bigbelly


Modeling and Simulation in Python

Copyright 2017 Allen Downey

License: Creative Commons Attribution 4.0 International


In [1]:
# Configure Jupyter so figures appear in the notebook
%matplotlib inline

# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'

# import functions from the modsim library
from modsim import *

Bigbelly

https://www.youtube.com/watch?v=frix_zTkPEs

If the following import fails, open a terminal and run

conda install -c conda-forge pysolar


In [2]:
from pysolar.solar import *

In [3]:
from datetime import datetime, timedelta

In [4]:
dt = datetime.now()


Out[4]:
datetime.datetime(2018, 10, 29, 14, 13, 30, 886072)

In [5]:
from pytz import timezone

dt = pytz.timezone('EST').localize(dt)


Out[5]:
datetime.datetime(2018, 10, 29, 14, 13, 30, 886072, tzinfo=<StaticTzInfo 'EST'>)

In [6]:
get_altitude(42.2931671, -71.263665, dt)


Out[6]:
22.52017419635824

In [7]:
latitude_deg = 42.3
longitude_deg = -71.3
altitude_deg = get_altitude(latitude_deg, longitude_deg, dt)
azimuth_deg = get_azimuth(latitude_deg, longitude_deg, dt)
radiation.get_radiation_direct(dt, altitude_deg)

# result is in Watts per square meter


Out[7]:
781.1690621573555

In [8]:
location = State(lat_deg=42.3, lon_deg=-71.3)


Out[8]:
values
lat_deg 42.3
lon_deg -71.3

In [9]:
dt = datetime(year=2017, month=9, day=15, hour=12, minute=30)
dt = pytz.timezone('EST').localize(dt)


Out[9]:
datetime.datetime(2017, 9, 15, 12, 30, tzinfo=<StaticTzInfo 'EST'>)

In [10]:
def compute_irradiance(location, dt):
    degree = UNITS.degree
    watt = UNITS.watt
    meter = UNITS.meter
    
    sun = State(
        altitude_deg = get_altitude(location.lat_deg, location.lon_deg, dt),
        azimuth_deg = get_azimuth(location.lat_deg, location.lon_deg, dt)
    )

    if sun.altitude_deg <= 0:
        irradiance = 0
    else:
        irradiance = radiation.get_radiation_direct(dt, sun.altitude_deg)

    sun.set(irradiance = irradiance * watt / meter**2)
    return sun

In [11]:
sun = compute_irradiance(location, dt)


Out[11]:
values
altitude_deg 48.9295
azimuth_deg 199.128
irradiance 886.7325337613937 watt / meter ** 2

In [12]:
dt = datetime(year=2017, month=9, day=15)
dt = pytz.timezone('EST').localize(dt)

delta_t = timedelta(minutes=15)

result = TimeSeries()
for i in range(24 * 4):
    dt += delta_t
    sun = compute_irradiance(location, dt)
    result[dt] = sun.irradiance.magnitude

result


Out[12]:
values
2017-09-15 00:15:00-05:00 0.000000
2017-09-15 00:30:00-05:00 0.000000
2017-09-15 00:45:00-05:00 0.000000
2017-09-15 01:00:00-05:00 0.000000
2017-09-15 01:15:00-05:00 0.000000
2017-09-15 01:30:00-05:00 0.000000
2017-09-15 01:45:00-05:00 0.000000
2017-09-15 02:00:00-05:00 0.000000
2017-09-15 02:15:00-05:00 0.000000
2017-09-15 02:30:00-05:00 0.000000
2017-09-15 02:45:00-05:00 0.000000
2017-09-15 03:00:00-05:00 0.000000
2017-09-15 03:15:00-05:00 0.000000
2017-09-15 03:30:00-05:00 0.000000
2017-09-15 03:45:00-05:00 0.000000
2017-09-15 04:00:00-05:00 0.000000
2017-09-15 04:15:00-05:00 0.000000
2017-09-15 04:30:00-05:00 0.000000
2017-09-15 04:45:00-05:00 0.000000
2017-09-15 05:00:00-05:00 0.000000
2017-09-15 05:15:00-05:00 0.000000
2017-09-15 05:30:00-05:00 0.000000
2017-09-15 05:45:00-05:00 32.163770
2017-09-15 06:00:00-05:00 171.524281
2017-09-15 06:15:00-05:00 315.417455
2017-09-15 06:30:00-05:00 430.773528
2017-09-15 06:45:00-05:00 519.951046
2017-09-15 07:00:00-05:00 589.344983
2017-09-15 07:15:00-05:00 644.186729
2017-09-15 07:30:00-05:00 688.226752
... ...
2017-09-15 16:45:00-05:00 461.969588
2017-09-15 17:00:00-05:00 355.515852
2017-09-15 17:15:00-05:00 219.817019
2017-09-15 17:30:00-05:00 69.743423
2017-09-15 17:45:00-05:00 0.218675
2017-09-15 18:00:00-05:00 0.000000
2017-09-15 18:15:00-05:00 0.000000
2017-09-15 18:30:00-05:00 0.000000
2017-09-15 18:45:00-05:00 0.000000
2017-09-15 19:00:00-05:00 0.000000
2017-09-15 19:15:00-05:00 0.000000
2017-09-15 19:30:00-05:00 0.000000
2017-09-15 19:45:00-05:00 0.000000
2017-09-15 20:00:00-05:00 0.000000
2017-09-15 20:15:00-05:00 0.000000
2017-09-15 20:30:00-05:00 0.000000
2017-09-15 20:45:00-05:00 0.000000
2017-09-15 21:00:00-05:00 0.000000
2017-09-15 21:15:00-05:00 0.000000
2017-09-15 21:30:00-05:00 0.000000
2017-09-15 21:45:00-05:00 0.000000
2017-09-15 22:00:00-05:00 0.000000
2017-09-15 22:15:00-05:00 0.000000
2017-09-15 22:30:00-05:00 0.000000
2017-09-15 22:45:00-05:00 0.000000
2017-09-15 23:00:00-05:00 0.000000
2017-09-15 23:15:00-05:00 0.000000
2017-09-15 23:30:00-05:00 0.000000
2017-09-15 23:45:00-05:00 0.000000
2017-09-16 00:00:00-05:00 0.000000

96 rows × 1 columns


In [13]:
result.plot()


Out[13]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc5239869b0>

In [14]:
cm = UNITS.centimeter
meter = UNITS.meter
watt = UNITS.watt
second = UNITS.second
joule = UNITS.joule


Out[14]:
joule

In [15]:
width = 45 * cm


Out[15]:
45 centimeter

In [16]:
area = width**2


Out[16]:
2025 centimeter2

In [17]:
power = sun.irradiance * area


Out[17]:
0.0 centimeter2 watt/meter2

In [18]:
area = area.to(meter**2)


Out[18]:
0.2025 meter2

In [19]:
power = sun.irradiance * area


Out[19]:
0.0 watt

In [20]:
delta_t = 1 * second
energy = power * delta_t


Out[20]:
0.0 second watt

In [21]:
energy.to(joule)


Out[21]:
0.0 joule

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