{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NMEC Baseline Model Predictability:  Data Science Deep Dive\n",
    "\n",
    "Normalized metered energy consumption (NMEC) methods offer many benefits to energy efficiency utility programs and their participants, but care must be used. NMEC programs rely on baseline models for pre-screening and energy savings calculations following implementation. The results of each of these steps determine program eligibility and the size of a participant’s incentive check. So, it’s vital that methods are used with good judgement and understanding of the statistical principles and key model metrics behind them.\n",
    "\n",
    "The example presented below demonstrates that current technical guidance should serve only as guidance and not be taken as criteria that determines a site \"passing or failing\" the NMEC screening process. In this post, I will walk you through an example building dataset where goodness-of-fit thresholds for three key model metrics are met but the baseline model is not sufficient to ensure fair and equitable project outcomes for all stakeholders. Focusing too narrowly on key model metrics, without accounting for the overall context, can lead to poorly performing projects and missed opportunities, especially in the pay-for-performance programs.\n",
    "\n",
    "### Background of NMEC in Energy Efficiency Programs\n",
    "\n",
    "Typical (non-NMEC) efficiency programs pay incentives based on estimated or deemed energy saving values, whereas NMEC programs offer pay-for-performance incentives. This creates an undeniable incentive for NMEC program participants to maximize actual energy savings. However, since NMEC baseline models calculate drive both project eligibility and energy savings using, it is imperative that models are as accurate as possible.\n",
    "To participate in an NMEC program, a building’s energy use must be predictable, within an acceptable degree of certainty. During pre-screening for a site-level NMEC program, a predictability analysis is performed wherein candidate baseline models are screened and vetted to ensure they can adequately characterize a site’s energy use. For buildings with regular operation, like grocery stores or university campus buildings, this can usually be accomplished. However, if models cannot characterize building energy use with enough accuracy, they cannot participate in the program.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictability Analysis\n",
    "\n",
    "The standard procedure in Site-Level NMEC programs involves a predictability analysis stage wherein candidate baseline models are screened and vetted to ensure that they can adequately characterize a site’s energy use. The screening process relies on three key model metrics: \n",
    "\n",
    "- CV(RMSE): measure of random error between the model fit and the actual data\n",
    "- NMBE: measure of the total bias error between the model fit and the actual data\n",
    "- R2: measure of the amount of variation in the energy use explained by the model\n",
    "\n",
    "The NMEC Rulebook references the requirements listed in the LBNL Technical Guidance to demonstrate feasibility of NMEC approaches on target buildings. The guidance provides the following three thresholds for model goodness-of-fit:\n",
    "\n",
    "- CV(RMSE) < 25%\n",
    "- NMBE between -0.5% and +0.5%\n",
    "- R2 > 0.7\n",
    "\n",
    "As with almost all thresholds and magic numbers, these criteria have taken a life of their own and are often referenced and evaluated without context when screening projects for NMEC programs. In this post, I will walk you through an example building dataset where these criteria are met but the baseline model is not sufficient to ensure a fair and equitable outcome for all stakeholders. This issue is of prime importance in pay-for-performance programs where the implementors’ payments depend on these baseline models’ accuracy to characterize the building energy use.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Load in required packages\n",
    "\n",
    "import_package = function(package_name){ ## function to suppress messages and warnings during package load\n",
    "    suppressWarnings(suppressMessages(require(package_name, character.only = TRUE)))\n",
    "}\n",
    "\n",
    "import_package(\"nmecr\")\n",
