Electricity price forecasting in deregulated markets: A review and evaluation
By Sanjeev Kumar Aggarwal, Lalit Mohan Saini, Ashwani Kumar. 2009
537 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/213851b255d9c3ef6ed1279eb39930dcde312b79
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
- Florian Ziel
- Rafal Weron
- R. Steinert
- F. Serinaldi
- Jakub Nowotarski
- Grzegorz Marcjasz
- Ervin Ceperic
- Daniel Hsu
Citations in the corpus
- Efficient modeling and forecasting of electricity spot prices (2014, unknown): “As a detailed overview of all those approaches would be outside the scope of this paper, the interested reader is referred to the works of Weron (2006) or Aggarwal et al. (2009) who provide a comprehensive overview of the recent literature.”
- Distributional modeling and short-term forecasting of electricity prices by Generalized Additive Models for Location, Scale and Shape (2011, background): “More recently, Aggarwal et al. (2009) classified price forecasting methods in three wide groups, namely, game theory, simulation, and time series models.”
- Time Series Compression Based on Adaptive Piecewise Recurrent Autoencoder (2017, background): “The high-resolution data would be extremely beneficial for many analytical applications such as data visualization [2], short-term forecasts of energy generation/load [3] or energy prices [4].”
- Probabilistic mid- and long-term electricity price forecasting (2017, background): “Aggarwal et al. (2009) or Weron (2014) for a literature review on electricity price forecasting).”
- Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, background): “Aggarwal, Saini, and Kumar (2009a) review 47 ‘time series’ and ‘neural network’ papers published between 1997 and 2006 in terms of the model type and architecture, forecast horizon(s), model input and output variables, preprocessing and datasets used.”
- A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines (2013, background): “The overviews of the load forecasting methods can be found in [2], [3], and [10].”