diff --git a/data/PP/energy_use_big.ndjson b/data/PP/energy_use_big.ndjson
index 1e5855a..6fe0fdb 100644
--- a/data/PP/energy_use_big.ndjson
+++ b/data/PP/energy_use_big.ndjson
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diff --git a/data/PP/standby_verbruik_demo.ipynb b/demo_standby_verbruik.ipynb
similarity index 96%
rename from data/PP/standby_verbruik_demo.ipynb
rename to demo_standby_verbruik.ipynb
index 57ef794..2e0db94 100644
--- a/data/PP/standby_verbruik_demo.ipynb
+++ b/demo_standby_verbruik.ipynb
@@ -2,14 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "# imports\n",
     "import polars as pl\n",
     "import json\n",
-    "import altair as alt"
+    "import altair as alt\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "# %autoreload?"
    ]
   },
   {
@@ -28,28 +32,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.01 μs ± 161 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
+      "6.07 μs ± 115 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
      ]
     }
    ],
    "source": [
     "%%timeit\n",
     "energy_use_df = pl.scan_ndjson(\n",
-    "    \"energy_use_test1.ndjson\",\n",
+    "    \"data/PP/energy_use_test1.ndjson\",\n",
     "    schema={\"timestamp\": pl.Datetime(time_zone=\"Europe/Brussels\"), \"total\": pl.Float64},\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -79,14 +83,14 @@
        "└───────────────────────────────┴───────┘"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "energy_use_lf_1 = pl.scan_ndjson(\n",
-    "    \"energy_use_test1.ndjson\",\n",
+    "    \"data/PP/energy_use_test1.ndjson\",\n",
     "    schema={\"timestamp\": pl.Datetime(time_zone=\"Europe/Brussels\"), \"total\": pl.Float64},\n",
     ")\n",
     "energy_use_lf_1.collect().head()"
@@ -101,21 +105,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "35.1 ms ± 2.47 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "34.3 ms ± 1.25 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
    "source": [
     "%%timeit\n",
     "# Read the JSON file\n",
-    "with open(\"energy_use.json\", \"r\") as file:\n",
+    "with open(\"data/PP/energy_use.json\", \"r\") as file:\n",
     "    data = json.load(file)\n",
     "\n",
     "# Convert the data into a list of dictionaries\n",
@@ -131,15 +135,46 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Defining the analysis \n",
+    "# Base Load analysis\n",
     "\n",
-    "and defining the frames\n",
-    "## WHAT is the standby?\n"
+    "## loading in the data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Base Load: 90570.4W\n",
+      "Daily Usage: 2173.7 kWh\n",
+      "Base Percentage: 65.5%\n"
+     ]
+    }
+   ],
+   "source": [
+    "from openenergyid.baseload.main import main\n",
+    "\n",
+    "metrics = main(\"data/PP/energy_use_big.ndjson\")\n",
+    "# display(metrics)\n",
+    "print(f\"Base Load: {metrics.base_load_watts:.1f}W\")\n",
+    "print(f\"Daily Usage: {metrics.daily_usage_kwh:.1f} kWh\")\n",
+    "print(f\"Base Percentage: {metrics.base_percentage:.1f}%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Test analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -177,11 +212,41 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>timestamp</th><th>total</th></tr><tr><td>datetime[μs, Europe/Brussels]</td><td>f64</td></tr></thead><tbody><tr><td>2024-01-01 00:00:00 CET</td><td>51.625</td></tr><tr><td>2024-01-01 00:15:00 CET</td><td>50.75</td></tr><tr><td>2024-01-01 00:30:00 CET</td><td>38.5</td></tr><tr><td>2024-01-01 00:45:00 CET</td><td>40.25</td></tr><tr><td>2024-01-01 01:00:00 CET</td><td>59.500004</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 2)\n",
+       "┌───────────────────────────────┬───────────┐\n",
+       "│ timestamp                     ┆ total     │\n",
+       "│ ---                           ┆ ---       │\n",
+       "│ datetime[μs, Europe/Brussels] ┆ f64       │\n",
+       "╞═══════════════════════════════╪═══════════╡\n",