    "import_package(\"ggplot2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A predictability analysis usually begins by charting the energy use against time and temperature to visualize energy use patterns and identify major non-routine events that would make the building clearly ineligible for an NMEC program.\n",
    "\n",
    "\n",
    "Here we have energy use data from a commercial facility in southern California:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "eload_data <- readRDS(\"Data/Processed Energy Consumption - multiyear.rds\")\n",
    "temp_data <- readRDS(\"Data/Processed Temperature - multiyear.rds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Align energy and temperature data at daily intervals\n",
    "\n",
    "energy_temp_df <- nmecr::create_dataframe(eload_data, temp_data, start_date = \"2020-03-31\",\n",
    "                                          end_date = \"2021-04-01\", \n",
    "                                          convert_to_data_interval = \"Daily\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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wi4DdjvSFvuNfDOHgL2Q9oh0Sb6kGWB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v896GcoK0Q6JN9CHXop2hnKcs0gKynBx1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\nAADQgP8DcAEsHBEtGNIAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 960
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot energy use against time\n",
    "\n",
    "options(repr.plot.width=16, repr.plot.height=8)\n",
    "   \n",
    "temperatureColor <- \"#69b3a2\"\n",
    "priceColor <- rgb(0.2, 0.6, 0.9, 1)\n",
    "    \n",
    "energy_temp_df %>%\n",
    "    ggplot2::ggplot(aes(x = time)) +\n",
    "    geom_line(aes(y=eload), size=1.5, color=temperatureColor) + \n",
    "    geom_line( aes(y=temp*200), size=1.5, color=priceColor) +\n",
    "    scale_y_continuous(\n",
    "    name = \"Energy Consumption (kWh)\",    \n",
    "    sec.axis = sec_axis(~.*1/200, name=\"Temperature (deg F)\")) +\n",
    "    theme(\n",
    "    axis.title.y = element_text(color = temperatureColor, size=20),\n",
    "    axis.title.y.right = element_text(color = priceColor, size=20),\n",
    "    text = element_text(size = 20)) +\n",
    "    xlab(\"\")     \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The temperature data is plotted in blue on the right-axis, and the energy use data is plotted in green on the left-axis. The two datastreams are plotted as a scatterplot below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FHQ3gccYAufmBOIwwypQBwQBsUx7z7g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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 960
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot energy use against temperature\n",
    "options(repr.plot.width=16, repr.plot.height=8)\n",
    "\n",
    "energy_temp_df[complete.cases(energy_temp_df), ] %>%\n",
    "    ggplot2::ggplot(aes(x = temp, y = eload)) +\n",
    "    geom_point() +\n",
    "    scale_x_continuous(limits = c(0, NA)) +\n",
    "    scale_y_continuous(limits = c(0, NA)) +\n",
    "    theme(text = element_text(size = 20)) +\n",
    "    labs(x = \"Temperature (deg F)\", y = \"Energy Consumption (kWh)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a zoomed-in version of the scatterplot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4EdwUzHgCdjXPxZOoqgdlSAP9ydXHzknbA6kmgUB7CjwI5gprOgJ4MA+xHUjgpw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\ns6H/2zwlrS8S4A7ojeHVHWDHDejQ6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E\n2IxEo/SCs6DHgx0BchTYUQS1U/pHgM1INIoD2FFgR4AcBXYUQe2U/hFgMxKN4gB2FNgRIEeB\nHUVQO6V/BNiMRKM4gB0FdgTIUWBHEdRO6R8BNiPRKA5gR4EdAXIU2FEEtVP6R4DNSDSKA9hR\nYEeAHAV2FEHtlP4RYDMSjeIAdhTYESBHgR1FUDulfwTYjESjOIAdBXYEyFFgRxHUTukfATYj\n0SgOYEeBHQFyFNhRBLVT+keAzUg0igPYUWBHgBwFdhRB7ZT+EWAzEo3iAHYU2BEgR4EdRVA7\npX8E2IxEoziAHQV2BMhRYEcR1E7pHwE2I9EoDmBHgR0BchTYUQS1U/pHgM1INIoD2FFgR4Ac\nBXYUQe2U/hFgMxKN4gB2FNgRIEeBHUVQO6V/BNiMRKM4gB0FdgTIUWBHEdRO6R8BNiPRKA5g\nR4EdAXIU2FEEtVP6R4DNSDSKA9hRYEeAHAV2FEHtlP4RYDMSjeIAdhTYESBHgR1FUDulfwTY\njESjOIAdBXYEyFFgRxHUTukfATYj0SgOYEeBHQFyFNhRBLVT+keAzUg0igPYUWBHgBwFdhRB\n7ZT+EWAzEo3iAHYU2BEgR4EdRVA7pX8E2IxEoziAHQV2BMhRYEcR1E7pHwE2I9EoDmBHgR0B\nchTYUQS1U/pHgM1INIoD2FFgR4AcBXYUQe2U/hFgMxKN4gB2FNgRIEeBHUVQO6V/BNiMRKM4\ngB0FdgTIUWBHEdRO6R8BNiPRKA5gR4EdAXIU2FEEtVP6R4DNSDSKA9hRYEeAHAV2FEHtlP4R\nYDMSjeIAdhTYESBHgR1FUDulfwTYjESjOIAdBXYEyFFgRxHUTukfATYj0SgOYEeBHQFyFNhR\nBLVT+keAzUg0igPYUWBHgBwFdhRB7ZT+EWAzEo3iAHYU2BEgR4EdRVA7pX8E2IxEoziAHQV2\nBMhRYEcR1E7pHwE2I9EoDmBHgR0BchTYUQS1U/o3e4DhNvi/ue8ALBWWDgwl/NohwDAJ4R8J\nEBWWDgwl/NohwDAJ4R8JEBWWDgwl/NohwDAJ4R8JEBWWDgwl/NohwAAAADNAgAEAAGaAAAMA\nAMwAAQYAAJgBAgwAADADBBgAAGAGCDA48s+nur7/8vv90o+nuq6ffsx6j2AB1EfsvsLSgc78\n+/d6q/P3f/uLodcOAQY3fv/1vhHdLf5P75c+zXy3IDpH/b3ffoGlA5358r5Y/tldjL12CDC4\ncV9v/hn6e/3v0c0+8Lf6/p8/qz//3Ndf5r5jsBB+1P9u/sfSgc78W99v/sH/434Ra4cAgxdf\n6r93f/h7u/Z3GV6t/nvfqwFo4Xf9bft/lg505tP7E24/6qfN/4KvHQIMTvyp6z/7P91vHg/7\nf4H+vfunKUALn3bbTJYOdGd/2sDuD9HXDgEGJ/53+pzPl3p/GsSP9/0aAMk/70uGpQPdOQ1w\n9LVDgMGJs39xPtX70xL/2z03BKC5/2v3f5YOdGf/FPS/29Ouoq8dAgxOPNV/ti9D+vS/7cX7\n+vA3MY/GQDAOOy8sHejO+0lY/+5Owoq+dggwOFHX+5chfdpdPPqb2e4ULIf6r/0fWDrQnX93\nm51thsOvHQIMTtT1X/c//qwfD0/bAkd/JEAw/qn/9/4nlg70YP864O0pKNHXDgEGJ9b/Bn0/\nC/ppsymN/kiAYPx1WCUsHejOp+27Dyzl3/0EGJyoD+cf/rs5/SH6IwFi8e/+VeQsHejBj8O5\nVk+b98KKvnYIMDhxtvT/Cn42BMSivH6EpQPd+bvev/X89jTo6GuHAIMTT6cBjv56AIjF0emr\nLB3ozP2yNjsEGJz4cvin6Pbfnt+OXhEf8l1ZIRK/j948n6UDnTl74i362iHA4MSP/ceR7A7n\nlfV/9OQiQDP/O3rfIpYOdObT4d/923/DRV87BBi8+Gv/UNi9N83+XdF/hzwZAmJxsrlk6UBX\n/jkUd3sSVvS1Q4DBi/1b0nzaHXz5cvhcsIjvyQqxKMfuViwd6MF6c7N5C6z//t4dxAi+dggw\nuPHj9KOwY38yNoTifv9RWltYOtCZT0va7BBg8OPPl7/WC//wXOL/ntaPg6eIR2IgGmdPGLJ0\noDM/ThdL6LVDgAEAAGaAAAMAAMwAAQYAAJgBAgwAADADBBgAAGAGCDAAAMAMEGAAAIAZIMAA\nAAAzQIABAABmgAADAADMAAEGAACYAQIMMAHVJRPfg7eP46/jpXo4/cKQMS41fKxext83gOVB\ngAEmYPYAPxvc4GtdvZ1+xSbAb1X9OvrOASwPAgwwAbMH2OIG76png2tt0PBc3Y2+cwDLgwAD\nTMj0Tz3b3fDXqj7/0sAAX3ytrr4Ou08AS4YAA0zIkgNcX+wAmwX4+bLtAPkhwAATsuAAN0XS\nKsBNcQdIDwEGmJCL+rx8XH/p4eX4r1/uquru++bi84f1n17O/+pZ/fT3D9WHx+1fPa6/d/3n\nn+9/sz/genQP3v948mNnV3lEXT0e/vz1YX3Vz8dXdvFjz3ebW3+7nLgxwI/sAsMNQoABJuSs\nPm9372W8ez389ePuK8+bk562PJ7/1f6c4Yafft1c/Lhave7/avfjbQHe/9jFVRa+VtX39z++\nfnj/nsOVXfzY/luqr90C/L3iKDDcHgQYYELO6rPPVPW+A7gJ7/4rh6jtXiV7/FfvBW746Yf3\n76+rwkt7gPc/dnGVx3d1/5W3w5U/7K/s/Mfejm+/S4DXd/hDP5MAy4cAA0zIaX0+roO1yd7L\nOmAP739dfVh/5fs6YPX2eeiXerdrumvo49vqbZPhh6s/vdtdft781dv7392d3HBjgPfPLl9c\n5YG397uxZp3der1//vZ8qOvFj22ej35+O/6Wawr2rK/0reHLAJkhwAATclKf13W33quzLtfr\n7q93u5AvmxLv/1S//+T7s7Sbv3u98tMPhy/83P3prVT24h4c7QFfu0MHvlb706Req/1fvbzX\n9eLH3va77YdvOVXQsHP8zHPQcHsQYIAJOcnR5+rwHozrUH3e/fUucyVhl5ncHAr+fOWnLyPW\nKcBfr92hA4+HpH8+7C9vd3Sbfuz54ltO71BDgL9XR+d4AdwGBBhgQk5y9HB0YZfX6pC56vCU\nbMnkvnLft9+sfvqdn88PdacA/7x2h47v68/zP23uSNX0Yw/lhK3vHQP88/w5b4D8EGCACTnJ\n0cmZStsnmstfX/ypOjpIuv2S+unV/mVIh8rpAF+9Q8f39XKE9+e3L36sPilyp2PAq8vTvgCy\nQ4ABJuSkPtUpq7YAn16L+unjlyH1CPDlVTbc8Ys7cvljl99yTUHrlwESw5oHmJCpArzbKf3w\n8PxCgAGiwpoHmJDzAF/965YA1/qnNy9Den5t/PGWALff8cvnl2VjCTDANVjzABNykpmH85Om\nWgJ8dO7Tg/7pTi9Der0I8OVVHqiPv2l/htXPan8S1umPtZ2E1XQDHAOG24MAA0zISX0+X7z0\nRgd4/82P29cNdfvp54YAv175q4arPPBw/DKk/VtyPFb7lyGd/tjnk/vaKcCcBQ03CAEGmJCT\n+mx2IN9fWvSyq5oO8HsCN4d131p/+ufhJk4r+2H/At/Xi1coNVzlgcfDTm15ifLr+5Vf/Njb\n5e1fUXCA1wHDDUKAASbktD6b91J+Xrf0dbOf+H3VGuDD2zt+bvnpzYcVrb/y+nn7U2+7v9s1\n8nF/NfVlgC+v8sDL4Z2wyjUcztS6+LHLb7mmYM9zeZkzwK1AgAEm5LQ+/9/eHa04CANRGA5R\nRKSICFK8WPr+b7mJbEzUgFMKTt3839WydsSbcDoxadIzC7rt5VwAr1uL6rPq0aR8M7rU+res\nz/XfhwXSmVvGR02miNfnePyVHsvePoyB34JGgQhg4EK79Inbdbvd5VwAh/ORmtPqcHChsf58\nBP9Dk7EXDeH8yK2v3t8yqpNP1eF7QCg9lL17HOErfKsACkIAAxc6pM/YWn9w/XN/ORfALkYr\nY9vpvNpd8dPCzbC8XF2ira/Wzrlzf7bP7Aan/S2jIZ2T9nev+k3PvS8bXCbbbhZuQ5qYgUaB\nCGDgFvKN44XsdlWWlKy1bdmEhAIRwMAtqAdwL39LmzzrZCS5/ROXeAHlIICBW1APYNcCS0Oy\ninlai8757WmAUSLtMQ1ARD+AB3FK+iVg3ewaW78ETFJkJSkN/DfaYxqAiH4Au25W2AKn25Ik\ni6t6lkCjSOpjGoDEFwTwbKzwLfC6C8nY6fzTLq/nz54MuCX1MQ1A4gsC+DXKF0Iv25JsI5pZ\nbtmChDLpj2kAAApEAAMAoIAABgBAAQEMAIACAhgAAAUEMAAACghgAAAUEMAAACj4BaZ6shLv\nV9IqAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 960
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot energy use against temperature - zoomed in\n",
    "\n",
    "options(repr.plot.width=16, repr.plot.height=8)\n",
    "\n",
    "energy_temp_df[complete.cases(energy_temp_df), ] %>%\n",
    "    ggplot2::ggplot(aes(x = temp, y = eload)) +\n",
    "    geom_point() +\n",
    "    theme(text = element_text(size = 20)) +\n",
    "    labs(x = \"Temperature (deg F)\", y = \"Energy Consumption (kWh)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the shape of the scatter plot, a four-parameter model seems appropriate. However, as seen in the time-series chart, the energy use is also dependent on the time of use: weekday vs weekend. \n",
    "\n",
    "In the next section, the time-of-week & temperature and the four parameter algorithms are assessed for this data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Candidate Model Screening"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the two models and the dataframe for plotting\n",
