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CACER-simulator/Load_profile_emulator_with_tutorial.ipynb
AndreStork a422acf477 Integrazione codebase RSE
2025-06-16 09:31:15 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **Import all functions and libraries**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from src.Functions_Load_Emulator_and_DSM import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **General input**\n",
"\n",
"Before to run the emulation of the domestic load profiles is necessary to specify some useful parameters. In particular:\n",
"\n",
"- Start day for simulation;\n",
"\n",
"- End day for simulation;\n",
"\n",
"- Total number of users to emulate;"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"start_day = date(2025, 1, 1) # start day for simulation\n",
"end_day = date(2025, 12, 31) # end day for simulation\n",
"\n",
"num_days = (end_day - start_day).days + 1 # total number of days"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"num_user = 20 # number of emulated users"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **Usage probability of all appliances**\n",
"\n",
"To emulate the domestic load profile are used the following usage probability on quarterly level."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
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"### **Load profile of all appliances**\n",
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"text": "Load profile of the different appliances"
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **Flag**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"flag_daily_activation = True # if false we dont use a daily usage activation for some specified appliances\n",
"flag_multi_use = False # if true we activate the possibility to have multiple activations for the selected appliances during the day"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **1. Create calendar**\n",
"\n",
"Creating calendar for load profiles."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[34mGenerate calendar:\n",
"\u001b[0m\n",
"**** Calendar successfully created! *****\n"
]
}
],
"source": [
"calendar = generate_calendar_modified(start_day, end_day)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **2. Create start time dictionary**\n",
"\n",
"\n",
"A dictionary is created with the activation time (number of the quarter-hour, from 0 to 95) for each day of the simulation and for each user.\n",
"\n",
"**start_time_dict**: \n",
"\n",
"- user_0:\n",
" - **df_start_time_user_0**\n",
"\n",
"- user_1:\n",
" - **df_start_time_user_0**\n",
"\n",
"- ...\n",
"\n",
" \n",
"\n",
"**df_start_time_user:**\n",
"\n",
"| index (type int) | appliance_0 (type int) | appliance_1 (type int) | ... | \n",
"| :--- | :----: | :----: | ---: |\n",
"| day_0 | start_time_day_0 | start_time_day_0 | ... | \n",
"| day_1 | start_time_day_1 | start_time_day_1 | ... | \n",
"| ... | ... | ... | ... | "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(start_time_dict_1, start_time_dict_2, start_time_dict_3) = create_all_user_appliance_start_time_mod(num_user, num_days, calendar, flag_daily_activation, flag_multi_use)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **3. Create all_user load profile dictionary**\n",
"\n",
"\n",
"A dictionary is created with the daily load profile for each user.\n",
"\n",
"**all_user_load_profile_dict**: \n",
"\n",
"- user_0:\n",
" - **df_load_profile_user_0**:\n",
"- user_1:\n",
" - **df_load_profile_user_1**:.\n",
"- ...\n",
"\n",
"**df_load_profile_user:**\n",
"\n",
"| index (type int) | day_0 (type float) | day_1 (type float) | ... | \n",
"| :--- | :----: | :----:| ---:|\n",
"| timestep_0 | kWh_timetep_0 | kWh_timetep_0 | ... | \n",
"| timestep_1 | kWh_timetep_1 | kWh_timetep_1 | ... |\n",
"| ... | ... | ... | ... | "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"emulated_users_list = create_emulated_users_list(num_user)\n",
"all_user_load_profile_dict = create_all_user_load_profile(start_time_dict_1, start_time_dict_2, start_time_dict_3, emulated_users_list, num_days, flag_DSM = False, flag_all_appliance = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **4. Create all user file dataframe**\n",
"\n",
"\n",
"a dataframe is reconstructed with the load profiles of each user.\n",
"\n",
"**all_user_df:**\n",
"\n",
"| index (type int) | user_0 (type float) | user_1 (type float) | ... | \n",
"| :--- | :----: | :----: | ---:|\n",
"| timestep_0 | kWh_timetep_0 | kWh_timetep_0 | ... | \n",
"| timestep_1 | kWh_timetep_1 | kWh_timetep_1 | ... |\n",
"| ... | ... | ... | ... | "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_user_df = create_single_user_load_profile_df(all_user_load_profile_dict, calendar, flag_DSM = False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **Output: csv file with emulated load profile**\n",
"\n",
"The csv file with emulated load profile can be found at the following path:\n",
"\n",
"- emulated_load_profile.csv [[link csv](files/energy/input/emulated_load_profile.csv)]\n",
"\n",
"📂 *files/energy/input/emulated_load_profile.csv*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **6. Create all users appliance load profile dictionary**\n",
"\n",
"\n",
"A dictionary is created with all appliance daily load profile for each user.\n",
"\n",
"**all_user_appliance_load_profile_dict**: \n",
"\n",
"- user_0:\n",
" - appliance_0:\n",
" - **df_appliance_load_profile_0**\n",
" - appliance_1:\n",
" - **df_appliance_load_profile_1**\n",
" - ...\n",
"\n",
"- user_1:\n",
" - appliance_0:\n",
" - **df_appliance_load_profile_0**\n",
" - appliance_1:\n",
" - **df_appliance_load_profile_1**\n",
" - ...\n",
"- ...\n",
"\n",
" \n",
"\n",
"**df_appliace_load_profile:**\n",
"\n",
"| index (type int) | day_0 (type float) | day_1 (type float) | ... | \n",
"| :--- | :----: | :----:| ---:|\n",
"| timestep_0 | kWh_timetep_0 | kWh_timetep_0 | ... | \n",
"| timestep_1 | kWh_timetep_1 | kWh_timetep_1 | ... |\n",
"| ... | ... | ... | ... | "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_user_appliance_load_profile_dict = create_all_user_appliance_load_profile(start_time_dict_1, emulated_users_list, num_days, flag_DSM = False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **7. Plot**\n",
"\n",
"Before plotting the results, select a user for the visualization."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"user = 'u_001' # user to be plotted"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### **7.1 Plot load profile**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# plot load profile for all days of the years for the selcted user\n",
"plot_all_day_load_profile(all_user_load_profile_dict, user)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# plot average users load profiles\n",
"plot_average_users_load_profile(all_user_load_profile_dict, plot_type = 'scatter plot') # plot_type : [scatter plot; barchart]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# comparison of average users load profiles with arera load profile for every region in a specified month \n",
"\n",
"month = 1 # month to be plotted\n",
"\n",
"comparison_average_load_profile_arera_profile(all_user_load_profile_dict, month)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### **7.2 Plot single user appliance load profile**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# plot of appliance load profile for a single user for a selected day\n",
"\n",
"day = 0\n",
"\n",
"plot_single_user_appliance_load_profile(all_user_appliance_load_profile_dict, id_user = user, day = day)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### **7.3 Plot single appliance load profile in each days for a single user**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# plot of all day appliance load profile for a single user\n",
"\n",
"appliance = 'washing_machine'\n",
"\n",
"plot_all_day_appliance_load_profile(user, appliance)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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