2016-04-05-differences.ipynb

Kernel: Python 3 (Anaconda)

We want to calculate the energy that has been conducted through the walls of a building that we have the temperature data for.

You've done each of these pieces separately but now we are combining them.

• Assume a UA value of 2 watts per kelvin

• Assume $q = UA \Delta T$ is valid

• Given a graph of a temperature difference, what is the energy lost?

$$Q = UA \Delta T \Delta t$$$$Q_i = UA \Delta T_i \Delta t_i$$$$Q_{total} = \sum Q_i$$

What is $\Delta t$?

$$Q = \int UA \Delta T dt$$

Since the insulation isn't changing over time, we don't need to keep the UA inside the integral.

$$Q = UA \int \Delta T dt$$

• Show the reimann sum sort of thing.

• Estimate using geometry.

• Show how to calculate using columns.

• Perform calculations with columns.

%matplotlib notebook
# plt is the library with all the plotting
import matplotlib.pyplot as plt
# pd is the library with the spreadsheet functions
import pandas as pd

data = pd.read_csv('2016-02-26-combined.csv', parse_dates=True)
data = pd.read_csv('2016-02-16-combined.csv', parse_dates=True)

data.head()

data['temp_difference'] = data['t_in'] - data['t_out']
((data['t_in'] - data['t_out']) * 300 * 2).sum()
delta_t_seconds = 300
UA_watt_per_kelvin = 2
(data['temp_difference'] * delta_t_seconds * UA_watt_per_kelvin).sum()

151500.59999999998

plt.plot(data['time'], data['t_in']-data['t_out'], marker='o')
plt.grid()
plt.xlabel('time (sec)')
plt.ylabel('Temp Difference (C)')

WARNING: Some output was deleted.

data['temp_difference'] = data['t_in'] - data['t_out']

151500.59999999998

power = 47
time = 3300
power * time

155100

data['Time']

0     2016-02-16 07:25:00
1     2016-02-16 07:30:00
2     2016-02-16 07:35:00
3     2016-02-16 07:40:00
4     2016-02-16 07:45:00
5     2016-02-16 07:50:00
6     2016-02-16 07:55:00
7     2016-02-16 08:00:00
8     2016-02-16 08:05:00
9     2016-02-16 08:10:00
10    2016-02-16 08:15:00
11    2016-02-16 08:20:00
12    2016-02-16 08:25:00
13    2016-02-16 08:30:00
14    2016-02-16 08:35:00
15    2016-02-16 08:40:00
16    2016-02-16 08:45:00
17    2016-02-16 08:50:00
18    2016-02-16 08:55:00
19    2016-02-16 09:00:00
20    2016-02-16 09:05:00
21    2016-02-16 09:10:00
22    2016-02-16 09:15:00
23    2016-02-16 09:20:00
24    2016-02-16 09:25:00
25    2016-02-16 09:30:00
26    2016-02-16 09:35:00
27    2016-02-16 09:40:00
28    2016-02-16 09:45:00
29    2016-02-16 09:50:00
30    2016-02-16 09:55:00
31    2016-02-16 10:00:00
32    2016-02-16 10:05:00
33    2016-02-16 10:10:00
34    2016-02-16 10:15:00
35    2016-02-16 10:20:00
36    2016-02-16 10:25:00
37    2016-02-16 10:30:00
38    2016-02-16 10:35:00
39    2016-02-16 10:40:00
40    2016-02-16 10:45:00
41    2016-02-16 10:50:00
42    2016-02-16 10:55:00
43    2016-02-16 11:00:00
44    2016-02-16 11:05:00
45    2016-02-16 11:10:00
46    2016-02-16 11:15:00
47    2016-02-16 11:20:00
48    2016-02-16 11:25:00
49    2016-02-16 11:30:00
Name: Time, dtype: object