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
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
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
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
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
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 …