example_acm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Python code translates the R code (the example_acm.R)  The code creates a scatter plot that displays an adaptive comfort model based on the ASHRAE Global Thermal Comfort Database II. The plot shows the relationship between outdoor temperature and neutral temperature for different building cooling types: Air Conditioned, Mixed Mode, and Naturally Ventilated. </br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as smf\n",
    "from matplotlib.ticker import FormatStrFormatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ruijisun/opt/anaconda3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3444: DtypeWarning: Columns (5,35,36) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "# Read metadata from Github\n",
    "url_meta = \"https://github.com/CenterForTheBuiltEnvironment/ashrae-db-II/raw/master/v2.1.0/db_metadata.csv\"\n",
    "df_meta = pd.read_csv(url_meta)\n",
    "\n",
    "# Read database from Github\n",
    "url_measurements = \"https://github.com/CenterForTheBuiltEnvironment/ashrae-db-II/raw/master/v2.1.0/db_measurements_v2.1.0.csv.gz\"\n",
    "df_measurements = pd.read_csv(url_measurements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "         index  record_id  building_id             timestamp   season  \\\n",
       "0            0          1            1  1995-05-18T00:00:00Z   winter   \n",
       "1            1          2            1  1995-05-18T00:00:00Z   winter   \n",
       "2            2          3            1  1995-05-18T00:00:00Z   winter   \n",
       "3            3          4            1  1995-05-18T00:00:00Z   winter   \n",
       "4            4          5            1  1995-05-18T00:00:00Z   winter   \n",
       "...        ...        ...          ...                   ...      ...   \n",
       "109028  110065     110066          809  2014-06-02T00:00:00Z  hot/wet   \n",
       "109029  110066     110067          809  2014-06-02T00:00:00Z  hot/wet   \n",
       "109030  110067     110068          809  2014-06-02T00:00:00Z  hot/wet   \n",
       "109031  110068     110069          809  2014-06-02T00:00:00Z  hot/wet   \n",
       "109032  110069     110070          809  2014-06-02T00:00:00Z  hot/wet   \n",
       "\n",
       "       subject_id  age  gender  ht  wt  ...  rh_out  t_out_monthly  t_out_isd  \\\n",
       "0            10.0  NaN  female NaN NaN  ...    99.1            NaN  16.295833   \n",
       "1            14.0  NaN  female NaN NaN  ...    99.1            NaN  16.295833   \n",
       "2            15.0  NaN  female NaN NaN  ...    99.1            NaN  16.295833   \n",
       "3            17.0  NaN  female NaN NaN  ...    99.1            NaN  16.295833   \n",
       "4            21.0  NaN    male NaN NaN  ...    99.1            NaN  16.295833   \n",
       "...           ...  ...     ...  ..  ..  ...     ...            ...        ...   \n",
       "109028        NaN  NaN     NaN NaN NaN  ...     NaN           28.8  28.316667   \n",
       "109029        NaN  NaN     NaN NaN NaN  ...     NaN           28.8  28.316667   \n",
       "109030        NaN  NaN     NaN NaN NaN  ...     NaN           28.8  28.316667   \n",
       "109031        NaN  NaN     NaN NaN NaN  ...     NaN           28.8  28.316667   \n",
       "109032        NaN  NaN     NaN NaN NaN  ...     NaN           28.8  28.316667   \n",
       "\n",
       "        rh_out_isd   set   pmv   ppd  pmv_ce  ppd_ce  t_mot_isd  \n",
       "0        86.837514  28.1  0.50  10.2    0.38     8.0       17.4  \n",
       "1        86.837514  27.2  0.40   8.4    0.40     8.4       17.4  \n",
       "2        86.837514  24.8 -0.07   5.1   -0.07     5.1       17.4  \n",
       "3        86.837514  25.5  0.31   7.0    0.14     5.4       17.4  \n",
       "4        86.837514  26.0  0.05   5.0   -0.06     5.1       17.4  \n",
       "...            ...   ...   ...   ...     ...     ...        ...  \n",
       "109028   82.059739   NaN   NaN   NaN     NaN     NaN       28.0  \n",
       "109029   82.059739   NaN   NaN   NaN     NaN     NaN       28.0  \n",
       "109030   82.059739   NaN   NaN   NaN     NaN     NaN       28.0  \n",
       "109031   82.059739   NaN   NaN   NaN     NaN     NaN       28.0  \n",
       "109032   82.059739   NaN   NaN   NaN     NaN     NaN       28.0  \n",
       "\n",
       "[109033 rows x 53 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
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    }
   ],
   "source": [
    "df_measurements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To conduct adaptive thermal model analysis, we need concurrent observations of indoor air temperature, thermal sensation vote, and outdoor mean monthly temperature. However, here here the orignial R code (Python version) also requires relative humidity data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of rows that have required data for acm: 74701\n",