+       "│ 2024-01-01 00:00:00 CET       ┆ 51.625    │\n",
+       "│ 2024-01-01 00:15:00 CET       ┆ 50.75     │\n",
+       "│ 2024-01-01 00:30:00 CET       ┆ 38.5      │\n",
+       "│ 2024-01-01 00:45:00 CET       ┆ 40.25     │\n",
+       "│ 2024-01-01 01:00:00 CET       ┆ 59.500004 │\n",
+       "└───────────────────────────────┴───────────┘"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "energy_use_lf_1 = pl.scan_ndjson(\n",
-    "    \"energy_use_big.ndjson\",\n",
+    "    \"data/PP/energy_use_big.ndjson\",\n",
     "    schema={\"timestamp\": pl.Datetime(time_zone=\"Europe/Brussels\"), \"total\": pl.Float64},\n",
     ")\n",
     "testframe = (\n",
@@ -189,19 +254,20 @@
     "    # .with_columns(pl.col(\"timestamp\").interpolate(method=\"linear\"))\n",
     ")\n",
     "tf = testframe.collect()\n",
-    "display(tf)"
+    "display(tf)\n",
+    "display(energy_use_lf_1.collect().head())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_44261/1445527080.py:2: DeprecationWarning: `GroupBy.count` is deprecated. It has been renamed to `len`.\n",
+      "/tmp/ipykernel_22265/3491075188.py:2: DeprecationWarning: `GroupBy.count` is deprecated. It has been renamed to `len`.\n",
       "  value_counts = tf.group_by(\"total\").count().sort(\"total\")\n"
      ]
     },
@@ -210,23 +276,23 @@
       "text/html": [
        "\n",
        "<style>\n",
-       "  #altair-viz-c8547db926b64a229a639db01f646509.vega-embed {\n",
+       "  #altair-viz-c5d0a2dabcb9499abc7b36d0ce5f45ac.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-c8547db926b64a229a639db01f646509.vega-embed details,\n",
-       "  #altair-viz-c8547db926b64a229a639db01f646509.vega-embed details summary {\n",
+       "  #altair-viz-c5d0a2dabcb9499abc7b36d0ce5f45ac.vega-embed details,\n",
+       "  #altair-viz-c5d0a2dabcb9499abc7b36d0ce5f45ac.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-c8547db926b64a229a639db01f646509\"></div>\n",
+       "<div id=\"altair-viz-c5d0a2dabcb9499abc7b36d0ce5f45ac\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-c8547db926b64a229a639db01f646509\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-c8547db926b64a229a639db01f646509\");\n",
+       "    if (outputDiv.id !== \"altair-viz-c5d0a2dabcb9499abc7b36d0ce5f45ac\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-c5d0a2dabcb9499abc7b36d0ce5f45ac\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
@@ -272,7 +338,7 @@
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        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
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@@ -314,7 +380,7 @@
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   {
    "cell_type": "code",
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+   "execution_count": 20,
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@@ -380,7 +446,7 @@
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   {
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@@ -432,7 +498,7 @@
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+   "execution_count": 22,
    "metadata": {},
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-       "  #altair-viz-0b8433113d0e49a6811d2d45ca41ae29.vega-embed {\n",
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-       "  #altair-viz-0b8433113d0e49a6811d2d45ca41ae29.vega-embed details summary {\n",
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        "</style>\n",
-       "<div id=\"altair-viz-0b8433113d0e49a6811d2d45ca41ae29\"></div>\n",
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        "<script type=\"text/javascript\">\n",
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-   "metadata": {
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-   },
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [
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-       "<div id=\"altair-viz-30c7828ae4cd4b24b52acdd0eb66abd8\"></div>\n",
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@@ -671,7 +733,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 24,
