Electricity price classification using extreme learning machines
Forecasting electricity prices has been a widely investigated research issue in the
deregulated power market scenario. High price volatilities, price spikes caused by a number …
deregulated power market scenario. High price volatilities, price spikes caused by a number …
Medium term electricity price forecasting using extreme learning machine
Accurate electricity market price forecasting gives a capability to make better decisions in
electricity market environment when, this market is complicated due to the severe …
electricity market environment when, this market is complicated due to the severe …
Short-term electricity price forecasting and classification in smart grids using optimized multikernel extreme learning machine
Short-term electricity price forecasting in deregulated electricity markets has been studied
extensively in recent years but without significant reduction in price forecasting errors. Also …
extensively in recent years but without significant reduction in price forecasting errors. Also …
A hybrid wavelet-ELM based short term price forecasting for electricity markets
NA Shrivastava, BK Panigrahi - International Journal of Electrical Power & …, 2014 - Elsevier
Accurate electricity price forecasting is a formidable challenge for market participants and
managers owing to high volatility of the electricity prices. Price forecasting is also the most …
managers owing to high volatility of the electricity prices. Price forecasting is also the most …
Wavelet transform and Kernel-based extreme learning machine for electricity price forecasting
Y Zhang, C Li, L Li - Energy Systems, 2018 - Springer
In deregulated electricity markets, sophisticated factors, such as the weather, the season,
high frequencies, the presence of jumps and the relationship between electricity loads and …
high frequencies, the presence of jumps and the relationship between electricity loads and …
Electricity price forecasting with extreme learning machine and bootstrapping
X Chen, ZY Dong, K Meng, Y Xu… - IEEE transactions on …, 2012 - ieeexplore.ieee.org
Artificial neural networks (ANNs) have been widely applied in electricity price forecasts due
to their nonlinear modeling capabilities. However, it is well known that in general, traditional …
to their nonlinear modeling capabilities. However, it is well known that in general, traditional …
Day-ahead forecasting of wholesale electricity pricing using extreme learning machine
JEC Tee, TT Teo, T Logenthiran… - TENCON 2017-2017 …, 2017 - ieeexplore.ieee.org
In a deregulated electricity market where consumers can prepare bidding plans and
purchase electricity directly from supplies, consumers can expect the price to fluctuate based …
purchase electricity directly from supplies, consumers can expect the price to fluctuate based …
Application of extreme learning machine-autoencoder to medium term electricity price forecasting
Electricity market prices are highly volatile, highly frequent, non-linear, and non-stationary
which makes forecasting very complicated. Although accurate forecasting plays a crucial …
which makes forecasting very complicated. Although accurate forecasting plays a crucial …
Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods
Z Yang, L Ce, L Lian - Applied Energy, 2017 - Elsevier
Electricity prices have rather complex features such as high volatility, high frequency,
nonlinearity, mean reversion and non-stationarity that make forecasting very difficult …
nonlinearity, mean reversion and non-stationarity that make forecasting very difficult …
Week ahead electricity price forecasting using artificial bee colony optimized extreme learning machine with wavelet decomposition
T Aruldoss Albert Victoire - Tehnički vjesnik, 2021 - hrcak.srce.hr
Sažetak Electricity price forecasting is one of the more complex processes, due to its non-
linearity and highly varying nature. However, in today's deregulated market and smart grid …
linearity and highly varying nature. However, in today's deregulated market and smart grid …