A state-of-the-art review of artificial intelligence techniques for short-term electric load forecasting
According to privatization and deregulation of power system, accurate electric load
forecasting has come into prominence recently. The new energy market and the smart grid …
forecasting has come into prominence recently. The new energy market and the smart grid …
Modeling of district load forecasting for distributed energy system
W Ma, S Fang, G Liu, R Zhou - Applied Energy, 2017 - Elsevier
Distributed energy system (DES) has successfully aroused increasing interests among
energy policy makers and system designers, as its potential of replacing conventional …
energy policy makers and system designers, as its potential of replacing conventional …
A novel convolutional neural network framework based solar irradiance prediction method
N Dong, JF Chang, AG Wu, ZK Gao - … Journal of Electrical Power & Energy …, 2020 - Elsevier
As an important part of solar power system, photovoltaic grid-connected system and solar
thermal system, solar irradiance has the inherent characteristics of variability and …
thermal system, solar irradiance has the inherent characteristics of variability and …
Enhanced ensemble structures using wavelet neural networks applied to short-term load forecasting
Load forecasting implies directly in financial return and information for electrical systems
planning. A framework to build wavenet ensemble for short-term load forecasting is …
planning. A framework to build wavenet ensemble for short-term load forecasting is …
A GPSO-optimized convolutional neural networks for EEG-based emotion recognition
An urgent problem in the field of deep learning is the optimization of model construction,
which frequently hinders its performance and often needs to be designed by experts …
which frequently hinders its performance and often needs to be designed by experts …
Residential load forecasting using wavelet and collaborative representation transforms
Short-term household-level load forecasting requires to acquire knowledge about lifestyle
and consumption patterns of residents. A new forecasting framework is proposed in this …
and consumption patterns of residents. A new forecasting framework is proposed in this …
Gesture recognition based on BP neural network improved by chaotic genetic algorithm
DJ Li, YY Li, JX Li, Y Fu - International Journal of Automation and …, 2018 - Springer
Aim at the defects of easy to fall into the local minimum point and the low convergence
speed of back propagation (BP) neural network in the gesture recognition, a new method …
speed of back propagation (BP) neural network in the gesture recognition, a new method …
[HTML][HTML] A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers
Short-term load forecasting (STLF) is vital for the effective and economic operation of power
grids and energy markets. However, the non-linearity and non-stationarity of electricity …
grids and energy markets. However, the non-linearity and non-stationarity of electricity …
Bayesian optimized echo state network applied to short-term load forecasting
G Trierweiler Ribeiro, J Guilherme Sauer… - Energies, 2020 - mdpi.com
Load forecasting impacts directly financial returns and information in electrical systems
planning. A promising approach to load forecasting is the Echo State Network (ESN), a …
planning. A promising approach to load forecasting is the Echo State Network (ESN), a …
In search of deep learning architectures for load forecasting: A comparative analysis and the impact of the Covid-19 pandemic on model performance
In power grids, short-term load forecasting (STLF) is crucial as it contributes to the
optimization of their reliability, emissions, and costs, while it enables the participation of …
optimization of their reliability, emissions, and costs, while it enables the participation of …