Short-term electricity price forecasting by employing ensemble empirical mode decomposition and extreme learning machine
Day-ahead electricity price forecasting plays a critical role in balancing energy consumption
and generation, optimizing the decisions of electricity market participants, formulating …
and generation, optimizing the decisions of electricity market participants, formulating …
An ensemble model based on machine learning methods and data preprocessing for short-term electric load forecasting
Y Lin, H Luo, D Wang, H Guo, K Zhu - Energies, 2017 - mdpi.com
The experience with deregulated electricity market has shown the increasingly important
role of short-term electric load forecasting in the energy producing and scheduling …
role of short-term electric load forecasting in the energy producing and scheduling …
Electricity energy price forecasting based on hybrid multi-stage heterogeneous ensemble: Brazilian commercial and residential cases
MHDM Ribeiro, RG da Silva, C Canton… - … Joint Conference on …, 2020 - ieeexplore.ieee.org
The development of accurate models to forecast electricity energy prices is a challenge due
to the number of factors which can affect this commodity. In this paper, a hybrid multi-stage …
to the number of factors which can affect this commodity. In this paper, a hybrid multi-stage …
Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm
In the deregulated competitive electricity market, the price which reflects the relationship
between electricity supply and demand is one of the most important elements, making it …
between electricity supply and demand is one of the most important elements, making it …
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 …
A novel machine learning-based electricity price forecasting model based on optimal model selection strategy
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 optimization methods while ignoring the significance of adaptive data …
Day-ahead electricity price forecasting employing a novel hybrid frame of deep learning methods: A case study in NSW, Australia
YQ Tan, YX Shen, XY Yu, X Lu - Electric Power Systems Research, 2023 - Elsevier
Day-ahead electricity price forecasting plays a vital role in electricity markets under
liberalization and deregulation, which can provide references for participants in bidding …
liberalization and deregulation, which can provide references for participants in bidding …
Electricity price forecasting based on self-adaptive decomposition and heterogeneous ensemble learning
Electricity price forecasting plays a vital role in the financial markets. This paper proposes a
self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term …
self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term …
Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm
L Yan, Z Yan, Z Li, N Ma, R Li, J Qin - Energies, 2023 - mdpi.com
Electricity price forecasting is a crucial aspect of spot trading in the electricity market and
optimal scheduling of microgrids. However, the stochastic and periodic nature of electricity …
optimal scheduling of microgrids. However, the stochastic and periodic nature of electricity …
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 …