Taha NAKABI's repositories
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.
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.
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
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
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
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.
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
Config files for my GitHub profile.