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optim_results_analysis.py
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# -*- coding: utf-8 -*-
import pathlib
import pickle
import pandas as pd
import plotly.io as pio
from emhass.forecast import Forecast
from emhass.optimization import Optimization
from emhass.retrieve_hass import RetrieveHass
from emhass.utils import (
build_config,
build_params,
build_secrets,
get_days_list,
get_logger,
get_root,
get_yaml_parse,
)
pio.renderers.default = "browser"
pd.options.plotting.backend = "plotly"
# the root folder
root = pathlib.Path(str(get_root(__file__, num_parent=2)))
emhass_conf = {}
emhass_conf["data_path"] = root / "data/"
emhass_conf["root_path"] = root / "src/emhass/"
emhass_conf["docs_path"] = root / "docs/"
emhass_conf["config_path"] = root / "config.json"
emhass_conf["defaults_path"] = emhass_conf["root_path"] / "data/config_defaults.json"
emhass_conf["associations_path"] = emhass_conf["root_path"] / "data/associations.csv"
# create logger
logger, ch = get_logger(__name__, emhass_conf, save_to_file=False)
def get_forecast_optim_objects(
retrieve_hass_conf, optim_conf, plant_conf, params, get_data_from_file
):
fcst = Forecast(
retrieve_hass_conf,
optim_conf,
plant_conf,
params,
emhass_conf,
logger,
get_data_from_file=get_data_from_file,
)
df_weather = fcst.get_weather_forecast(method=optim_conf["weather_forecast_method"])
P_PV_forecast = fcst.get_power_from_weather(df_weather)
P_load_forecast = fcst.get_load_forecast(method=optim_conf["load_forecast_method"])
df_input_data_dayahead = pd.concat([P_PV_forecast, P_load_forecast], axis=1)
df_input_data_dayahead.columns = ["P_PV_forecast", "P_load_forecast"]
opt = Optimization(
retrieve_hass_conf,
optim_conf,
plant_conf,
fcst.var_load_cost,
fcst.var_prod_price,
"profit",
emhass_conf,
logger,
)
return fcst, P_PV_forecast, P_load_forecast, df_input_data_dayahead, opt
if __name__ == "__main__":
show_figures = False
save_figures = False
save_html = False
get_data_from_file = True
# Build params with default config and default secrets
config = build_config(emhass_conf, logger, emhass_conf["defaults_path"])
_, secrets = build_secrets(emhass_conf, logger, no_response=True)
params = build_params(emhass_conf, secrets, config, logger)
# if get_data_from_file:
# retrieve_hass_conf, optim_conf, plant_conf = get_yaml_parse({},logger)
# else:
retrieve_hass_conf, optim_conf, plant_conf = get_yaml_parse(params, logger)
retrieve_hass_conf, optim_conf, plant_conf = (
retrieve_hass_conf,
optim_conf,
plant_conf,
)
rh = RetrieveHass(
retrieve_hass_conf["hass_url"],
retrieve_hass_conf["long_lived_token"],
retrieve_hass_conf["optimization_time_step"],
retrieve_hass_conf["time_zone"],
params,
emhass_conf,
logger,
)
if get_data_from_file:
with open(
pathlib.Path(emhass_conf["data_path"] / "test_df_final.pkl"), "rb"
) as inp:
rh.df_final, days_list, var_list = pickle.load(inp)
retrieve_hass_conf["sensor_power_load_no_var_loads"] = str(var_list[0])
retrieve_hass_conf["sensor_power_photovoltaics"] = str(var_list[1])
retrieve_hass_conf["sensor_linear_interp"] = [
retrieve_hass_conf["sensor_power_photovoltaics"],
retrieve_hass_conf["sensor_power_load_no_var_loads"],
]
retrieve_hass_conf["sensor_replace_zero"] = [
retrieve_hass_conf["sensor_power_photovoltaics"]
]