    "\n",
    "four_parameter_model <- nmecr::model_with_CP(training_data = energy_temp_df,\n",
    "                                               model_input_options = nmecr::assign_model_inputs(regression_type = \"4P\"))\n",
    "\n",
    "TOWT_model <- nmecr::model_with_TOWT(training_data = energy_temp_df, \n",
    "                                     model_input_options = nmecr::assign_model_inputs(regression_type = \"TOWT\"))\n",
    "\n",
    "actual_modeled_df <- four_parameter_model$training_data %>%\n",
    "    select(-c(\"model_fit\")) %>%\n",
    "    mutate('4P_fit' = four_parameter_model$training_data$model_fit) %>%\n",
    "    mutate('TOWT_fit' = TOWT_model$training_data$model_fit)\n",
    "\n",
    "baseline_scatter_df <- tidyr::pivot_longer(actual_modeled_df,\n",
    "                                           cols = c(\"eload\", \"4P_fit\", \"TOWT_fit\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "generate_scatter_plot <- function(data) {   # to generate scatter plot \n",
    "    data %>%\n",
    "    ggplot2::ggplot(aes(x = temp, y = value, color = name)) +\n",
    "    geom_point() +\n",
    "    scale_x_continuous(limits = c(0, NA)) +\n",
    "    scale_y_continuous(limits = c(0, NA)) +\n",
    "    theme(text = element_text(size = 20), legend.title = element_blank()) +\n",
    "    labs(x = \"Temperature (deg F)\", y = \"Energy Consumption (kWh)\")    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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m9Pt90ZSVKBaIsk1qBkYKwHYz0Yx2PpLfCwv+++/nIU9IXiDXB/BNb1o/rCWNEW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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 960
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=16, repr.plot.height=8)\n",
    "generate_scatter_plot(baseline_scatter_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Model Summary Stats \n",
    "\n",
    "generate_summary_stats <- function(model1, model2) {\n",
    "    \n",
    "    model1_stats <- nmecr::calculate_summary_statistics(model1)\n",
    "    model1_unc <- nmecr::calculate_savings_and_uncertainty(modeled_object = model1,\n",
    "                                        model_summary_statistics = model1_stats,\n",
    "                                        confidence_level = 90)\n",
    "\n",
    "    model1_metrics <- dplyr::bind_cols(model1_stats, model1_unc)\n",
    "\n",
    "    model2_stats <- nmecr::calculate_summary_statistics(model2)\n",
    "\n",
    "    model2_unc <- nmecr::calculate_savings_and_uncertainty(modeled_object = model2,\n",
    "                                        model_summary_statistics = model2_stats,\n",
    "                                        confidence_level = 90)\n",
    "\n",
    "    model2_metrics <- dplyr::bind_cols(model2_stats, model2_unc)\n",
    "\n",
    "    metrics <- dplyr::bind_rows(model1_metrics, model2_metrics)\n",
    "    metrics <- metrics[, c(1:5,9:10)]\n",
    "    names(metrics) <- c(\"R2\", \"Adj. R2\", \"CVRMSE%\", \"NDBE%\", \"NMBE%\", \n",
    "                    \"Savings Uncertainty @ 10% Savings\", \"Savings Fraction for 50% Uncertainty\")\n",
    "\n",
    "    metrics$`NDBE%` <- as.numeric(metrics$`NDBE%`)\n",
    "    metrics$`NMBE%` <- as.numeric(metrics$`NMBE%`)\n",
    "\n",
    "    metrics <- metrics %>%\n",
    "        mutate(\"Algorithm\" = c(\"4P\", \"TOWT\"))\n",
    "\n",
    "    metrics <- metrics %>%\n",
    "        select(c(\"Algorithm\", everything()))\n",
    "\n",
    "    return(metrics)    \n",
    "}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Algorithm</th><th scope=col>R2</th><th scope=col>Adj. R2</th><th scope=col>CVRMSE%</th><th scope=col>NDBE%</th><th scope=col>NMBE%</th><th scope=col>Savings Uncertainty @ 10% Savings</th><th scope=col>Savings Fraction for 50% Uncertainty</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>4P  </td><td>0.78</td><td>0.78</td><td>9.09</td><td>-2.407880e-16</td><td>-2.427946e-16</td><td>0.2759253</td><td>0.05518506</td></tr>\n",
       "\t<tr><td>TOWT</td><td>0.80</td><td>0.79</td><td>8.67</td><td>-2.750334e-14</td><td>-2.852489e-14</td><td>0.2831296</td><td>0.05662593</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 8\n",
       "\\begin{tabular}{llllllll}\n",