    "metadata": {},
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        "</style>\n",
-       "<div id=\"altair-viz-395255c5ff734a5898941f5008b88ce3\"></div>\n",
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-       "      outputDiv = document.getElementById(\"altair-viz-395255c5ff734a5898941f5008b88ce3\");\n",
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-     "execution_count": 32,
+     "execution_count": 24,
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@@ -800,7 +862,7 @@
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   {
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-   "execution_count": 33,
+   "execution_count": 25,
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   {
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-   "execution_count": 34,
+   "execution_count": 26,
    "metadata": {},
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-       "<div id=\"altair-viz-699a15127d13430c96efed69608bc546\"></div>\n",
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+   "execution_count": 28,
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        "\n",
        "<style>\n",
-       "  #altair-viz-3a2f89434e484656bd9efaa8f9c0aa17.vega-embed {\n",
+       "  #altair-viz-46fd846d77f24fc4806debfeb686feb6.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-3a2f89434e484656bd9efaa8f9c0aa17.vega-embed details,\n",
-       "  #altair-viz-3a2f89434e484656bd9efaa8f9c0aa17.vega-embed details summary {\n",
+       "  #altair-viz-46fd846d77f24fc4806debfeb686feb6.vega-embed details,\n",
+       "  #altair-viz-46fd846d77f24fc4806debfeb686feb6.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-3a2f89434e484656bd9efaa8f9c0aa17\"></div>\n",
+       "<div id=\"altair-viz-46fd846d77f24fc4806debfeb686feb6\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
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-       "      outputDiv = document.getElementById(\"altair-viz-3a2f89434e484656bd9efaa8f9c0aa17\");\n",
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+       "      outputDiv = document.getElementById(\"altair-viz-46fd846d77f24fc4806debfeb686feb6\");\n",
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        "    const paths = {\n",
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        "</script>"
       ],
       "text/plain": [
        "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
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@@ -1184,7 +1246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1192,23 +1254,23 @@
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        "\n",
        "<style>\n",
-       "  #altair-viz-97aae4b2a6f74ef7bcd449ff0b7e4e80.vega-embed {\n",
+       "  #altair-viz-61aad07bbaf9410cb010fd0e7c5c8b16.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
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-       "  #altair-viz-97aae4b2a6f74ef7bcd449ff0b7e4e80.vega-embed details summary {\n",
+       "  #altair-viz-61aad07bbaf9410cb010fd0e7c5c8b16.vega-embed details,\n",
+       "  #altair-viz-61aad07bbaf9410cb010fd0e7c5c8b16.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-97aae4b2a6f74ef7bcd449ff0b7e4e80\"></div>\n",
+       "<div id=\"altair-viz-61aad07bbaf9410cb010fd0e7c5c8b16\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-97aae4b2a6f74ef7bcd449ff0b7e4e80\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-97aae4b2a6f74ef7bcd449ff0b7e4e80\");\n",
+       "    if (outputDiv.id !== \"altair-viz-61aad07bbaf9410cb010fd0e7c5c8b16\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-61aad07bbaf9410cb010fd0e7c5c8b16\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
@@ -1254,14 +1316,14 @@
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
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+       "  #altair-viz-3c17b36a1ea342ecb3578a1375c97ee2.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-632b09f6acf84e35b5b482e204e1495d\"></div>\n",
