{ "cells": [ { "metadata": {}, "cell_type": "markdown", "source": [ "# Example\n", "\n", "There are some steps on how to use `energy_manager` in any project:" ] }, { "metadata": {}, "cell_type": "markdown", "source": "## 1. Installation" }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:15.233070Z", "start_time": "2024-12-27T20:32:15.226908Z" } }, "cell_type": "code", "source": "#%pip install energy_manager\n", "outputs": [], "execution_count": 1 }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:15.269731Z", "start_time": "2024-12-27T20:32:15.265666Z" } }, "cell_type": "code", "source": "", "outputs": [], "execution_count": null }, { "metadata": {}, "cell_type": "markdown", "source": "## 2. Imports" }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:16.029247Z", "start_time": "2024-12-27T20:32:15.578665Z" } }, "cell_type": "code", "source": [ "import os\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from energy_manager.classes.energy_manager import EnergyManager\n" ], "outputs": [], "execution_count": 2 }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:16.045953Z", "start_time": "2024-12-27T20:32:16.037731Z" } }, "cell_type": "code", "source": "", "outputs": [], "execution_count": null }, { "metadata": {}, "cell_type": "markdown", "source": "## 3. Explore the class EnergyManager" }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:16.087428Z", "start_time": "2024-12-27T20:32:16.081300Z" } }, "cell_type": "code", "source": "help(EnergyManager)\n", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class EnergyManager in module energy_manager.classes.energy_manager:\n", "\n", "class EnergyManager(builtins.object)\n", " | EnergyManager(city_name: str, dpe_usage: float, temperature: float, openweathermap_api_key: str, insulation_factor: float = 1.0)\n", " |\n", " | Manages energy consumption and calculates expenses based on provided parameters.\n", " |\n", " | This class is designed to handle energy management by leveraging data like\n", " | city name, average energy usage, temperature, and insulation factor. It interacts\n", " | with external APIs to compute daily energy expenses, taking into consideration\n", " | climatic and usage factors.\n", " |\n", " | Attributes:\n", " | _city_name (str): The name of the city for which energy expenses are calculated.\n", " | _dpe_usage (float): The average energy usage (DPE - Diagnostic de Performance\n", " | Énergétique) of the property in kWh/m².year.\n", " | _temperature (float): The current temperature in the city, used for expense calculations.\n", " | _openweathermap_api_key (str): API key for accessing OpenWeatherMap's forecast data.\n", " | _insulation_factor (float): Adjustment factor accounting for the property's insulation\n", " | efficiency. Defaults to 1.0, corresponding to standard insulation.\n", " |\n", " | Methods defined here:\n", " |\n", " | __init__(self, city_name: str, dpe_usage: float, temperature: float, openweathermap_api_key: str, insulation_factor: float = 1.0)\n", " | Initialize self. See help(type(self)) for accurate signature.\n", " |\n", " | get_daily_expenses(self) -> pandas.core.frame.DataFrame\n", " |\n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " |\n", " | __dict__\n", " | dictionary for instance variables\n", " |\n", " | __weakref__\n", " | list of weak references to the object\n", "\n" ] } ], "execution_count": 3 }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:16.107813Z", "start_time": "2024-12-27T20:32:16.103689Z" } }, "cell_type": "code", "source": "", "outputs": [], "execution_count": null }, { "metadata": {}, "cell_type": "markdown", "source": "## 2. Define mandatory parameters" }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:16.136550Z", "start_time": "2024-12-27T20:32:16.123540Z" } }, "cell_type": "code", "source": [ "user_city_name = \"Nangis\"\n", "openweathermap_api_key = os.getenv(\"OPEN_WEATHER_API_KEY\") # if you set your openweathermap api key as an environment variable\n", "user_temperature = 19.5\n", "user_dpe_usage = 1.5\n" ], "outputs": [], "execution_count": 4 }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:16.172528Z", "start_time": "2024-12-27T20:32:16.168970Z" } }, "cell_type": "code", "source": "", "outputs": [], "execution_count": null }, { "metadata": {}, "cell_type": "markdown", "source": "## 3. Define optional parameters if you want" }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:16.194914Z", "start_time": "2024-12-27T20:32:16.184819Z" } }, "cell_type": "code", "source": "user_insulation_factor = 1.5 # 1.0 is the default value set in the EnergyManager class\n", "outputs": [], "execution_count": 5 }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:16.230192Z", "start_time": "2024-12-27T20:32:16.226805Z" } }, "cell_type": "code", "source": "", "outputs": [], "execution_count": null }, { "metadata": {}, "cell_type": "markdown", "source": "## 4. Instantiate the class EnergyManager with your set of parameters" }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:19.837095Z", "start_time": "2024-12-27T20:32:16.244209Z" } }, "cell_type": "code", "source": [ "my_energy_manager = EnergyManager(\n", " city_name=user_city_name,\n", " openweathermap_api_key=openweathermap_api_key,\n", " temperature=user_temperature,\n", " dpe_usage=user_dpe_usage,\n", " insulation_factor=user_insulation_factor,\n", ")\n", "\n", "daily_expenses_df = my_energy_manager.get_daily_expenses()\n" ], "outputs": [], "execution_count": 6 }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:19.874677Z", "start_time": "2024-12-27T20:32:19.869156Z" } }, "cell_type": "code", "source": "", "outputs": [], "execution_count": null }, { "metadata": {}, "cell_type": "markdown", "source": "## 5. Explore results of daily_expenses_df and do any manipulation you want" }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:19.909185Z", "start_time": "2024-12-27T20:32:19.897677Z" } }, "cell_type": "code", "source": "daily_expenses_df.dtypes\n", "outputs": [ { "data": { "text/plain": [ "date_time datetime64[ns]\n", "weather_description object\n", "option_0 float64\n", "option_1 float64\n", "option_2 float64\n", "option_3 float64\n", "option_4 float64\n", "building_type object\n", "dpe_class object\n", "dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 7 }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:20.016199Z", "start_time": "2024-12-27T20:32:19.995053Z" } }, "cell_type": "code", "source": "daily_expenses_df[[\"date_time\", \"weather_description\", \"building_type\", \"dpe_class\", \"option_0\", \"option_3\"]].head()\n", "outputs": [ { "data": { "text/plain": [ " date_time weather_description building_type dpe_class option_0 \\\n", "0 2024-12-27 00:00:00 clear sky Appartement A 3.755618 \n", "1 2024-12-27 01:00:00 clear sky Appartement A 3.866196 \n", "2 2024-12-27 02:00:00 few clouds Appartement A 3.689272 \n", "3 2024-12-27 03:00:00 few clouds Appartement A 3.723450 \n", "4 2024-12-27 04:00:00 scattered clouds Appartement A 3.761650 \n", "\n", " option_3 \n", "0 3.086891 \n", "1 3.177779 \n", "2 3.032358 \n", "3 3.060451 \n", "4 3.091849 " ], "text/html": [ "
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date_timeweather_descriptionbuilding_typedpe_classoption_0option_3
02024-12-27 00:00:00clear skyAppartementA3.7556183.086891
12024-12-27 01:00:00clear skyAppartementA3.8661963.177779
22024-12-27 02:00:00few cloudsAppartementA3.6892723.032358
32024-12-27 03:00:00few cloudsAppartementA3.7234503.060451
42024-12-27 04:00:00scattered cloudsAppartementA3.7616503.091849
\n", "
" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 8 }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:20.088433Z", "start_time": "2024-12-27T20:32:20.086128Z" } }, "cell_type": "code", "source": "", "outputs": [], "execution_count": null }, { "metadata": {}, "cell_type": "markdown", "source": "## 6. Results meaning" }, { "metadata": {}, "cell_type": "markdown", "source": "Columns option_0, option_1, ..., option_4 values are energy costs in euros per square meter." }, { "metadata": {}, "cell_type": "markdown", "source": [ "Settings of these 5 options provided by ENEDIS for EDF consumers are such as :\n", "\n", " - option_0 : energy price is the same at any hour of the day with a value of 25.16 euros/kwh;\n", " - option_1, ..., option_4 : energy price during peak hour is 27 euros/Kwh and is 20.68 euros/Kwh during for off-peak hours.