       " Algorithm & R2 & Adj. R2 & CVRMSE\\% & NDBE\\% & NMBE\\% & Savings Uncertainty @ 10\\% Savings & Savings Fraction for 50\\% Uncertainty\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 4P   & 0.78 & 0.78 & 9.09 & -2.407880e-16 & -2.427946e-16 & 0.2759253 & 0.05518506\\\\\n",
       "\t TOWT & 0.80 & 0.79 & 8.67 & -2.750334e-14 & -2.852489e-14 & 0.2831296 & 0.05662593\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 8\n",
       "\n",
       "| Algorithm &lt;chr&gt; | R2 &lt;dbl&gt; | Adj. R2 &lt;dbl&gt; | CVRMSE% &lt;dbl&gt; | NDBE% &lt;dbl&gt; | NMBE% &lt;dbl&gt; | Savings Uncertainty @ 10% Savings &lt;dbl&gt; | Savings Fraction for 50% Uncertainty &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 4P   | 0.78 | 0.78 | 9.09 | -2.407880e-16 | -2.427946e-16 | 0.2759253 | 0.05518506 |\n",
       "| TOWT | 0.80 | 0.79 | 8.67 | -2.750334e-14 | -2.852489e-14 | 0.2831296 | 0.05662593 |\n",
       "\n"
      ],
      "text/plain": [
       "  Algorithm R2   Adj. R2 CVRMSE% NDBE%         NMBE%        \n",
       "1 4P        0.78 0.78    9.09    -2.407880e-16 -2.427946e-16\n",
       "2 TOWT      0.80 0.79    8.67    -2.750334e-14 -2.852489e-14\n",
       "  Savings Uncertainty @ 10% Savings Savings Fraction for 50% Uncertainty\n",
       "1 0.2759253                         0.05518506                          \n",
       "2 0.2831296                         0.05662593                          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_summary_stats(four_parameter_model, TOWT_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As per the three thresholds, both models demonstrate feasibility for an NMEC approach. The R2 value is above 0.7, the CV(RMSE) is well below 25%, and NMBE is near zero.\n",
    "\n",
    "The guidance, however, also notes that \"...these (thresholds) do not comprise pass/fail criteria, but rather an analysis that will feed into an interpretation of model suitability\".\n",
    "\n",
    "This statement is very important but is overshadowed by the threshold criteria which serve as heuristics, often used in place of a comprehensive analysis rather than with it. A comprehensive analysis involves looking beyond the minimum 12 months of data and assessing the complete energy use behavior of the site. Limiting our focus to 12 months leaves us oblivious to slow moving overall trends or NREs outside the 12-month period.\n",
    "\n",
    "One such NRE is the onset of the COVID-19 pandemic. The shelter-in-place restrictions first went into effect in California on March 19th, 2020. The dataset we have here is completely within the shutdown period, and it is hard for me to imagine a facility that has continued to operate similarly during the pandemic as it did prior to the shutdown. \n",
    "\n",
    "A well-known and widely trusted way to check for this behavior is to conduct model testing on a dataset different from the one used to develop and train the model. We take another 12 months of data from the same building and test the model’s ability to predict the energy use on this “new” dataset. The only requirement of this methodology is that the building operations and energy use should be similar between the two datasets. In this post, I’ve chosen an additional 12 months of data to test the model. The testing period’s length is context driven and may be lengthened or shortened based on data availability and other factors. The Efficiency Valuation Organization (EVO) uses this methodology  to evaluate the accuracy of M&V tools against the benchmarked public-domain tools. \n",
    "\n",
    "In the next section, I apply this process to the candidate models. I have another year of data which I can use to assess the model’s skill in capturing the underlying trend of the site’s energy consumption profile: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Expanded Candidate Model Screening (Predictive Accuracy)\n",
    "\n",
    "The models were built on the dataset: April, 2020 – April, 2021. The NMEC Rulebook requires that the time between the end of the baseline period and the completion of the project implementation stage should not exceed 18 months. To comply with this requirement, I am using the data from April, 2019 – April, 2020 to evaluate the models’ predictive accuracy. The scatter plot below shows the actual energy use in green and the model predictions in red and blue. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictive Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "energy_temp_df <- nmecr::create_dataframe(eload_data, temp_data, start_date = \"2020-03-31\",\n",