      "number of buildings that have required data for acm: 732\n",
      "number of office buildings that have required data for acm: 304\n",
      "number of rows (office) that have required data for acm: 49584\n"
     ]
    }
   ],
   "source": [
    "# remove nan values in Ta, thermal_sensation, and t_out_isd or t_out\n",
    "df_acm = df_measurements.loc[(~df_measurements['ta'].isna()) &\n",
    "                              (~df_measurements['thermal_sensation'].isna()) &\n",
    "                              (~df_measurements['rh'].isna()) &\n",
    "                              (~(df_measurements['t_out_isd'].isna()) | ~(df_measurements['t_out'].isna()))].copy()\n",
    "print('number of rows that have required data for acm:', len(df_acm))\n",
    "print('number of buildings that have required data for acm:', len(df_acm.building_id.unique()))\n",
    "\n",
    "# Fill the missing values in the outdoor temperature column\n",
    "df_acm.loc[:, 't_out_combined'] = df_acm.loc[:, 't_out_isd'].fillna(df_acm.loc[:, 't_out'])\n",
    "\n",
    "# Remove original temperature columns\n",
    "df_acm = df_acm.drop(columns=['t_out_isd', 't_out'])\n",
    "\n",
    "# Merge metadata and databased by office buildings\n",
    "df_acm = df_acm.merge(df_meta[['building_id', 'region', 'building_type', 'cooling_type', 'records']], on='building_id', how='left')\n",
    "df_acm = df_acm[df_acm['building_type'] == 'office']\n",
    "df_acm = df_acm.drop(columns=['building_type'])\n",
    "print('number of office buildings that have required data for acm:', len(df_acm.building_id.unique()))\n",
    "print('number of rows (office) that have required data for acm:', len(df_acm))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The orginal R code evalue the p-value of intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <td>air conditioned</td>\n",
       "      <td>americas</td>\n",
       "      <td>0.583480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>23.642083</td>\n",
       "      <td>137</td>\n",
       "      <td>mixed mode</td>\n",
       "      <td>oceania</td>\n",
       "      <td>19.284220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>22.071788</td>\n",
       "      <td>128</td>\n",
       "      <td>air conditioned</td>\n",
       "      <td>americas</td>\n",
       "      <td>9.048210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>763</td>\n",
       "      <td>20.837842</td>\n",
       "      <td>134</td>\n",
       "      <td>naturally ventilated</td>\n",
       "      <td>europe</td>\n",
       "      <td>9.983256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>771</td>\n",
       "      <td>23.439683</td>\n",
       "      <td>161</td>\n",
       "      <td>air conditioned</td>\n",
       "      <td>europe</td>\n",
       "      <td>15.119837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>772</td>\n",
       "      <td>23.082808</td>\n",
       "      <td>94</td>\n",
       "      <td>air conditioned</td>\n",
       "      <td>europe</td>\n",
       "      <td>2.571260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>779</td>\n",
       "      <td>28.210619</td>\n",
       "      <td>118</td>\n",
       "      <td>air conditioned</td>\n",
       "      <td>asia</td>\n",
       "      <td>28.855652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>786</td>\n",
       "      <td>24.368883</td>\n",
       "      <td>83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>asia</td>\n",
       "      <td>-19.109639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>301 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     building_id  neutral_temp  records          cooling_type    region  \\\n",
       "0              1     22.585738      170            mixed mode   oceania   \n",
       "1              2     22.058339       83       air conditioned   oceania   \n",
       "2              3     23.142187       85       air conditioned  americas   \n",
       "3              4     23.642083      137            mixed mode   oceania   \n",
       "4              5     22.071788      128       air conditioned  americas   \n",
       "..           ...           ...      ...                   ...       ...   \n",
       "299          763     20.837842      134  naturally ventilated    europe   \n",
       "300          771     23.439683      161       air conditioned    europe   \n",
       "301          772     23.082808       94       air conditioned    europe   \n",
       "302          779     28.210619      118       air conditioned      asia   \n",
       "303          786     24.368883       83                   NaN      asia   \n",
       "\n",
       "     t_out_mean  \n",
       "0     15.296857  \n",
       "1     13.995833  \n",
       "2      0.583480  \n",