\n" ] }, { "metadata": {}, "cell_type": "markdown", "source": "The first row of df_daily_expenses means that for the day 2024-12-26 from 00:00:00 to 01:00:00, if your house is an \"Appartement\" and has a DPE (diagnostic de performance energetique) of class A, your estimated energy cost for the option_0 is approximately less than 3 euros per square meter for the desired temperature of 19.5 degrees Celsius." }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:20.146274Z", "start_time": "2024-12-27T20:32:20.141440Z" } }, "cell_type": "code", "source": [ "mask = (daily_expenses_df[\"building_type\"] == \"Appartement\") & (daily_expenses_df[\"dpe_class\"] == \"A\")\n", "new_df = daily_expenses_df[mask]\n" ], "outputs": [], "execution_count": 9 }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:20.238510Z", "start_time": "2024-12-27T20:32:20.228031Z" } }, "cell_type": "code", "source": "new_df[[\"date_time\", \"weather_description\", \"dpe_class\", \"option_0\", \"option_3\"]].head(n=10)\n", "outputs": [ { "data": { "text/plain": [ " date_time weather_description dpe_class option_0 option_3\n", "0 2024-12-27 00:00:00 clear sky A 3.755618 3.086891\n", "1 2024-12-27 01:00:00 clear sky A 3.866196 3.177779\n", "2 2024-12-27 02:00:00 few clouds A 3.689272 3.032358\n", "3 2024-12-27 03:00:00 few clouds A 3.723450 3.060451\n", "4 2024-12-27 04:00:00 scattered clouds A 3.761650 3.091849\n", "5 2024-12-27 05:00:00 scattered clouds A 4.091372 3.362861\n", "6 2024-12-27 06:00:00 clear sky A 3.789797 3.114984\n", "7 2024-12-27 07:00:00 few clouds A 3.781755 3.108374\n", "8 2024-12-27 08:00:00 few clouds A 3.755618 4.030274\n", "9 2024-12-27 09:00:00 few clouds A 3.492242 3.747637" ], "text/html": [ "
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date_timeweather_descriptiondpe_classoption_0option_3
02024-12-27 00:00:00clear skyA3.7556183.086891
12024-12-27 01:00:00clear skyA3.8661963.177779
22024-12-27 02:00:00few cloudsA3.6892723.032358
32024-12-27 03:00:00few cloudsA3.7234503.060451
42024-12-27 04:00:00scattered cloudsA3.7616503.091849
52024-12-27 05:00:00scattered cloudsA4.0913723.362861
62024-12-27 06:00:00clear skyA3.7897973.114984
72024-12-27 07:00:00few cloudsA3.7817553.108374
82024-12-27 08:00:00few cloudsA3.7556184.030274
92024-12-27 09:00:00few cloudsA3.4922423.747637
\n", "
" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 10 }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:20.697225Z", "start_time": "2024-12-27T20:32:20.334079Z" } }, "cell_type": "code", "source": [ "plt.style.use('ggplot')\n", "fig, ax = plt.subplots(figsize=(12, 7), dpi=100)\n", "colors = sns.color_palette(\"husl\", 5)\n", "\n", "for i, option in enumerate([\"option_0\", \"option_1\", \"option_2\", \"option_3\", \"option_4\"]):\n", " ax.step(new_df.date_time.dt.hour, new_df[option], where=\"post\", label=option,\n", " linewidth=2, color=colors[i])\n", "\n", "ax.set_title(\"Energy Cost Over Time in Euros Per Square Meter\", fontsize=16, fontweight='bold', pad=20)\n", "ax.set_xlabel(\"Hour of the Day\", fontsize=12, labelpad=10)\n", "ax.set_ylabel(\"Energy Cost (€/m²)\", fontsize=12, labelpad=10)\n", "ax.set_xticks(range(0, 24, 2))\n", "ax.set_xlim(0, 23)\n", "ax.tick_params(axis='both', which='major', labelsize=10)\n", "ax.legend(title=\"Options\", title_fontsize='12', fontsize='10', loc='upper left', bbox_to_anchor=(1, 1))\n", "ax.grid(True, linestyle='--', alpha=0.7)\n", "\n", "plt.tight_layout()\n", "plt.show()\n" ], "outputs": [ { "data": { "text/plain": [ "
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}, "metadata": {}, "output_type": "display_data" } ], "execution_count": 11 }, { "metadata": {}, "cell_type": "markdown", "source": "##### _This could be an insight on how to choose your EDF energy contract based on your consumption hours._" }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-27T20:32:20.851314Z", "start_time": "2024-12-27T20:32:20.848202Z" } }, "cell_type": "code", "source": "", "outputs": [], "execution_count": null } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 4 }