Overload Alarm Prediction in Power Distribution Transformers
The growing demand for electricity puts more strain on the grid, requiring automated and
proactive strategies such as overload prediction to improve grid maintenance. However, the …
proactive strategies such as overload prediction to improve grid maintenance. However, the …
Agent-based machine learning assessment on real data for improvement of the daily load factor using demand response program
One method of demand response (DR) is time-of-use planning, which assigns a fee to the
use of low-load (LL), middle-load (ML), and peak-load (PL) hours. This strategy can be …
use of low-load (LL), middle-load (ML), and peak-load (PL) hours. This strategy can be …
ML-assistant for human operators using alarm data to solve and classify faults in electrical grids
Nowadays, human operators at control centers analyze a large volume of alarm information
during outage events and must act fast to restore the service. To assist operator decisions …
during outage events and must act fast to restore the service. To assist operator decisions …
Wavelet Based fMRI Analysis for Autism Spectrum Disorder Detection using Feature Selection and Ridge Classifier
As autism spectrum disorder (ASD) rates rise, timely diagnosis and treatment are
increasingly crucial. However, current diagnostic methods rely on subjective criteria, such as …
increasingly crucial. However, current diagnostic methods rely on subjective criteria, such as …
Advanced Demand Forecasting and Pricing in Moroccan Auto Industry: A CNN-LSTM-Attention and Reinforcement Learning Approach
This paper presents a novel forecasting model that combines Convolutional Neural Network-
Long Short-Term Memory with an Attention mechanism (CNN-LSTM-Attention) and …
Long Short-Term Memory with an Attention mechanism (CNN-LSTM-Attention) and …