       "3     19.284220  \n",
       "4      9.048210  \n",
       "..          ...  \n",
       "299    9.983256  \n",
       "300   15.119837  \n",
       "301    2.571260  \n",
       "302   28.855652  \n",
       "303  -19.109639  \n",
       "\n",
       "[301 rows x 6 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the neutral temperature for each building\n",
    "def run_lm(bldg):\n",
    "    try:\n",
    "        lm_result = smf.ols(formula='ta ~ thermal_sensation', data=bldg).fit()\n",
    "        # check whether the slope is significant\n",
    "        if lm_result.pvalues['Intercept'] < 0.05:\n",
    "            return lm_result.params['Intercept']\n",
    "        else:\n",
    "            return np.nan\n",
    "    except (ValueError, TypeError):\n",
    "        return np.nan\n",
    "\n",
    "df_models = df_acm.groupby('building_id').apply(run_lm).reset_index()\n",
    "df_models.columns = ['building_id', 'neutral_temp']\n",
    "df_models = df_models.merge(df_meta[['building_id', 'records', 'cooling_type', 'region']], on='building_id', how='left')\n",
    "df_models['t_out_mean'] = df_acm.groupby('building_id')['t_out_combined'].mean().values\n",
    "# get rid of all nan values in the neutral_temp column\n",
    "df_models = df_models[~df_models['neutral_temp'].isna()]\n",
    "df_models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 711.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "# Assuming filtered_df is already loaded and filtered\n",
    "\n",
    "# Define a custom function to draw the weighted regression line\n",
    "def weighted_regression_line(x, model):\n",
    "    return model.params[0] + model.params[1] * x\n",
    "\n",
    "colors = {\"Air Conditioned\": \"#000000\", \"Mixed Mode\": \"#E69F00\", \"Naturally Ventilated\": \"#56B4E9\"}\n",
    "\n",
    "lm = sns.lmplot(data=filtered_df, x='t_out_mean', y='neutral_temp', hue='cooling_type', \n",
    "                palette=colors, scatter_kws={'alpha': 0.25}, \n",
    "                height=5, aspect=1.6, fit_reg=False)\n",
    "\n",
    "# Add point_size directly to the scatterplot\n",
    "sns.scatterplot(data=filtered_df, x='t_out_mean', y='neutral_temp', hue='cooling_type', \n",
    "                size='point_size', sizes=(10, 200), legend=False, \n",
    "                palette=colors, alpha=0.25, ax=lm.ax)\n",
    "\n",
    "# Fit weighted linear regression and plot the lines for each cooling_type\n",
    "for cooling_type, color in colors.items():\n",
    "    sub_df = filtered_df[filtered_df['cooling_type'] == cooling_type]\n",
    "    wlm = smf.wls(formula='neutral_temp ~ t_out_mean', data=sub_df, weights=sub_df['point_size']).fit()\n",
    "    x_values = np.linspace(sub_df['t_out_mean'].min(), sub_df['t_out_mean'].max(), 100)\n",
    "    y_values = weighted_regression_line(x_values, wlm)\n",
    "    lm.ax.plot(x_values, y_values, color=color, linewidth=2)\n",
    "\n",
    "lm.ax.set_xlim(8, 32)\n",
    "lm.ax.set_ylim(17, 33)\n",
    "lm.ax.set_xlabel('\\nOutdoor Temperature')\n",
    "lm.ax.set_ylabel('Neutral Temperature\\n')\n",
    "lm.ax.xaxis.set_major_formatter(FormatStrFormatter('%d°C'))\n",
    "lm.ax.yaxis.set_major_formatter(FormatStrFormatter('%d°C'))\n",
    "\n",
    "# Annotations\n",
    "lm.ax.annotate(\"Lower 80%\", xy=(33.7, 24.5), xycoords='data', fontsize=10, color='grey', fontstyle='italic')\n",
    "lm.ax.annotate(\"Lower 90%\", xy=(33.7, 25.5), xycoords='data', fontsize=10, color='grey', fontstyle='italic')\n",
    "lm.ax.annotate(\"Upper 90%\", xy=(33.7, 28.0), xycoords='data', fontsize=10, color='grey', fontstyle='italic')\n",
    "lm.ax.annotate(\"Upper 80%\", xy=(33.7, 30.5), xycoords='data', fontsize=10, color='grey', fontstyle='italic')\n",
    "\n",
    "\n",
    "plt.title('Adaptive Comfort Model', fontsize=16)\n",
    "plt.suptitle('Example analysis of adaptive thermal comfort using the ASHRAE Global Thermal Comfort Database II', fontsize=12, fontstyle='italic', y=0.95)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regression results for natural ventilated buildings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           neutral_temp   R-squared:                       0.998\n",
      "Model:                            OLS   Adj. R-squared:                  0.998\n",
      "Method:                 Least Squares   F-statistic:                 4.675e+04\n",
      "Date:                Thu, 04 May 2023   Prob (F-statistic):          2.73e-118\n",
      "Time:                        11:32:21   Log-Likelihood:                -204.03\n",
      "No. Observations:                  87   AIC:                             412.1\n",
      "Df Residuals:                      85   BIC:                             417.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     15.8851      0.820     19.380      0.000      14.255      17.515\n",
      "t_out_mean     0.4446      0.037     11.902      0.000       0.370       0.519\n",
      "==============================================================================\n",
      "Omnibus:                       24.237   Durbin-Watson:                   1.162\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               36.336\n",
      "Skew:                          -1.201   Prob(JB):                     1.29e-08\n",
      "Kurtosis:                       5.062   Cond. No.                         65.8\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Filter the data for naturally ventilated buildings\n",
    "naturally_ventilated = df_models[df_models['cooling_type'] == 'naturally ventilated']\n",
    "\n",
    "# Run weighted linear regression between neutral_temp and t_out_mean\n",
    "lm_w_nv = smf.ols(formula='neutral_temp ~ t_out_mean', weights=naturally_ventilated.records, data=naturally_ventilated).fit()\n",
    "\n",
    "# Print the summary of the linear regression\n",
    "print(lm_w_nv.summary())\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regression results for mixed mode buildings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           neutral_temp   R-squared:                       0.999\n",
      "Model:                            OLS   Adj. R-squared:                  0.999\n",
      "Method:                 Least Squares   F-statistic:                 2.935e+04\n",
      "Date:                Thu, 04 May 2023   Prob (F-statistic):           3.84e-45\n",
      "Time:                        11:32:11   Log-Likelihood:                -54.866\n",
      "No. Observations:                  31   AIC:                             113.7\n",
      "Df Residuals:                      29   BIC:                             116.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     21.2096      0.700     30.292      0.000      19.778      22.642\n",
      "t_out_mean     0.1672      0.033      5.132      0.000       0.101       0.234\n",
      "==============================================================================\n",
      "Omnibus:                        5.021   Durbin-Watson:                   1.608\n",
      "Prob(Omnibus):                  0.081   Jarque-Bera (JB):                3.496\n",
      "Skew:                          -0.772   Prob(JB):                        0.174\n",
      "Kurtosis:                       3.567   Cond. No.                         57.2\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Filter the data for mix mode buildings\n",
    "mixed_mode = df_models[df_models['cooling_type'] == 'mixed mode']\n",
    "\n",
    "# Run linear regression between neutral_temp and t_out_mean\n",
    "lm_w_mm = smf.ols(formula='neutral_temp ~ t_out_mean', weights=mixed_mode.records, data=mixed_mode).fit()\n",
    "\n",
    "# Print the summary of the linear regression\n",
    "print(lm_w_mm.summary())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regression results for air_conditioned buildings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           neutral_temp   R-squared:                       0.984\n",
      "Model:                            OLS   Adj. R-squared:                  0.984\n",
      "Method:                 Least Squares   F-statistic:                 1.117e+04\n",
      "Date:                Thu, 04 May 2023   Prob (F-statistic):          6.22e-164\n",
      "Time:                        11:31:54   Log-Likelihood:                -362.29\n",
      "No. Observations:                 182   AIC:                             728.6\n",
      "Df Residuals:                     180   BIC:                             735.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     22.4932      0.232     96.981      0.000      22.036      22.951\n",
      "t_out_mean     0.0471      0.011      4.376      0.000       0.026       0.068\n",
      "==============================================================================\n",
      "Omnibus:                       65.631   Durbin-Watson:                   0.975\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              584.985\n",
      "Skew:                          -1.040   Prob(JB):                    9.38e-128\n",
      "Kurtosis:                      11.533   Cond. No.                         37.9\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Filter the data for naturally ventilated buildings\n",
    "air_conditioned = df_models[df_models['cooling_type'] == 'air conditioned']\n",
    "\n",
    "# Run linear regression between neutral_temp and t_out_mean\n",
    "lm_w_ac = smf.ols(formula='neutral_temp ~ t_out_mean', weights=air_conditioned.records, data=air_conditioned).fit()\n",
    "\n",
    "# Print the summary of the linear regression\n",
    "print(lm_w_ac.summary())"
   ]
  }
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