Review and prospect of data-driven techniques for load forecasting in integrated energy systems
With synergies among multiple energy sectors, integrated energy systems (IESs) have been
recognized lately as an effective approach to accommodate large-scale renewables and …
recognized lately as an effective approach to accommodate large-scale renewables and …
Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges
J Yang, S Li, Z Wang, H Dong, J Wang, S Tang - Materials, 2020 - mdpi.com
The detection of product defects is essential in quality control in manufacturing. This study
surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects …
surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects …
HBO-LSTM: Optimized long short term memory with heap-based optimizer for wind power forecasting
The forecasting and estimation of wind power is a challenging problem in renewable energy
generation due to the high volatility of wind power resources, inevitable intermittency, and …
generation due to the high volatility of wind power resources, inevitable intermittency, and …
Short-term load forecasting based on LSTM networks considering attention mechanism
J Lin, J Ma, J Zhu, Y Cui - International Journal of Electrical Power & Energy …, 2022 - Elsevier
Reliable and accurate zonal electricity load forecasting is essential for power system
operation and planning. Probabilistic load forecasts can present more comprehensive …
operation and planning. Probabilistic load forecasts can present more comprehensive …
A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting
This paper proposes an effective computing framework for Short-Term Load Forecasting
(STLF). The proposed technique copes with the stochastic variations of the load demand …
(STLF). The proposed technique copes with the stochastic variations of the load demand …
Short-term load forecasting for industrial customers based on TCN-LightGBM
Y Wang, J Chen, X Chen, X Zeng… - … on Power Systems, 2020 - ieeexplore.ieee.org
Accurate and rapid load forecasting for industrial customers has been playing a crucial role
in modern power systems. Due to the variability of industrial customers' activities, individual …
in modern power systems. Due to the variability of industrial customers' activities, individual …
Deep learning framework to forecast electricity demand
J Bedi, D Toshniwal - Applied energy, 2019 - Elsevier
The increasing world population and availability of energy hungry smart devices are major
reasons for alarmingly high electricity consumption in the current times. So far, various …
reasons for alarmingly high electricity consumption in the current times. So far, various …
Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm
G Memarzadeh, F Keynia - Electric Power Systems Research, 2021 - Elsevier
Nowadays, a basic commodity for a human being to lead a standard lifestyle with human
comfort irrespective of the nature of environmental conditions is electric power. The …
comfort irrespective of the nature of environmental conditions is electric power. The …
[HTML][HTML] Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges
The current expansion of theory and research on artificial intelligence in management and
organization studies has revitalized the theory and research on decision-making in …
organization studies has revitalized the theory and research on decision-making in …
Machine learning driven smart electric power systems: Current trends and new perspectives
MS Ibrahim, W Dong, Q Yang - Applied Energy, 2020 - Elsevier
The current power systems are undergoing a rapid transition towards their more active,
flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in …
flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in …