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About me

Hello, I am Sultan

  • Born in Kazakhstan, studied Electrical and Electronic engineering at Nazarbayev University, Astana. 
  • Currently finishing my Master in Robotics at Innopolis University, Russia.
This blog is an attempt to figure out what life is about and to reflect my educational and personal findings in various fields.

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Machine Learning. Part II.

Non-residential building occupancy modeling. Part II. Occupancy classification So dataset was taken from this place . The dataset comprised of different sources: surveys, data logging from sensors, weather, environment variables. Total feature list consist of 118 features and can be grouped as general (occupancy, time), environment (indoor, outdoor), personal characteristics (age, office type, accepted sensation range etc), comfort/productivity/satisfaction, behavior (clothing, window, interaction with thermostat etc ), personal values (choices on different set points). It contains data on 24 occupants whether it private office or joint one, the first task is to implement binary classification of each occupant using some input data from sensors and time. For rapid protoyping I will use python Tensor Flow wrapper Keras along Anaconda framework. First, loading all required libraries from keras.models import Sequential from keras.layers import Dense import numpy ...
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Application of Reinforcement Learning in HVAC systems. Part 2

So, how to model an office building to simulate the work of our controller? In short, I have used the following list of programs: Matlab, EnergyPlus and MLE+ in tandem. First things first, EnergyPlus - is an building simulation engine that will allow you to modulate maybe not all but most of the physical phenomena running inside real physical structures, including heat transfer and temperature spread. Even though it is a quite a hard to understand how to use EnergyPlus if you are not expert (maybe even for civil engineers), you can always download already designed models of a buildings like I did), which can be found on energy.gov site. Therefore, I took one floor three office medium building EnergyPlus model that comes with and .idf and weather files. Basically, the building has three rooms with electric radiant heating floors and one window and some ventilation system. Simple schematics shown on a picture below: As you may guess, I want to be able to test some controllers on th...
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Machine Learning. Grid search

How to choose best fit parameters for your ANN or SVM model using scikit learn grid search ? (This topic refers to classification problem). As a beginner, after creating a couple of simple neural networks either via tensorflow or Matlab I was thinking about one question: How do I decide which network architecture (number of layers, neurons) and parameters (epoch number, batch size, optimization algorithms and initialization types) to choose? And actually, this question was answered via Grid Search scikit python library that automatizes process of the best parameters. In example below I tried cover all typical tuning parameters: # Since running GridSearch may be quite time/calculation consuming # I used GPU based tensorflow in order to speed-up calculation import tensorflow as tf from keras.backend.tensorflow_backend import set_session config = tf.ConfigProto() # Defining GPU usage limit to 75% config.gpu_options.per_process_gpu_memory_fraction = 0.75 ...
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