else:
days_list = get_days_list(retrieve_hass_conf["historic_days_to_retrieve"])
var_list = [
retrieve_hass_conf["sensor_power_load_no_var_loads"],
retrieve_hass_conf["sensor_power_photovoltaics"],
]
rh.get_data(
days_list, var_list, minimal_response=False, significant_changes_only=False
)
rh.prepare_data(
retrieve_hass_conf["sensor_power_load_no_var_loads"],
load_negative=retrieve_hass_conf["load_negative"],
set_zero_min=retrieve_hass_conf["set_zero_min"],
var_replace_zero=retrieve_hass_conf["sensor_replace_zero"],
var_interp=retrieve_hass_conf["sensor_linear_interp"],
)
df_input_data = rh.df_final.copy()
fcst, P_PV_forecast, P_load_forecast, df_input_data_dayahead, opt = (
get_forecast_optim_objects(
retrieve_hass_conf, optim_conf, plant_conf, params, get_data_from_file
)
)
df_input_data = fcst.get_load_cost_forecast(df_input_data)
df_input_data = fcst.get_prod_price_forecast(df_input_data)
template = "presentation"
# Let's plot the input data
y_axis_title = "Power (W)"
fig_inputs1 = df_input_data[
[
retrieve_hass_conf["sensor_power_photovoltaics"],
str(retrieve_hass_conf["sensor_power_load_no_var_loads"] + "_positive"),
]
].plot()
fig_inputs1.layout.template = template
fig_inputs1.update_yaxes(title_text=y_axis_title)
fig_inputs1.update_xaxes(title_text="Time")
if show_figures:
fig_inputs1.show()
if save_figures:
fig_inputs1.write_image(
emhass_conf["docs_path"] / "images/inputs_power.svg",
width=1080,
height=0.8 * 1080,
)
fig_inputs_dah = df_input_data_dayahead.plot()
fig_inputs_dah.layout.template = template
fig_inputs_dah.update_yaxes(title_text=y_axis_title)
fig_inputs_dah.update_xaxes(title_text="Time")
if show_figures:
fig_inputs_dah.show()
if save_figures:
fig_inputs_dah.write_image(
emhass_conf["docs_path"] / "images/inputs_dayahead.svg",
width=1080,
height=0.8 * 1080,
)
# And then perform a dayahead optimization
df_input_data_dayahead = fcst.get_load_cost_forecast(df_input_data_dayahead)
df_input_data_dayahead = fcst.get_prod_price_forecast(df_input_data_dayahead)
optim_conf["treat_deferrable_load_as_semi_cont"] = [True, True]
optim_conf["set_deferrable_load_single_constant"] = [True, True]
unit_load_cost = df_input_data[opt.var_load_cost].values
unit_prod_price = df_input_data[opt.var_prod_price].values
opt_res_dah = opt.perform_optimization(
df_input_data_dayahead,
P_PV_forecast.values.ravel(),
P_load_forecast.values.ravel(),
unit_load_cost,
unit_prod_price,
debug=True,
)
# opt_res_dah = opt.perform_dayahead_forecast_optim(df_input_data_dayahead, P_PV_forecast, P_load_forecast)
opt_res_dah["P_PV"] = df_input_data_dayahead[["P_PV_forecast"]]
fig_res_dah = opt_res_dah[
[
"P_deferrable0",
"P_deferrable1",
"P_grid",
"P_PV",
]
].plot() # 'P_def_start_0', 'P_def_start_1', 'P_def_bin2_0', 'P_def_bin2_1'
fig_res_dah.layout.template = template
fig_res_dah.update_yaxes(title_text=y_axis_title)
fig_res_dah.update_xaxes(title_text="Time")
# if show_figures:
fig_res_dah.show()
if save_figures:
fig_res_dah.write_image(
emhass_conf["docs_path"]
/ "images/optim_results_PV_defLoads_dayaheadOptim.svg",
width=1080,
height=0.8 * 1080,
)
print(
"System with: PV, two deferrable loads, dayahead optimization, profit >> total cost function sum: "
+ str(opt_res_dah["cost_profit"].sum())
)
print(opt_res_dah)
if save_html:
opt_res_dah.to_html("opt_res_dah.html")