Electricity Day-Ahead Market Price Forecasting by Using Artificial Neural Networks: An Application for Turkey
Sign Up to like & getrecommendations! Published in 2020 at "Arabian Journal for Science and Engineering"
DOI: 10.1007/s13369-020-04349-1
Abstract: The reference price is the most important signal for market participant to have position of selling or buying in the trade of electricity. The electricity price has a dynamic structure and is directly and/or indirectly… read more here.
Keywords: market; electricity; price; price forecasting ... See more keywords
On the impact of outlier filtering on the electricity price forecasting accuracy
Sign Up to like & getrecommendations! Published in 2019 at "Applied Energy"
DOI: 10.1016/j.apenergy.2018.11.076
Abstract: Abstract Increasing the accuracy of short-term electricity price forecasting allows day-ahead power market participants to obtain a positive economic effect by bidding close to the equilibrium price. However the electricity price time-series is generally infested… read more here.
Keywords: forecasting accuracy; price forecasting; electricity price; price ... See more keywords
Ensemble of relevance vector machines and boosted trees for electricity price forecasting
Sign Up to like & getrecommendations! Published in 2019 at "Applied Energy"
DOI: 10.1016/j.apenergy.2019.05.062
Abstract: Abstract Real-time prediction of electricity pricing signals is essential for scheduling load demand in price-directed grids. In a deregulated electricity market, this helps substantially increase the gains of utility companies and minimize the electricity cost… read more here.
Keywords: relevance vector; price forecasting; electricity; vector ... See more keywords
Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks
Sign Up to like & getrecommendations! Published in 2018 at "Energy Economics"
DOI: 10.1016/j.eneco.2017.12.016
Abstract: We conduct an extensive empirical study on short-term electricity price forecasting (EPF) to address the long-standing question if the optimal model structure for EPF is univariate or multivariate. We provide evidence that despite a minor… read more here.
Keywords: high dimensional; multivariate modeling; electricity price; multivariate ... See more keywords
On the importance of the long-term seasonal component in day-ahead electricity price forecasting
Sign Up to like & getrecommendations! Published in 2019 at "Energy Economics"
DOI: 10.1016/j.eneco.2018.02.007
Abstract: A recent electricity price forecasting study has shown that the Seasonal Component AutoRegressive (SCAR) modeling framework, which consists of decomposing a series of spot prices into a trend-seasonal and a stochastic component, modeling them independently… read more here.
Keywords: seasonal component; price forecasting; predictive distributions; scar ... See more keywords
Monthly crude oil spot price forecasting using variational mode decomposition
Sign Up to like & getrecommendations! Published in 2019 at "Energy Economics"
DOI: 10.1016/j.eneco.2019.07.009
Abstract: Crude oil is one of the most important trade commodities in the world and its price fluctuation has significant effects on global economic activities. In this paper, we proposed hybrid models for monthly crude oil… read more here.
Keywords: crude oil; oil price; price forecasting; oil ... See more keywords
Application of bagging in day-ahead electricity price forecasting and factor augmentation
Sign Up to like & getrecommendations! Published in 2021 at "Energy Economics"
DOI: 10.1016/j.eneco.2021.105573
Abstract: 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. Particularly,… read more here.
Keywords: price forecasting; electricity price; day ahead; ahead electricity ... See more keywords
A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting
Sign Up to like & getrecommendations! Published in 2021 at "Energy"
DOI: 10.1016/j.energy.2021.120963
Abstract: Abstract Oil price plays a vital role in a country's economy. Oil price forecasting helps in making better economic planning and decisions. The fluctuation in the oil price occurs due to several factors that make… read more here.
Keywords: price forecasting; oil price; score; oil ... See more keywords
A novel machine learning-based electricity price forecasting model based on optimal model selection strategy
Sign Up to like & getrecommendations! Published in 2022 at "Energy"
DOI: 10.1016/j.energy.2021.121989
Abstract: Abstract Current electricity price forecasting models rely on only simple hybridizations of data preprocessing and optimization methods while ignoring the significance of adaptive data preprocessing and effective optimization and selection strategies to obtain optimal models… read more here.
Keywords: electricity price; selection; electricity; model ... See more keywords
A feature extraction- and ranking-based framework for electricity spot price forecasting using a hybrid deep neural network
Sign Up to like & getrecommendations! Published in 2021 at "Electric Power Systems Research"
DOI: 10.1016/j.epsr.2021.107453
Abstract: Abstract In deregulated electricity markets, reliable electricity market price forecasting is the foundation for making the bidding strategy, operating dispatch control, and hedging volatility risk. However, electricity prices are high-volatile, nonstationary, multi-seasonal, making it difficult… read more here.
Keywords: price forecasting; feature; model; extraction ... See more keywords
Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?
Sign Up to like & getrecommendations! Published in 2018 at "International Journal of Forecasting"
DOI: 10.1016/j.ijforecast.2019.07.002
Abstract: Abstract Recent electricity price forecasting studies have shown that decomposing a series of spot prices into a long-term trend-seasonal and a stochastic component, modeling them independently and then combining their forecasts, can yield more accurate… read more here.
Keywords: price forecasting; electricity price; point; narx networks ... See more keywords