Electricity price forecasting: A review of the state-of-the-art with a look into the future

By Rafal Weron. 2014

1,558 citations recorded by the source corpus.

A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims at explaining the complexity of available solutions, their strengths and weaknesses, and the opportunities and treats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures and (iii) statistical testing of the significance of the outperformance of one model by another.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1c858f62945df1a0c20c3dee2f12e77bdf222812

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Electrical and Electronic Engineering

Citations in the corpus

On the importance of the long-term seasonal component in day-ahead electricity price forecasting (2016, methodology): “To find the optimal value of λ we use a similar grid as in Weron and Zator (2015), the only difference is that in this study the price series are in hourly (not daily) resolution and the values of λ have to be larger.”
A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting (2020, neutral): “While ML algorithms have been successful (Weron, 2014) in some applications like energy forecasting (Dimoulkas, Mazidi, & Herre, 2019), where the series being extrapolated are often numerous, long, and accompanied by explanatory variables, the performance of ML algorithms in more typical time…”
Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark (2020, methodology): “In this context, several studies advocate for the use of scaled errors [5,128,129], where a scaled error is simply the MAE scaled by the in-sample MAE of a naive forecast.”
Tackling Climate Change with Machine Learning (2019, background): “nd farms to increase these farms’ proﬁts, for instance by storing their electricity when prices are low and then selling it when prices are high; prior work has used ML to forecast electricity prices [101, 102] or reinforcement learning to control batteries based on current and historical prices [10”
Forecasting: theory and practice (2020, background): “Electricity price forecasting117 Forecasting electricity prices has various challenges that are highlighted in the detailed review paper by Weron (2014).”
Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging (2016, methodology): “We extend the Quantile Regression Averaging (QRA) approach of Nowotarski and Weron (2014a) and use principal component analysis to automate the selection process from among a large set of individual forecasting models available for averaging.”
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † (2018, methodology): “Commonly used price forecasting techniques include multi-agent, reduced-form, statistical and computational intelligence as reviewed in [13].”