Arshid Ali's starred repositories
time-series-transformers-review
A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.
awesome-AI-for-time-series-papers
A professional list of Papers, Tutorials, and Surveys on AI for Time Series in top AI conferences and journals.
DRL-Energy-Management
Deep reinforcement learning based energy management strategy for hybrid electric vehicle
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
Time_Series_Classification
The pytorch implementation of time series classification model, including LSTM_FCN, MLSTM_FCN, GRU_FCN, mWDN, Rocket, TCN, XCM, gMLP, TabTransformer, GatedTabTransformer
Transfer_DRL_EMS
Cross-type transfer for deep reinforcement learning based hybrid electric vehicle energy management
Reinforcement-Learning-forPowerGrid-Operation_and_Maineinance
reinforcement learning for power grid optimal operations and maintenance
smart-grid-scheduling
Energy-consumption scheduling algorithms for smart electrical grids
miles-guess
Machine learning models for estimating aleatoric and epistemic uncertainty with evidential and ensemble methods.
power-grid-model-workshop
Workshop assignments on power-grid-model: A distribution power system analysis library
SmartGrid-Failure-Prediction
A neural network approach for predicting imminent failures in a smart power grid
MA-VPP-RL-bidding
Masterthesis: Reinforcement Learning based Strategic Bidding in the Balancing Market with a Virtual Power Plant
SmartGridFraudDetection
Electricity Fraud Detection in Smart Grids
metro_state_spring_2017
Severe Weather Analysis and Forecasting with Python Tools tutorial.
Power-System-Dynamic-Security-Analysis-Integrating-Supervised-Machine-Learning-Classification-Appro
🌎 I showed how successful Ireland has been so far in terms of accommodating renewable energies within its electricity system and what are the current challenges and needs of its power system ⁉️ 🏭 I briefly talked about the new system services, particularly fast frequency response from commercial demand response units such as data centres, supermarkets, commercial offices etc and how they can be beneficial to integrating more renewables in the power system! ✔️ 👨💻 👷🏽I then talked about machine learning and whether it can take the place of our current frequency stability analysis platform. ✅ The machine learning model could reduce the computational time from 216 hours to less than 1 second by replacing the frequency stability analysis with a classifier to detect "Normal" or "At risk" operating hours. ✅The machine learning framework to deal with the imbalanced dataset from power system dynamic security has been implemented with collaboration of "UCD School of Mathematics and Statistics"
PowerModelsDistributionStateEstimation.jl
A Julia Package for Power System State Estimation.
PowerSystemsAnalysis
A Software to Analyze Power Systems
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.