tahanakabi
PhD. in machine learning methods applied to smart grid and demand response programs. Currently working as a Machine learning engineer at Tecnotree Corporation.
www.tnakabi.comFinland
Pinned Repositories
AI-blog
Accompanying repository for Let's make a DQN / A3C series.
An-ANN-LSTM-based-Model-for-Learning-Individual-Customer-Behavior-in-Response-to-Electricity-Prices
An ANN-LSTM based Model for Learning Individual Customer Behavior in Response to Electricity Prices
binary_classification
Deep-Reinforcenment-learning-for-TCL-control
This is an attempt to implement the RL control method used in https://arxiv.org/pdf/1604.08382.pdf
DRL-for-microgrid-energy-management
We study the performance of various deep reinforcement learning algorithms for the problem of microgrid’s energy management system. We propose a novel microgrid model that consists of a wind turbine generator, an energy storage system, a population of thermostatically controlled loads, a population of price-responsive loads, and a connection to the main grid. The proposed energy management system is designed to coordinate between the different sources of flexibility by defining the priority resources, the direct demand control signals and the electricity prices. Seven deep reinforcement learning algorithms are implemented and empirically compared in this paper. The numerical results show a significant difference between the different deep reinforcement learning algorithms in their ability to converge to optimal policies. By adding an experience replay and a second semi-deterministic training phase to the well-known Asynchronous advantage actor critic algorithm, we achieved considerably better performance and converged to superior policies in terms of energy efficiency and economic value.
GHSOM-clustering
A GHSOM algorithm for electricity users classification
How-to-Predict-Stock-Prices-Easily-Demo
How to Predict Stock Prices Easily - Intro to Deep Learning #7 by Siraj Raval on Youtube
microgrid_demo
Optimal-Price-Based-control-of-heterogeneous-thermostatically-controlled-loads-under-uncertainty-usi
we consider the problem of thermostatically controlled load (TCL) control through dynamic electricity prices, under partial observability of the environment and uncertainty of the control response. The problem is formulated as a Markov decision process where an agent must find a near-optimal pricing scheme using partial observations of the state and action. We propose a long-short-term memory (LSTM) network to learn the individual behaviors of TCL units. We use the aggregated information to predict the response of the TCL cluster to a pricing policy. We use this prediction model in a genetic algorithm to find the best prices in terms of profit maximization in an energy arbitrage operation. The simulation results show that the proposed method offers a profit equal to 96% of the theoretical optimal solution.
RNN_predict_robot_positions
The aim of this project is to predict the next 60 positions of a moving robot given training data and the previous positions. We used a RNN with long short term memory to predict the next positions as a time series.
tahanakabi's Repositories
tahanakabi/DRL-for-microgrid-energy-management
We study the performance of various deep reinforcement learning algorithms for the problem of microgrid’s energy management system. We propose a novel microgrid model that consists of a wind turbine generator, an energy storage system, a population of thermostatically controlled loads, a population of price-responsive loads, and a connection to the main grid. The proposed energy management system is designed to coordinate between the different sources of flexibility by defining the priority resources, the direct demand control signals and the electricity prices. Seven deep reinforcement learning algorithms are implemented and empirically compared in this paper. The numerical results show a significant difference between the different deep reinforcement learning algorithms in their ability to converge to optimal policies. By adding an experience replay and a second semi-deterministic training phase to the well-known Asynchronous advantage actor critic algorithm, we achieved considerably better performance and converged to superior policies in terms of energy efficiency and economic value.
tahanakabi/Optimal-Price-Based-control-of-heterogeneous-thermostatically-controlled-loads-under-uncertainty-usi
we consider the problem of thermostatically controlled load (TCL) control through dynamic electricity prices, under partial observability of the environment and uncertainty of the control response. The problem is formulated as a Markov decision process where an agent must find a near-optimal pricing scheme using partial observations of the state and action. We propose a long-short-term memory (LSTM) network to learn the individual behaviors of TCL units. We use the aggregated information to predict the response of the TCL cluster to a pricing policy. We use this prediction model in a genetic algorithm to find the best prices in terms of profit maximization in an energy arbitrage operation. The simulation results show that the proposed method offers a profit equal to 96% of the theoretical optimal solution.
tahanakabi/microgrid_demo
tahanakabi/An-ANN-LSTM-based-Model-for-Learning-Individual-Customer-Behavior-in-Response-to-Electricity-Prices
An ANN-LSTM based Model for Learning Individual Customer Behavior in Response to Electricity Prices
tahanakabi/Deep-Reinforcenment-learning-for-TCL-control
This is an attempt to implement the RL control method used in https://arxiv.org/pdf/1604.08382.pdf
tahanakabi/GHSOM-clustering
A GHSOM algorithm for electricity users classification
tahanakabi/RNN_predict_robot_positions
The aim of this project is to predict the next 60 positions of a moving robot given training data and the previous positions. We used a RNN with long short term memory to predict the next positions as a time series.
tahanakabi/AI-blog
Accompanying repository for Let's make a DQN / A3C series.
tahanakabi/binary_classification
tahanakabi/How-to-Predict-Stock-Prices-Easily-Demo
How to Predict Stock Prices Easily - Intro to Deep Learning #7 by Siraj Raval on Youtube
tahanakabi/recommender_system_KNN
tahanakabi/KNN_predict_robot_position
The aim of this project is to predict the next 60 positions of a moving robot given training data and the previous positions. We used a KNN regressor to predict the next positions as a regression problem using the last position and velocity.
tahanakabi/lida-dashboard
Automatic Generation of Visualizations and Infographics using Large Language Models
tahanakabi/pandas-ai
Chat with your data (SQL, CSV, pandas, polars, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG.
tahanakabi/tahanakabi
Config files for my GitHub profile.