    "                                          end_date = \"2021-04-01\", \n",
    "                                          convert_to_data_interval = \"Daily\")\n",
    "\n",
    "prediction_energy_temp_df <- nmecr::create_dataframe(eload_data, temp_data, start_date = \"2019-03-31\",\n",
    "                                          end_date = \"2020-04-01\", \n",
    "                                          convert_to_data_interval = \"Daily\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "CP_prediction <- nmecr::calculate_model_predictions(training_data = energy_temp_df,\n",
    "                                                           prediction_data = prediction_energy_temp_df,\n",
    "                                                           modeled_object = four_parameter_model)\n",
    "\n",
    "TOWT_prediction <- nmecr::calculate_model_predictions(training_data = energy_temp_df,\n",
    "                                                           prediction_data = prediction_energy_temp_df,\n",
    "                                                           modeled_object = TOWT_model)\n",
    "\n",
    "all_predictions_df <- CP_prediction %>%\n",
    "    select(-c(\"predictions\")) %>%\n",
    "    mutate('4P_predictions' = CP_prediction$predictions) %>%\n",
    "    mutate('TOWT_predictions' = TOWT_prediction$predictions)\n",
    "\n",
    "prediction_scatter_df <- tidyr::pivot_longer(all_predictions_df,\n",
    "                                           cols = c(\"eload\", \"4P_predictions\", \"TOWT_predictions\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Arw50UBtka2OKhsqWRlC+S8APe2Oq8N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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 960
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=16, repr.plot.height=8)\n",
    "generate_scatter_plot(prediction_scatter_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both models’ predictions are much lower than the actual energy use of the building prior to the COVID-19 related shutdowns. This result is expected as most buildings emptied out during the pandemic and consequently, had much lower energy use. As the economy reopens, these buildings will be reoccupied and energy use will go back up. \n",
    "\n",
    "If we were to use the models built on the 2020-2021 dataset, the adjusted baseline may be much lower than actual energy use after project implementation, and the project would appear to have negative savings. Narrowly focusing on the three thresholds would, therefore, lead to poor outcomes for pay-for-performance programs and large amounts of losses for the implementers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "calculate_prediction_errors <- function(data) {\n",
    "    \n",
    "    CP_prediction_error <- sum(data$eload - data$`4P_predictions`, na.rm = T)/\n",
    "                            sum(data$eload, na.rm = T)\n",
    "    TOWT_prediction_error <- sum(data$eload - data$TOWT_predictions, na.rm = T)/\n",
    "                            sum(data$eload, na.rm = T)\n",
    "    \n",
    "    CP_prediction_error <- paste0(round(100*CP_prediction_error, 2), \"%\")\n",
    "\n",
    "    TOWT_prediction_error <- paste0(round(100*TOWT_prediction_error, 2), \"%\")\n",
    "    \n",
    "    return(list(CP_prediction_error, TOWT_prediction_error))   \n",
    "    \n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Prediction Error using the Four Parameter model is 12.62% while the error for the TOWT model is 12.18%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prediction_errors <- calculate_prediction_errors(all_predictions_df)\n",
    "\n",
    "message(\"Prediction Error using the Four Parameter model is \", prediction_errors[1], \n",
    "        \" while the error for the TOWT model is \", prediction_errors[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional Independent Variables\n",
    "\n",
    "After the onset of COVID-19, building occupancy has become as important a predictor as time-of-week and temperature in determining the general trend of energy use profiles. While not all buildings have the luxury of tracking occupancy through key-card swipes, wi-fi connections, or room occupancy, those that do have an easier solution to the problem. \n",
    "\n",
