Sultan Yerumbayev's blog

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 ...

Non-residential building occupancy modeling. Part III. Defining occupancy patterns. Recalling from the last part II we have got building occupancy data-set. In this part I will apply k-means cluster algorithm in order to identify typical occupancy patterns with daily resolution. To better visualize input data we have, see the following table below: Month Day Weekday Minutes Temperature Relative humidity Air Velocity Occupancy out of 24 7 31 3 779 23 89 1.3411 0 8 1 4 823 24 88 3.578 2 ... ... ... ... ... ... ... ... So I changed initial data by transforming Matlab absolute time readings into month, day, weekday(where 1 - Sunday,2 - Monday etc.), day time in minutes (with 15 min time step); temperature,velocity and humidity is general outdoor data; and finally total building occupancy. Original...

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 ...

Machine Learning. Non-residential building occupancy modeling. Part I. Idea : providing occupancy driven energy efficiency model. Since about 40% of building energy goes to HVAC(heating, ventilation and air conditioning) it is quite good idea to use these equipment when it is really required,e.g. switch on the air conditioner or ventilation when people in a room The study discusses approaches towards optimizing energy consumption of commercial buildings. Such task is considered to be a part of more broad topics, e.g. smart buildings, green buildings. During the past years, the number of papers dedicated to energy efficiency optimization in buildings has been growing which confirms societal concern about finding the most efficient methods of improving energy usage by buildings. To maximize building’s energy efficiency various methods are known and can be split into re-organizational advances and strengthening currently employed management systems [1], [2]. This methods incl...

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. Keep updated!