Modeling and forecasting the electricity clearing price: A novel BELM based pattern classification framework and a comparative analytic study on multi-layer BELM …

Z Shao, Q Zheng, S Yang, F Gao, M Cheng, Q Zhang… - Energy Economics, 2020 - Elsevier
With the deregulation of power market and the increasing penetration of renewable energy,
the core role of demand side management (DSM) has become even more prominent. In this …

A feature extraction-and ranking-based framework for electricity spot price forecasting using a hybrid deep neural network

Z Shao, Q Zheng, C Liu, S Gao, G Wang… - Electric Power Systems …, 2021 - Elsevier
In deregulated electricity markets, reliable electricity market price forecasting is the
foundation for making the bidding strategy, operating dispatch control, and hedging volatility …

Short-term electricity price forecasting based on similarity day screening, two-layer decomposition technique and Bi-LSTM neural network

K Wang, M Yu, D Niu, Y Liang, S Peng, X Xu - Applied Soft Computing, 2023 - Elsevier
Electricity price forecasting (EPF) has been challenged by the widespread grid integration of
renewable energy (RE), so it is critical to develop a highly accurate and reliable EPF model …

Short-term electricity price forecasting by employing ensemble empirical mode decomposition and extreme learning machine

S Khan, S Aslam, I Mustafa, S Aslam - Forecasting, 2021 - mdpi.com
Day-ahead electricity price forecasting plays a critical role in balancing energy consumption
and generation, optimizing the decisions of electricity market participants, formulating …

Short-term electricity price forecasting and classification in smart grids using optimized multikernel extreme learning machine

R Bisoi, PK Dash, PP Das - Neural Computing and Applications, 2020 - Springer
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 …

Electricity price classification using extreme learning machines

NA Shrivastava, BK Panigrahi, MH Lim - Neural Computing and …, 2016 - Springer
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 …

A new electricity price prediction strategy using mutual information-based SVM-RFE classification

Z Shao, SL Yang, F Gao, KL Zhou, P Lin - Renewable and Sustainable …, 2017 - Elsevier
Owing to the central role in electricity market operation, researchers have long sought to
investigate the price responsiveness of both electricity supply and consumption sides. From …

[HTML][HTML] Electricity market price forecasting using ELM and Bootstrap analysis: A case study of the German and Finnish Day-Ahead markets

S Loizidis, A Kyprianou, GE Georghiou - Applied Energy, 2024 - Elsevier
Electricity market liberalization and the absence of cost-efficient energy storage
technologies have led to the transformation of state-owned electricity companies into …

A novel machine learning-based electricity price forecasting model based on optimal model selection strategy

W Yang, S Sun, Y Hao, S Wang - Energy, 2022 - Elsevier
Current electricity price forecasting models rely on only simple hybridizations of data
preprocessing and optimization methods while ignoring the significance of adaptive data …

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 …