+       "<div id=\"altair-viz-3c17b36a1ea342ecb3578a1375c97ee2\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
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-       "      outputDiv = document.getElementById(\"altair-viz-632b09f6acf84e35b5b482e204e1495d\");\n",
+       "    if (outputDiv.id !== \"altair-viz-3c17b36a1ea342ecb3578a1375c97ee2\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-3c17b36a1ea342ecb3578a1375c97ee2\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
@@ -1448,7 +1510,7 @@
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\"max_power_usage_per_day\": 8232.0}, {\"timestamp\": 1726603200000, \"total_daily_usage\": 4366.250002, \"lowest_recorded\": 19.25, \"min_power_usage_per_day\": 1848.0, \"max_power_usage_per_day\": 9156.0}, {\"timestamp\": 1726689600000, \"total_daily_usage\": 4520.250014, \"lowest_recorded\": 19.25, \"min_power_usage_per_day\": 1848.0, \"max_power_usage_per_day\": 8064.0}, {\"timestamp\": 1726776000000, \"total_daily_usage\": 4292.75001, \"lowest_recorded\": 19.25, \"min_power_usage_per_day\": 1848.0, \"max_power_usage_per_day\": 8484.0}, {\"timestamp\": 1726862400000, \"total_daily_usage\": 2805.249985999999, \"lowest_recorded\": 19.25, \"min_power_usage_per_day\": 1848.0, \"max_power_usage_per_day\": 4116.0}, {\"timestamp\": 1726948800000, \"total_daily_usage\": 2592.625006, \"lowest_recorded\": 19.25, \"min_power_usage_per_day\": 1848.0, \"max_power_usage_per_day\": 3276.0}, {\"timestamp\": 1727035200000, \"total_daily_usage\": 4653.250006, \"lowest_recorded\": 19.25, 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        "</script>"
       ],
       "text/plain": [
@@ -1476,114 +1538,6 @@
     "kde_chart.display()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### testing differences between 1. absolute lowest value 2. 1 percent or 3. 5 percent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import altair as alt\n",
-    "\n",
-    "# # Assuming df is your DataFrame from the previous analysis\n",
-    "# # clear lowest1percent variable\n",
-    "# # df = df.with_columns(pl.lit(None).alias(\"lowest_1_percent\"))\n",
-    "# # 1. Comparison of Different Standby Measures Over Time\n",
-    "# standby_comparison = (\n",
-    "#     alt.Chart(df)\n",
-    "#     .transform_fold([\"low_end\", \"lowest_1_percent\", \"lowest_5_percent\"], as_=[\"measure\", \"value\"])\n",
-    "#     .mark_line()\n",
-    "#     .encode(\n",
-    "#         x=\"timestamp:T\",\n",
-    "#         y=\"value:Q\",\n",
-    "#         color=alt.Color(\"measure:N\", scale=alt.Scale(scheme=\"category10\")),\n",
-    "#         tooltip=[\"timestamp:T\", \"value:Q\", \"measure:N\"],\n",
-    "#     )\n",
-    "#     .properties(\n",
-    "#         width=800, height=400, title=\"Comparison of Different Standby Usage Measures Over Time\"\n",
-    "#     )\n",
-    "# )\n",
-    "\n",
-    "# # 2. Daily Percentage of Total for Each Measure\n",
-    "# percentage_comparison = (\n",
-    "#     alt.Chart(df)\n",
-    "#     .transform_fold([\"low_end\", \"lowest_1_percent\", \"lowest_5_percent\"], as_=[\"measure\", \"value\"])\n",
-    "#     .transform_calculate(percentage=\"datum.value / datum.total * 100\")\n",
-    "#     .mark_line()\n",
-    "#     .encode(\n",
-    "#         x=\"timestamp:T\",\n",
-    "#         y=alt.Y(\"percentage:Q\", axis=alt.Axis(format=\"%\")),\n",
-    "#         color=alt.Color(\"measure:N\", scale=alt.Scale(scheme=\"category10\")),\n",
-    "#         tooltip=[\"timestamp:T\", alt.Tooltip(\"percentage:Q\", format=\".2%\"), \"measure:N\"],\n",
-    "#     )\n",
-    "#     .properties(\n",
-    "#         width=800, height=400, title=\"Daily Percentage of Total Energy Use for Each Standby Measure\"\n",
-    "#     )\n",
-    "# )\n",
-    "\n",
-    "# # 3. Box Plot of Different Measures\n",
-    "# box_plot = (\n",
-    "#     alt.Chart(df)\n",
