About

This project allows for calculation of amount of solar energy one can collect from faces of a house as well as changes of the energy over the heating season in a given geographical location.

A mesh file (.stl or .obj) with a model of a house is required. Heat-related parameters of a house are specified in a separated configuration file. After calculation is over, user gets plots and tabulated values of temperatures of elements inside house.

Model of thermal processes in the house are calculated via models described in the section Thermal theory in the project's documentation.

By changing different parameters of the house one can carry out calculations for every configuration and to choose the best combination of parameters to save energy for heating.

Credits:

• faces of mesh are processed by PyMesh library;
• to calculate solar power on each face of a house with respect to different tilts and azimuths, PVLIB is used; this library makes it possible to account for real weather when calculating power input.

Version

0.0.4

Documentation

Documentation.

Dependencies

numpy
scipy
trimesh
pvlib
pandas
matplotlib
mpld3
shapely
jinja2
netCDF4
siphon
tables

Installation

From PyPI:

$ pip install solarhouse

From GitHub:

$ git clone https://github.com/yaricp/py-solarhouse.git
$ cd py-solarhouse
$ ./install.sh

Usage

First of all you need to create a mesh with shape of a house. For example, it can be created online in SketchUp for free (registration is required).

Alternatively, the mesh can be created in any 3D editor which can produce .obj and .stl files, e.g., Blender.

A ready mesh is put .files/ folder.

file main.py:

import os

import uuid

import settings
from solarhouse.building import Building
from solarhouse.calculation import Calculation
import solarhouse.export as export


def main():
    calc = Calculation(
        tz=settings.TZ,
        geo=settings.GEO,
        building=Building(
            mesh_file=settings.PATH_FILE_OBJECT,
            geo=settings.GEO,
            wall_material=settings.WALL_MATERIAL,
            wall_thickness=settings.WALL_THICKNESS,
            start_temp_in=settings.TEMPERATURE_START,
            power_heat_inside=settings.POWER_HEAT_INSIDE,
            efficiency=settings.EFF,
            heat_accumulator={
                'volume': 0.032,
                'material': 'water',
            },
            windows={
                'area': 0.3,
                'therm_r': 5.0,
            },
            floor={
                'area': 1.0,
                'material': 'adobe',
                'thickness': 0.2,
                't_out': 4.0,
            },
        ),
                    )
    data_frame = calc.compute(date=22, month=12, year=2019, with_weather=False)
    calc_id = str(uuid.uuid4())
    output_dir = os.path.join(settings.PATH_OUTPUT, calc_id)
    os.makedirs(output_dir, exist_ok=True)
    csv_file_path = export.as_file(data_frame, 'csv', output_dir)
    export.as_html(data_frame, output_dir)

if __name__ == "__main__":
    main()

file settings.py:

import os
import pathlib


_this_dir = pathlib.Path(__file__).parent.absolute()

PATH_FILE_OBJECT = os.path.join(_this_dir, 'files/cube.obj')
TIME_TICK = 1    #1 hours
WALL_THICKNESS = 0.3
TEMPERATURE_START = 20  #celcium
POWER_HEAT_INSIDE = 0   #kWtt
MASS_INSIDE = 500   #kg
PATH_FILE_TEMPERATURE_OUTSIDE_FILE = os.path.join(_this_dir, 'files/temp_table.csv')
PATH_EXPORT_THERMO_RESULT_FILE = os.path.join(_this_dir, 'files/results.csv')
SPACE_POWER_ON_METER = 1000
WALL_MATERIAL = 'adobe'
EFF = 75        #in percents
EFF_ANG = 85.0
GEO = {
    'latitude': 54.841426,
    'longitude': 83.264479,
}
TZ = 'Asia/Novosibirsk'
COUNT_FACES_FOR_PARALLEL_CALC = 1000
PATH_OUTPUT = os.path.join(_this_dir, 'output')

All parameters of a house (mesh, thickness of wall, material of walls and etc.) sets in file settings.py

After that you can start calculation:

$ python3 main.py

As a result you a spreadsheet and a graph as two files in folder output/<calc_id>: data.csv and plot.html.

Author

Yaroslav Pisarev (yaricp@gmail.com).