Hybridization of hybrid structures for time series forecasting: A review
Z Hajirahimi, M Khashei - Artificial Intelligence Review, 2023 - Springer
Achieving the desired accuracy in time series forecasting has become a binding domain,
and developing a forecasting framework with a high degree of accuracy is one of the most …
and developing a forecasting framework with a high degree of accuracy is one of the most …
Recent advances of bat-inspired algorithm, its versions and applications
Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in
many problem domains. The ecosystem of bat animals inspires the main idea of BA. This …
many problem domains. The ecosystem of bat animals inspires the main idea of BA. This …
Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture
Accurate building energy consumption forecasting is crucial for developing efficient building
energy management systems, improving energy efficiency, and local building energy …
energy management systems, improving energy efficiency, and local building energy …
A deep model for short-term load forecasting applying a stacked autoencoder based on LSTM supported by a multi-stage attention mechanism
Z Fazlipour, E Mashhour, M Joorabian - Applied Energy, 2022 - Elsevier
This paper presents an innovative univariate Deep LSTM-based Stacked Autoencoder
(DLSTM-SAE) model for short-term load forecasting, equipped with a Multi-Stage Attention …
(DLSTM-SAE) model for short-term load forecasting, equipped with a Multi-Stage Attention …
Forecast the electricity price of US using a wavelet transform-based hybrid model
W Qiao, Z Yang - Energy, 2020 - Elsevier
Wavelet transform (WT), as a data preprocessing algorithm, has been widely applied in
electricity price forecasting. However, this deterministic-based algorithm does not present …
electricity price forecasting. However, this deterministic-based algorithm does not present …
Ocean wave energy forecasting using optimised deep learning neural networks
PMR Bento, JAN Pombo, RPG Mendes, MRA Calado… - Ocean …, 2021 - Elsevier
Ocean renewable energy is a promising inexhaustible source of renewable energy, with an
estimated harnessing potential of approximately 337 GW worldwide, which could re-shape …
estimated harnessing potential of approximately 337 GW worldwide, which could re-shape …
Hybrid ensemble intelligent model based on wavelet transform, swarm intelligence and artificial neural network for electricity demand forecasting
Availability of electrical energy affects many facets of an entire economy of a country. This
has made short-term electrical load forecasting an important area in recent years for policy …
has made short-term electrical load forecasting an important area in recent years for policy …
Deep learning with long short-term memory neural networks combining wavelet transform and principal component analysis for daily urban water demand forecasting
B Du, Q Zhou, J Guo, S Guo, L Wang - Expert Systems with Applications, 2021 - Elsevier
A reliable and accurate urban water demand forecasting plays a significant role in building
intelligent water supplying system and smart city. Due to the high frequency noise and …
intelligent water supplying system and smart city. Due to the high frequency noise and …
Short-term load forecasting for microgrid energy management system using hybrid HHO-FNN model with best-basis stationary wavelet packet transform
Accurate prediction of load has become one of the most crucial issue in the energy
management system of the microgrid. Therefore, a precise load forecasting tool is necessary …
management system of the microgrid. Therefore, a precise load forecasting tool is necessary …
基于聚类经验模态分解的CNN-LSTM 超短期电力负荷预测
刘亚珲， 赵倩 - 电网技术, 2021 - epjournal.csee.org.cn
为了减少复杂环境因素对电力负荷超短期预测效果的影响, 提高算法的预测精度和运算效率,
该文提出一种基于聚类经验模态分解(cluster empirical mode decomposition, CEMD) …
该文提出一种基于聚类经验模态分解(cluster empirical mode decomposition, CEMD) …