-    "#     .transform_fold([\"low_end\", \"lowest_1_percent\", \"lowest_5_percent\"], as_=[\"measure\", \"value\"])\n",
-    "#     .mark_boxplot()\n",
-    "#     .encode(x=\"measure:N\", y=\"value:Q\")\n",
-    "#     .properties(width=400, height=300, title=\"Distribution of Different Standby Measures\")\n",
-    "# )\n",
-    "\n",
-    "# # 4. Scatter Plot: Lowest 1% vs Lowest 5%\n",
-    "# scatter_1_5 = (\n",
-    "#     alt.Chart(df)\n",
-    "#     .mark_circle()\n",
-    "#     .encode(\n",
-    "#         x=\"lowest_1_percent:Q\",\n",
-    "#         y=\"lowest_5_percent:Q\",\n",
-    "#         color=alt.Color(\"month(timestamp):N\", scale=alt.Scale(scheme=\"category10\")),\n",
-    "#         tooltip=[\"timestamp:T\", \"lowest_1_percent:Q\", \"lowest_5_percent:Q\"],\n",
-    "#     )\n",
-    "#     .properties(width=400, height=400, title=\"Lowest 1% vs Lowest 5%\")\n",
-    "# )\n",
-    "\n",
-    "# # Combine charts\n",
-    "# combined_chart = (standby_comparison & percentage_comparison) | (box_plot & scatter_1_5)\n",
-    "\n",
-    "# # Display the combined chart\n",
-    "# combined_chart.display()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import pandas as pd\n",
-    "\n",
-    "# import plotly.express as px\n",
-    "# #\n",
-    "# # Create a histogram\n",
-    "# fig = px.histogram(\n",
-    "#     df,\n",
-    "#     x=\"timestamp\",\n",
-    "#     histfunc=\"avg\",\n",
-    "#     y=[\"total\", \"lowest_1_percent\"],\n",
-    "#     title=\"Energy use\",\n",
-    "#     labels={\n",
-    "#         \"total\": \"Energy use (kWh)\",\n",
-    "#         \"min_power_usage_per_day\": \"Min Power Usage per Day (kWh)\",\n",
-    "#     },\n",
-    "#     barmode=\"overlay\",\n",
-    "# )\n",
-    "# fig.update_traces(xbins_size=\"604800000\")\n",
-    "\n",
-    "# fig.show()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1593,7 +1547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1604,6 +1558,14 @@
       "An exception has occurred, use %tb to see the full traceback.\n",
       "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m Stopping the notebook execution here.\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/root/.cache/pypoetry/virtualenvs/openenergyid-Nm3FK_LY-py3.11/lib/python3.11/site-packages/IPython/core/interactiveshell.py:3585: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
+      "  warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
+     ]
     }
    ],
    "source": [
@@ -1646,7 +1608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1829,7 +1791,7 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "# Read in pandas series from a json file\n",
-    "energy_use_lf_1 = pd.read_json(\"energy_use.json\", orient=\"index\")\n",
+    "energy_use_lf_1 = pd.read_json(\"data/PP/energy_use.json\", orient=\"index\")\n",
     "energy_use_lf_1.columns = [\"energy_use\"]\n",
     "energy_use_lf_1.Name = \"energy_use\"\n",
     "display(energy_use_lf_1)\n",
@@ -1975,7 +1937,7 @@
     "import numpy as np\n",
     "\n",
     "# Read in pandas series from a json file\n",
-    "energy_use_lf_1 = pd.read_json(\"energy_use.json\", orient=\"index\")\n",
+    "energy_use_lf_1 = pd.read_json(\"data/PP/energy_use.json\", orient=\"index\")\n",
     "energy_use_lf_1.columns = [\"energy_use\"]\n",
     "energy_use_lf_1.Name = \"energy_use\"\n",
     "display(energy_use_lf_1)\n",
@@ -2020,7 +1982,7 @@
     "\n",
     "# Load and preprocess the data\n",
     "\n",
-    "data = pd.read_json(\"energy_use.json\", orient=\"index\")\n",
+    "data = pd.read_json(\"data/PP/energy_use.json\", orient=\"index\")\n",
     "data.columns = [\"usage\"]\n",
     "data.index.name = \"timestamp\"\n",
     "data.index = pd.to_datetime(data.index)"
diff --git a/openenergyid/baseload/__init__.py b/openenergyid/baseload/__init__.py
new file mode 100644
index 0000000..cbf7ffb
--- /dev/null
+++ b/openenergyid/baseload/__init__.py
@@ -0,0 +1,15 @@
+"""Base Load analysis for Open Energy ID."""
