[HTML][HTML] Recent development in electricity price forecasting based on computational intelligence techniques in deregulated power market

A Pourdaryaei, M Mohammadi, M Karimi, H Mokhlis… - Energies, 2021 - mdpi.com
The development of artificial intelligence (AI) based techniques for electricity price
forecasting (EPF) provides essential information to electricity market participants and …

[HTML][HTML] A new framework for electricity price forecasting via multi-head self-attention and CNN-based techniques in the competitive electricity market

A Pourdaryaei, M Mohammadi, H Mubarak… - Expert Systems with …, 2024 - Elsevier
Due to recent technical improvements, the smart grid has become a feasible platform for
electricity market participants to successfully regulate their bidding process based on …

A hybrid day-ahead electricity price forecasting framework based on time series

X **ong, G Qing - Energy, 2023 - Elsevier
Electricity price forecasting (EPF) plays an indispensable role in the decision-making
processes of electricity market participants. However, the complexity of electricity markets …

A hybrid electricity price forecasting model with Bayesian optimization for German energy exchange

H Cheng, X Ding, W Zhou, R Ding - … Journal of Electrical Power & Energy …, 2019 - Elsevier
Electricity price forecasting affects the operation of the entire electricity market and it is
extremely important to every market participant. In this paper, a novel hybrid method, with …

Short-term wind speed forecasting based on improved ant colony algorithm for LSSVM

Y Li, P Yang, H Wang - Cluster Computing, 2019 - Springer
In this paper, a least squares support vector machine (LSSVM) model with parameter
optimization is proposed for solving the problem that the forecast accuracy of neural network …

[HTML][HTML] Analysis and forecasting of the carbon price in China's regional carbon markets based on fast ensemble empirical mode decomposition, phase space …

W Sun, M Duan - Energies, 2019 - mdpi.com
With the development of the carbon market in China, research on the carbon price has
received more and more attention in related fields. However, due to its nonlinearity and …

Factor analysis and carbon price prediction based on empirical mode decomposition and least squares support vector machine optimized by improved particle swarm …

W Sun, YW Wang - Carbon Management, 2020 - Taylor & Francis
With the development of China's carbon market, there has been a growing interest in
research on carbon prices. Using analysis and prediction, the mechanism of the carbon …

Fuel price prediction using RNN

MC Lahari, DH Ravi, R Bharathi - … International Conference on …, 2018 - ieeexplore.ieee.org
The fuel market has its impact directly or indirectly on the income distribution of nations
affecting stock market, cost of living, education, essential commodities and many more …

[PDF][PDF] Optimal Scheduling of a Residential Energy Prosumer Incorporating Renewable Energy Sources and Energy Storage Systems in a Day-ahead Energy Market

H Huang, W Liao, H Parvaneh - Distrib. Gener. Altern. Energy J, 2021 - scholar.archive.org
Due to the world rapid population growth, the need for energy is accelerated especially in
the residential sector. One of the most efficient ways of responding to energy demand is the …

Electricity price prediction with support vector machine and bacterial foraging optimization algorithm for day-ahead model

WARI Azmira, A Ahmad, IZ Abidin… - 2020 IEEE Student …, 2020 - ieeexplore.ieee.org
Predicting the price of electricity is an important aspect in the operation and planning of
power systems. However, predicting the price of electricity is a relatively challenging task as …