Air-conditioner-controller-recommendation-using-fuzzy-logic

1. First Method

Install package from requirements.txt

pip install -r requirements.txt

OR Install package separately

pip install flask
pip install scikit-fuzzy

Run the development environment

flask run

2. Second Method

docker build --tag python-docker .
docker run -d -p 5000:5000 python-docker

Graph of membership of temperature and humidity

Importing libraries

import numpy as np
import skfuzzy as fuzz
from skfuzzy import control as ctrl

Initialize the fuzzy variable responsible for representing input set and output set

# Antecedents
temp= ctrl.Antecedent(np.arange(0, 41), 'temperature')
hum = ctrl.Antecedent(np.arange(0, 101), 'humidity')

# Consequents
cmd = ctrl.Consequent(np.arange(15, 27), 'command')

Trapezoidal membership function

A trapezoidal membership function in fuzzy logic is a membership function that has four parameters: a, b, c, and d, where a and d are the lower and upper bounds of the variable's domain, and b and c are the values at which the membership function starts and stops increasing, respectively. The trapezoidal membership function looks like a trapezoid, where the left and right sides are defined by a and d, and the top is defined by b and c. The trapezoidal membership function is used to model variables that have a gradual transition from one membership grade to another, but that also have a plateau in the middle of their domain where the membership grade is high.

Gaussian membership function

In fuzzy logic, the Gaussian or bell-shaped membership function is a commonly used function to represent the degree of membership of a value in a fuzzy set. The Gaussian function is defined as:

$μ(x) = e^{(-(\frac{x-c}{σ})^2)}$

where $c$ is the center of the curve and $σ$ is the standard deviation which controls the width of the curve.

The Gaussian function has a bell-shaped curve with a peak at $x = c$ and the curve decreases as the distance from the peak increases. The degree of membership of a value $x$ in a fuzzy set can be determined by evaluating the Gaussian function at $x$. The resulting value represents the degree of membership of $x$ in the fuzzy set.

The shape of the Gaussian function can be adjusted by changing the values of $c$ and $σ$. A smaller value of $σ$ results in a narrower peak, while a larger value of $σ$ results in a broader peak. The value of $c$ determines the location of the peak along the x-axis.

The Gaussian membership function is commonly used in fuzzy control systems, where it is used to represent the degree of membership of a variable in a set. It is also used in image processing, pattern recognition, and data analysis.

Triangle membership function

In fuzzy logic, the triangle membership function is a type of membership function that is shaped like a triangle. It is commonly used to represent fuzzy sets where the membership values increase linearly from 0 to 1 and then decrease linearly from 1 to 0.

The triangle membership function is defined by three parameters: the lower bound $a$, the peak value $b$, and the upper bound $c$. It is given by the following formula:

$$ \begin{equation} \mu(x)=\begin{cases} 0 & \text{if } x \leq a \newline \dfrac{x-a}{b-a} & \text{if } a < x \leq b \newline \dfrac{c-x}{c-b} & \text{if } b < x \leq c \newline 0 & \text{if } x > c \end{cases} \end{equation} $$

Here, $\mu(x)$ represents the degree of membership of an input value $x$ in the fuzzy set. The function starts at 0 at $x=a$, rises linearly to 1 at $x=b$, and then falls linearly to 0 at $x=c$. The triangle membership function is symmetric around the peak value $b$.

The triangle membership function is commonly used in fuzzy logic controllers to represent linguistic variables, where the peak value $b$ represents the "typical" value of the variable and the bounds $a$ and $c$ represent the range of values where the variable is considered to be "low" or "high".

# Temperature memberships
temp['coldest'] = fuzz.trapmf(temp.universe, [0, 4, 6, 8])
temp['cold'] = fuzz.trapmf(temp.universe, [6, 10, 12, 16])
temp['warm'] = fuzz.trapmf(temp.universe, [12, 16, 18, 24])
temp['hot'] = fuzz.trapmf(temp.universe, [18, 22, 24, 32])
temp['hottest'] = fuzz.trapmf(temp.universe, [24, 28, 30, 40])

# Humidity memberships
hum['low'] = fuzz.gaussmf(hum.universe, 0, 30) # 0,15
hum['optimal'] = fuzz.gaussmf(hum.universe, 50, 15) # 50, 15
hum['high'] = fuzz.gaussmf(hum.universe, 100, 50) # 100,15

# Command memberships
cmd['cool'] = fuzz.trimf(cmd.universe, [15, 17, 20])
cmd['warmup'] = fuzz.trimf(cmd.universe, [18, 20, 26])
# Rule system
# Rules for warming up
rule1 = ctrl.Rule(
    (temp['coldest'] & hum['low']) |
    (temp['coldest'] & hum['optimal']) |
    (temp['coldest'] & hum['high']) |
    (temp['cold'] & hum['low']) |
    (temp['cold'] & hum['optimal']) |
    (temp['warm'] & hum['low']), cmd['warmup'])

# Rules for cooling up
rule2 = ctrl.Rule(
    (temp['warm'] & hum['optimal']) |
    (temp['warm'] & hum['high']) |
    (temp['hot'] & hum['optimal']) |
    (temp['hot'] & hum['high']) |
    (temp['hottest'] & hum['low']) |
    (temp['hottest'] & hum['optimal']) |
    (temp['hottest'] & hum['high']), cmd['cool'])

# Control System Creation and Simulation
cmd_ctrl = ctrl.ControlSystem([rule1, rule2])
cmd_output = ctrl.ControlSystemSimulation(cmd_ctrl)

def generateOutput(temperature_value, humidity_value):
    cmd_output.input['temperature'] = temperature_value
    cmd_output.input['humidity'] = humidity_value

    cmd_output.compute()
    # Print output command and plots
    print("Command is defined between 15 y 26")
    re_temp = round(cmd_output.output['command'], 1)
    if (cmd_output.output['command'] > 20):
        return 'Warm up', re_temp
    elif (cmd_output.output['command'] < 20 and cmd_output.output['command'] > 18):
        return 'No change', re_temp
    else:
        return 'Cool Up', re_temp

Contributing

Contributions are welcome! If you find any issues or want to add new features, feel free to submit a pull request.

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