    "The prediction errors for the four-parameter model and the TOWT model above was 12.92% and 12.23%, respectively.  After adding in occupancy data to the model, the prediction errors were reduced to 4.25% and 4.07%, respectively. The scatter plot below shows the updated predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "occupancy_data <- readRDS(\"Data/Processed Occupancy Data - multiyear.rds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "energy_temp_df_upd <- nmecr::create_dataframe(eload_data, temp_data, \n",
    "                                              additional_independent_variables = occupancy_data,\n",
    "                                              additional_variable_aggregation = c(median),\n",
    "                                              start_date = \"2020-03-31\",\n",
    "                                              end_date = \"2021-04-01\", \n",
    "                                              convert_to_data_interval = \"Daily\")\n",
    "\n",
    "prediction_energy_temp_df_upd <- nmecr::create_dataframe(eload_data, temp_data, \n",
    "                                                         additional_independent_variables = occupancy_data,\n",
    "                                                         additional_variable_aggregation = c(median),\n",
    "                                                         start_date = \"2019-03-31\",\n",
    "                                                         end_date = \"2020-04-01\", \n",
    "                                                         convert_to_data_interval = \"Daily\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "four_parameter_model_upd <- nmecr::model_with_CP(training_data = energy_temp_df_upd,\n",
    "                                               model_input_options = nmecr::assign_model_inputs(regression_type = \"4P\"))\n",
    "\n",
    "TOWT_model_upd <- nmecr::model_with_TOWT(training_data = energy_temp_df_upd, \n",
    "                                     model_input_options = nmecr::assign_model_inputs(regression_type = \"TOWT\"))\n",
    "\n",
    "actual_modeled_df_upd <- four_parameter_model_upd$training_data %>%\n",
    "    select(-c(\"model_fit\")) %>%\n",
    "    mutate('4P_fit' = four_parameter_model_upd$training_data$model_fit) %>%\n",
    "    mutate('TOWT_fit' = TOWT_model_upd$training_data$model_fit)\n",
    "\n",
    "baseline_scatter_df_upd <- tidyr::pivot_longer(actual_modeled_df_upd,\n",
    "                                           cols = c(\"eload\", \"4P_fit\", \"TOWT_fit\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xINAhyGG+CP54eibnDx8Bzpclhmi8RqUDQw1oOxHoylNO3s9XfrHIX13q0CHx0S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\nEMAAABRAAAMAUAABDABAAQQwAAAF/A+Y1gBD9GBsZwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 960
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=16, repr.plot.height=8)\n",
    "generate_scatter_plot(baseline_scatter_df_upd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "CP_prediction_upd <- nmecr::calculate_model_predictions(training_data = energy_temp_df_upd,\n",
    "                                                           prediction_data = prediction_energy_temp_df_upd,\n",
    "                                                           modeled_object = four_parameter_model_upd)\n",
    "\n",
    "TOWT_prediction_upd <- nmecr::calculate_model_predictions(training_data = energy_temp_df_upd,\n",
    "                                                           prediction_data = prediction_energy_temp_df_upd,\n",
    "                                                           modeled_object = TOWT_model_upd)\n",
    "\n",
    "all_predictions_df_upd <- CP_prediction_upd %>%\n",
    "    select(-c(\"predictions\")) %>%\n",
    "    mutate('4P_predictions' = CP_prediction_upd$predictions) %>%\n",
    "    mutate('TOWT_predictions' = TOWT_prediction_upd$predictions)\n",
    "\n",
    "prediction_scatter_df_upd <- tidyr::pivot_longer(all_predictions_df_upd,\n",
    "                                           cols = c(\"eload\", \"4P_predictions\", \"TOWT_predictions\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Prediction Error using the Four Parameter model is 4.25% while the error for the TOWT model is 4.07%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prediction_errors <- calculate_prediction_errors(all_predictions_df_upd)\n",
    "\n",
    "message(\"Prediction Error using the Four Parameter model is \", prediction_errors[1], \n",
