isaac-severinsen/cylinder

cylinder

This file will become your README and also the index of your documentation.

Install

pip install cylinder

This basic model provides the core function for a step change calculation in a mixed hot water cylinder

How to use

Load some data that can be used to test the model - flow and electricity pricing

df = (pd.DataFrame(load_demand(path = Path('../data/drawprofiles'),bed=3,unit=3)))
df.columns=["flow"]
df = df.merge(load_power(path = Path('../data')), how='left', left_index=True, right_index=True)
df.head()

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	flow	price	price_kw	date	week	hour	day	peak	tou	cost	n_cost	sr_cost	lr_cost
timestamp
2020-01-01 00:00:00	0.0	7.920	0.007920	2020-01-01	1	0	2	0	0.02	0.027920	-0.22080	0.162438	0.149272
2020-01-01 00:01:00	0.0	8.526	0.008526	2020-01-01	1	0	2	0	0.02	0.028526	-0.21474	0.158675	0.145694
2020-01-01 00:02:00	0.0	9.132	0.009132	2020-01-01	1	0	2	0	0.02	0.029132	-0.20868	0.155010	0.142235
2020-01-01 00:03:00	0.0	9.738	0.009738	2020-01-01	1	0	2	0	0.02	0.029738	-0.20262	0.151437	0.138888
2020-01-01 00:04:00	0.0	10.344	0.010344	2020-01-01	1	0	2	0	0.02	0.030344	-0.19656	0.147952	0.135645

Create a hot water cylinder object and initialise it with the data

hwc = HWC(T_set=68, T_deadband=2, element=3000, radius=.25, height=1)

print(f'The HWC volume is {int(hwc.Volume*1000)} liters')

The HWC volume is 196 liters

Default thermogram

plt.imshow(hwc.thermogram)

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Run the model for a single day on thermostat and plot the results

results = []

for index, row in df[:24*60].iterrows():
  raw_flow = row['flow']
  hwc.flow = raw_flow*(hwc.T_demand-hwc.T_cold)/(hwc.T-hwc.T_cold)
  hwc._thermostat()
  hwc.T = hwc._update_temperatures(action=1)
  results.append([index,hwc.T, hwc.thermostat, hwc.flow,row.cost])
  r,c = row.day, row.hour
  hwc.thermogram[r,c] = hwc.thermostat * 1 * hwc.Q /60 + hwc.thermogram[r,c]*(1- 0.1)

results = pd.DataFrame(results, columns=['time','T','thermostat','flow','cost']).set_index('time')

plt.imshow(hwc.thermogram)

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fig, ax = plt.subplots(nrows=2, figsize=(12,6), sharex=True)
ax[0].plot(results['T'])
ax[0].set_ylabel('°C')
ax[0].set_title('Temperature')
ax[1].plot(results['thermostat'])
ax[1].xaxis.set_major_formatter(mdates.DateFormatter("%H:%M"))
ax[1].xaxis.set_minor_formatter(mdates.DateFormatter("%H:%M"))

Passive Cooling

hwc = HWC(T_set=68, T_deadband=2, element=3000, radius=.25, height=1)
results = []
for index, row in df[:24*60].iterrows():
  raw_flow = 0
  hwc.flow = raw_flow*(hwc.T_demand-hwc.T_cold)/(hwc.T-hwc.T_cold)
  hwc._thermostat()
  hwc.T = hwc._update_temperatures(action=0)
  results.append([index,hwc.T, hwc.thermostat, hwc.flow,row.cost])
  r,c = row.day, row.hour
  hwc.thermogram[r,c] = hwc.thermostat * 1 * hwc.Q /60 + hwc.thermogram[r,c]*(1- 0.1)
results = pd.DataFrame(results, columns=['time','T','thermostat','flow','cost']).set_index('time')

fig, ax = plt.subplots(nrows=2, figsize=(12,6), sharex=True)
ax[0].plot(results['T'])
ax[0].set_ylabel('°C')
ax[0].set_title('Temperature')
ax[1].plot(results['thermostat'])
ax[1].xaxis.set_major_formatter(mdates.DateFormatter("%H:%M"))
ax[1].xaxis.set_minor_formatter(mdates.DateFormatter("%H:%M"))

plt.imshow(hwc.thermogram)

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