Arshid Ali's starred repositories
Reinforcement-Learning-for-Real-time-Pricing-and-Scheduling-Control-in-EV-Charging-Stations
Reinforcement Learning for Real time Pricing and Scheduling Control in EV Charging Stations
ElectricityTheftDetection
Electricity-Theft Detection in Smart Grids
TII_Wide-Deep_Electricity_Theft_Detection
This is the source code of our paper on electricity-theft detection published in TII in the 2017 year.
smart-grid-scheduling
Energy-consumption scheduling algorithms for smart electrical grids
SmartGrid-Failure-Prediction
A neural network approach for predicting imminent failures in a smart power grid
data-science-smart-grid-stability
Machine learning - Deep learning - Smart grid stability
Electricity-Theft-Detection
Electricity Theft Detection
SmartGridFraudDetection
Electricity Fraud Detection in Smart Grids
Predictive_and_Proactive_Maintenance_in_Power_Systems
This repository contains the analysis of predictive and proactive maintenance in power systems. It includes fault simulation, ML fault prediction, thermographic infrared images analysis from diverse power systems. The aim of the project is to detect failure in the equipment through Computer Vision and Machine Learning methods.
electric_motor_fault_detection
In this work a machine learning model to detect static eccentricity fault in three phase induction motors is presented. The proposed method utilizes the k-nearest neighbour algorithm to classify static eccentricity from healthy operating condition.
Electric-Fault-Prediction
In this project we are trying to predict tripping at grid stations by applying simple machine learning algorithms.
Induction-Motor-Fault-Detection-using-KNN-and-FFT
electric motors are critical components of a modern system. Their failure causes sever impacts on operation. Monitoring of the electric motors using vibration sensors has become a popular method in predictive analysis. Any slight imbalances, bearing defect, Rotor defect, changes in load, loose feet, and etc. can result in the failure of the motor. In this project, experimental data recorded through 8 sensors of acceleration, rotation, and sound are used to identify faulty induction motor.
Unsupervised_Electricity_Theft_Detection
A Novel Unsupervised Data-Driven Method for Electricity Theft Detection in AMI Using Observer Meters
Electricity-theft-detection-in-smart-grids-based-on-deep-learning
The ML models used in this work for training are Utilizing CNN- based deep learning. And also compared using Logistic Regression, Decision Tree Regression, Random Forest Regression, Support Vector Machine (SVM).
Power_theft_detection-from-smart-meters
This system uses 2 machine learning approaches consequently to detect electricity theft in a locality by analysis of power usage patterns of households
Deep-Learning-classifiers-for-Condition-Monitoring-of-Induction-Motor
Extracted statistical features of stator current signal at various fault and healthy states of Induction motor and implemented various deep learning classifier like ANN, CNN etc to classify the type of faults occurs.
Electric-Grid-Fault-Prediction
Machine Learning Model for prediction of fault in Electrical Grid
Weather-forecast-quality-assessment-and-power-grid-faults-prediction
Data Science master thesis.
CNN-and-GRU-based-deep-neural-network-for-electricity-theft-detection
The objective of the project is to detect whether the electricity theft by analysing the electricity consumption pattern.
GAN-PyTorch-Parked-Vehicle
In this project, I implemented a Generative Adversarial Network (GAN) using PyTorch based on the repo: https://github.com/diegoalejogm/gans/blob/master/1.%20Vanilla%20GAN%20PyTorch.ipynb, to generate fake images of parked vehicles.
Transferable-Adversarial-Training
Code release for Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers (ICML2019)
transfer-learning
transfer learning using pytorch
RandomForest-Classification
Classifying remote sensing data with random forest
DeepLearningForTimeSeriesForecasting
A tutorial demonstrating how to implement deep learning models for time series forecasting
Individual-household-electric-power-consumption-Data-Set-
Individual household electric-power consumption Data Set (LSTM) [tutorial]
kite-power
Wind Energy Harvesting with Reinforcement Learning :airplane:
predicting_the_wind
Data Science in Wind Resource Assessment Tutorial at PyData San Diego, March 2020
SBSPS-Challenge-1160-Predicting-the-energy-output-of-wind-turbine-based-on-weather-condition
Predicting Energy Output of Wind Turbine
Wind_Prediction-Seq2Seq-pred
Sequence to Sequence Prediction using CNN-LSTM, ConvLSTM, SBU-LSTM, BD-LSTM, etc.