    "        \" while the error for the TOWT model is \", prediction_errors[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the updated goodness-of-fit metrics are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Algorithm</th><th scope=col>R2</th><th scope=col>Adj. R2</th><th scope=col>CVRMSE%</th><th scope=col>NDBE%</th><th scope=col>NMBE%</th><th scope=col>Savings Uncertainty @ 10% Savings</th><th scope=col>Savings Fraction for 50% Uncertainty</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>4P  </td><td>0.86</td><td>0.86</td><td>7.62</td><td>-9.056756e-17</td><td>-9.171399e-17</td><td>0.1980716</td><td>0.03961432</td></tr>\n",
       "\t<tr><td>TOWT</td><td>0.88</td><td>0.87</td><td>7.23</td><td>-3.915538e-14</td><td>-4.094680e-14</td><td>0.1976433</td><td>0.03952866</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 8\n",
       "\\begin{tabular}{llllllll}\n",
       " Algorithm & R2 & Adj. R2 & CVRMSE\\% & NDBE\\% & NMBE\\% & Savings Uncertainty @ 10\\% Savings & Savings Fraction for 50\\% Uncertainty\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 4P   & 0.86 & 0.86 & 7.62 & -9.056756e-17 & -9.171399e-17 & 0.1980716 & 0.03961432\\\\\n",
       "\t TOWT & 0.88 & 0.87 & 7.23 & -3.915538e-14 & -4.094680e-14 & 0.1976433 & 0.03952866\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 8\n",
       "\n",
       "| Algorithm &lt;chr&gt; | R2 &lt;dbl&gt; | Adj. R2 &lt;dbl&gt; | CVRMSE% &lt;dbl&gt; | NDBE% &lt;dbl&gt; | NMBE% &lt;dbl&gt; | Savings Uncertainty @ 10% Savings &lt;dbl&gt; | Savings Fraction for 50% Uncertainty &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 4P   | 0.86 | 0.86 | 7.62 | -9.056756e-17 | -9.171399e-17 | 0.1980716 | 0.03961432 |\n",
       "| TOWT | 0.88 | 0.87 | 7.23 | -3.915538e-14 | -4.094680e-14 | 0.1976433 | 0.03952866 |\n",
       "\n"
      ],
      "text/plain": [
       "  Algorithm R2   Adj. R2 CVRMSE% NDBE%         NMBE%        \n",
       "1 4P        0.86 0.86    7.62    -9.056756e-17 -9.171399e-17\n",
       "2 TOWT      0.88 0.87    7.23    -3.915538e-14 -4.094680e-14\n",
       "  Savings Uncertainty @ 10% Savings Savings Fraction for 50% Uncertainty\n",
       "1 0.1980716                         0.03961432                          \n",
       "2 0.1976433                         0.03952866                          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_summary_stats(four_parameter_model_upd, TOWT_model_upd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the two models have almost identical goodness-of-fit metrics. The occupancy variable provides information to the changepoint model that the TOWT tries to glean from the data internally. In this scenario, I would go with the simpler algorithm to develop the baseline model for this site’s M&V Plan.\n",
    "\n",
    "## Summary\n",
    "\n",
    "Testing a model's ability to predict data that it has not seen before is analogous to sitting for an exam. You can get the top score on an assessment if you are free to look through the answer key. Similarly, a model can perform very well on the dataset it is trained on and present low goodness-of-fit metrics. This is especially true for the advanced machine-learning algorithms that are built to minimize the model error. If left unchecked, many of these advanced algorithms begin to model the noise in the data and can present a false sense of high accuracy to the analyst. The technical term for this behavior is over-fitting. \n",
    "\n",
    "Your knowledge of the subject matter is truly tested when you give the exam and don't have the answers readily available. And similarly, a model's ability to produce reliable predictions, which can be used to correctly quantify project savings, is truly assessed using a new dataset that is different from the one the model was trained on, but is similar enough such that the data profiles are consistent with the training dataset. After all, you want the exam to cover the material that you studied for.\n",
    "\n",
    "A robust model is one that is built using all influential data streams and is able to produce reliable predictions on an 'unseen' dataset. Limiting its characteristics to the three goodness-of-fit metrics will corner us into an uncomfortable spot with a false sense of security and high exposure to financial as well as environmental risk."
   ]
  }
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