity price forecasting and factor augmentation .,” Thesis (M.S.) -- Graduate School of Social Sciences. Economics., Middle East Technical University, 2020.