Open alex9green opened 2 months ago
Are there specific commands or tools that can be used to interact with the database and make changes/updates ?
Hello alex9green,
this repository can simulate different heat pumps. For that you need the flow temperature of the water, the outside temperature for air/water heat pumps. You could start a simulation by downloading hplib and importing it in your file:
from hplib import hplib
than you could make a loop for all suitable heat pumps and than simulate the COP for every timestep
df = pd.read_csv('../input/TestYear.csv')
df['T_amb'] = df['T_in_primary'] # air/water heat pump -> T_amb = T_in_primary
# Simulate with values
# Load parameters
parameters = hpl.get_parameters('**modelname**')
# Create heat pump object with parameters
heatpump = hpl.HeatPump(parameters)
# whereas mode = 1 is for heating and mode = 2 is for cooling
results = heatpump.simulate(t_in_primary=df['T_in_primary'].values, t_in_secondary=df['T_in_secondary'].values, t_amb=df['T_amb'].values, mode=1)
Good luck with your project!
Kind regards, Hauke
I'd be incredibly grateful if someone could share some additional examples or resources that would be helpful for someone in my situation. (I don't have a programming background/just edit and run scripts )
Scenario: A house has a load of 8.7 kW ( A-7C) for a flow temperature of 55°C. Goal: I'd like to identify the most suitable heat pump for my needs and get an estimation of its performance using Degree Days data from https://www.degreedays.net/.
-or the possibility of optimizing defrost cycle parameters for heat pumps using real-world data. I am particularly interested in exploring how data-driven approaches can be applied to improve the efficiency and performance of heat pumps.
-select refrigerant type
Examples like that would be particularly helpful
Any insights or resources you can share would be greatly appreciated!
Thank you for your time and support.