Optimized data-driven models for short-term electricity price forecasting based on signal decomposition and clustering techniques

AI Arvanitidis, D Bargiotas, D Kontogiannis, A Fevgas… - Energies, 2022 - mdpi.com
In recent decades, the traditional monopolistic energy exchange market has been replaced
by deregulated, competitive marketplaces in which electricity may be purchased and sold at …

Dynamic ensemble deep echo state network for significant wave height forecasting

R Gao, R Li, M Hu, PN Suganthan, KF Yuen - Applied Energy, 2023 - Elsevier
Forecasts of the wave heights can assist in the data-driven control of wave energy systems.
However, the dynamic properties and extreme fluctuations of the historical observations …

Multi-step carbon price forecasting using a hybrid model based on multivariate decomposition strategy and deep learning algorithms

K Zhang, X Yang, T Wang, J Thé, Z Tan, H Yu - Journal of Cleaner …, 2023 - Elsevier
Accurate prediction of carbon price effectively ensures the stability of the carbon trading
market and reduces carbon emissions. However, making accurate prediction is challenging …

[HTML][HTML] Online dynamic ensemble deep random vector functional link neural network for forecasting

R Gao, R Li, M Hu, PN Suganthan, KF Yuen - Neural Networks, 2023 - Elsevier
This paper proposes a three-stage online deep learning model for time series based on the
ensemble deep random vector functional link (edRVFL). The edRVFL stacks multiple …

A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms

K Zhang, H Cao, J Thé, H Yu - Applied Energy, 2022 - Elsevier
Accurate and reliable coal price prediction is of great significance to enhance the stability of
the coal market. Numerous methods have been developed to improve the prediction …

Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm

P Jiang, Z Liu, J Wang, L Zhang - Resources Policy, 2021 - Elsevier
Effective crude oil and natural gas futures price forecasting is a crucial endeavor for financial
energy markets and is also a challenging work due to the nonlinear and fluctuant …

A novel multiscale forecasting model for crude oil price time series

R Li, Y Hu, J Heng, X Chen - Technological Forecasting and Social …, 2021 - Elsevier
Forecasting crude oil prices is an essential research field in the international bulk
commodities market. However, price movements present more complex nonlinear behavior …

Multi-step-ahead stock price prediction using recurrent fuzzy neural network and variational mode decomposition

H Nasiri, MM Ebadzadeh - Applied Soft Computing, 2023 - Elsevier
Financial time series prediction has attracted considerable interest from scholars, and
several approaches have been developed. Among them, decomposition-based methods …

Random vector functional link neural network based ensemble deep learning for short-term load forecasting

R Gao, L Du, PN Suganthan, Q Zhou… - Expert Systems with …, 2022 - Elsevier
Electric load forecasting is essential for the planning and maintenance of power systems.
However, its un-stationary and non-linear properties impose significant difficulties in …

Transformer-based forecasting for intraday trading in the Shanghai crude oil market: Analyzing open-high-low-close prices

W Huang, T Gao, Y Hao, X Wang - Energy Economics, 2023 - Elsevier
The Shanghai crude oil futures market exudes distinct speculative attributes, underscoring
the pivotal significance of precise price forecasts. Accurate forecasting of Shanghai crude oil …