Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review
Meteorological changes urge engineering communities to look for sustainable and clean
energy technologies to keep the environment safe by reducing CO 2 emissions. The …
energy technologies to keep the environment safe by reducing CO 2 emissions. The …
Prospects and challenges of the machine learning and data-driven methods for the predictive analysis of power systems: A review
The use of machine learning and data-driven methods for predictive analysis of power
systems offers the potential to accurately predict and manage the behavior of these systems …
systems offers the potential to accurately predict and manage the behavior of these systems …
Load forecasting for regional integrated energy system based on complementary ensemble empirical mode decomposition and multi-model fusion
The multiple loads of the Regional Integrated Energy System (RIES) possess characteristics
of randomness and relatively higher complexity. The current forecasting methods struggle to …
of randomness and relatively higher complexity. The current forecasting methods struggle to …
Robust recurrent neural networks for time series forecasting
Recurrent neural networks (RNNs) are widely utilized in time series forecasting tasks. In
practical applications, there are noises in real-life time series data. A model's generalization …
practical applications, there are noises in real-life time series data. A model's generalization …
[HTML][HTML] A novel approach to predicting the stability of the smart grid utilizing MLP-ELM technique
Evaluating and forecasting stability across different conditions is essential since smart grid
stabilization is among the most significant characteristics that could be employed to assess …
stabilization is among the most significant characteristics that could be employed to assess …
Efficient residential electric load forecasting via transfer learning and graph neural networks
The accurate short-term electric load forecasting (STLF) is critical for the safety and
economical operation of modern electric power systems. Recently, the graph neural network …
economical operation of modern electric power systems. Recently, the graph neural network …
[HTML][HTML] A hybrid stacking model for enhanced short-term load forecasting
The high penetration of distributed energy resources poses significant challenges to the
dispatch and operation of power systems. Improving the accuracy of short-term load …
dispatch and operation of power systems. Improving the accuracy of short-term load …
HSIC bottleneck based distributed deep learning model for load forecasting in smart grid with a comprehensive survey
Load forecasting is a vital part of smart grids for predicting the required electrical power
using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the …
using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the …
Meta-ANN–A dynamic artificial neural network refined by meta-learning for Short-Term Load Forecasting
In this paper a dynamic Artificial Neural Network (ANN) model called Meta-ANN is
developed for forecasting the short-term grid load. The primary ingredient of the model is a …
developed for forecasting the short-term grid load. The primary ingredient of the model is a …
A multi-scale spatial-temporal graph neural network-based method of multienergy load forecasting in integrated energy system
Accurately predicting multi-energy loads is essential for optimizing the dispatch and
economic operation of integrated energy systems (IES). However, existing multi-energy load …
economic operation of integrated energy systems (IES). However, existing multi-energy load …