Behavioral Considerations for Effective Time-Varying Electricity Prices

2016 ◽  
Author(s):  
Ian Schneider ◽  
Cass R. Sunstein
Keyword(s):  
Time Varying ◽  
Electricity Prices ◽  
Effective Time

Time-Varying Retail Electricity Prices

2013 ◽  
pp. 317-354 ◽  
Author(s):  
Severin Borenstein
Keyword(s):  
Time Varying ◽  
Electricity Prices

Household Response to Time-Varying Electricity Prices

2017 ◽  
Vol 9 (1) ◽  
pp. 337-359 ◽  
Author(s):  
Matthew Harding ◽  
Steven Sexton
Keyword(s):  
Time Varying ◽  
Electricity Prices

Forecasting electricity prices: The impact of fundamentals and time-varying coefficients

2008 ◽  
Vol 24 (4) ◽  
pp. 764-785 ◽  
Author(s):  
Nektaria V. Karakatsani ◽  
Derek W. Bunn
Keyword(s):  
Time Varying ◽  
Electricity Prices ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
The Impact

Texture-based feature tracking for effective time-varying data visualization

2007 ◽  
Vol 13 (6) ◽  
pp. 1472-1479 ◽  
Author(s):  
Jesus Caban ◽  
Alark Joshi ◽  
Penny Rheingans
Keyword(s):  
Data Visualization ◽  
Feature Tracking ◽  
Time Varying ◽  
Effective Time

scholarly journals Optimal control of storage under time varying electricity prices

2017 ◽  
Author(s):  
Md Umar Hashmi ◽  
Arpan Mukhopadhyay ◽  
Ana Busic ◽  
Jocelyne Elias
Keyword(s):  
Optimal Control ◽  
Time Varying ◽  
Electricity Prices

scholarly journals The Impact of Forecasting Jumps on Forecasting Electricity Prices

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 336
Author(s):  
Maciej Kostrzewski ◽  
Jadwiga Kostrzewska
Keyword(s):  
Weather Conditions ◽  
Electricity Price ◽  
Time Varying ◽  
Electricity Prices ◽  
Jump Detection ◽  
Explanatory Variables ◽  
Detection Techniques ◽  
Jump Diffusion Model ◽  
Upward Jump ◽  
The Impact

The paper is devoted to forecasting hourly day-ahead electricity prices from the perspective of the existence of jumps. We compare the results of different jump detection techniques and identify common features of electricity price jumps. We apply the jump-diffusion model with a double exponential distribution of jump sizes and explanatory variables. In order to improve the accuracy of electricity price forecasts, we take into account the time-varying intensity of price jump occurrences. We forecast moments of jump occurrences depending on several factors, including seasonality and weather conditions, by means of the generalised ordered logit model. The study is conducted on the basis of data from the Nord Pool power market. The empirical results indicate that the model with the time-varying intensity of jumps and a mechanism of jump prediction is useful in forecasting electricity prices for peak hours, i.e., including the probabilities of downward, no or upward jump occurrences into the model improves the forecasts of electricity prices.

scholarly journals Wavelet estimation for factor models with time-varying loadings

2021 ◽  
pp. 2150033
Author(s):  
Duván Humberto Cataño ◽  
C. Vladimir Rodríguez-Caballero ◽  
Daniel Peña ◽  
Chang Chiann
Keyword(s):  
Factor Model ◽  
Common Factors ◽  
Estimation Procedure ◽  
Generalized Least Squares ◽  
High Dimensional ◽  
Second Step ◽  
Time Varying ◽  
Finite Sample ◽  
Electricity Prices ◽  
Two Stages

We introduce a high-dimensional factor model with time-varying loadings. We cover both stationary and nonstationary factors to increase the possibilities of applications. We propose an estimation procedure based on two stages. First, we estimate common factors by principal components. In the second step, considering the estimated factors as observed, the time-varying loadings are estimated by an iterative generalized least squares procedure using wavelet functions. We investigate the finite sample features by some Monte Carlo simulations. Finally, we apply the model to study the Nord Pool power market’s electricity prices and loads.

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