+
+from .main import (
+    BaseLoadMetrics,
+    EnergySchema,
+    load_data,
+    calculate_base_load,
+)
+
+__all__ = [
+    "BaseLoadMetrics",
+    "EnergySchema",
+    "load_data",
+    "calculate_base_load",
+]
diff --git a/openenergyid/baseload/main.py b/openenergyid/baseload/main.py
new file mode 100644
index 0000000..b02f6bf
--- /dev/null
+++ b/openenergyid/baseload/main.py
@@ -0,0 +1,114 @@
+"""
+This module provides functionality for loading, validating, and analyzing energy usage data.
+
+Classes:
+    BaseLoadMetrics: A NamedTuple container for base load analysis metrics.
+    EnergySchema: A pandera DataFrameModel for validating energy usage data.
+
+Functions:
+    load_data(path: str) -> pl.LazyFrame:
+        Loads and validates energy usage data from an NDJSON file.
+
+    calculate_base_load(lf: pl.LazyFrame) -> BaseLoadMetrics:
+        Calculates base load metrics from energy usage data.
+
+    main(file_path: str) -> BaseLoadMetrics:
+        Processes energy data and returns base load metrics.
+
+    test_energy_validation():
+        Tests various data validation scenarios using pytest.
+"""
+
+from typing import NamedTuple
+import polars as pl
+import pandera.polars as pa
+## VERY important to use pandera.polars instead of pandera to avoid pandas errors
+
+
+class BaseLoadMetrics(NamedTuple):
+    """Container for base load analysis metrics"""
+
+    base_load_watts: float  # Average base load in watts
+    daily_usage_kwh: float  # Average daily usage in kWh
+    base_percentage: float  # Base load as percentage of total
+
+
+class EnergySchema(pa.DataFrameModel):
+    """Schema for energy usage data validation"""
+
+    timestamp: pl.Datetime = pa.Field(
+        nullable=False,
+        coerce=True,
+        title="Measurement Timestamp",
+        description="Time of energy measurement in Europe/Brussels timezone",
+    )
+    total: float = pa.Field(
+        ge=0,  # Power should be non-negative
+        nullable=False,
+        title="Total Power",
+        description="Total power measurement in kW",
+    )
+
+    # Add example of pandera validation: dataframe-level validation
+    @pa.dataframe_check
+    def timestamps_are_ordered(self, data: pl.DataFrame) -> bool:
+        """Check if timestamps are in chronological order"""
+        return data["timestamp"].is_sorted()
+
+
+def load_data(path: str) -> pl.LazyFrame:
+    """Load and validate energy usage data from NDJSON file"""
+    lf = pl.scan_ndjson(
+        path,
+        schema={"timestamp": pl.Datetime(time_zone="Europe/Brussels"), "total": pl.Float64},
+    )
+    # Convert to DataFrame for data-level validation, then back to LazyFrame for processing
+    validated_df = EnergySchema.validate(lf).collect()  # type: ignore
+    return pl.LazyFrame(validated_df)
+
+
+def calculate_base_load(lf: pl.LazyFrame) -> BaseLoadMetrics:
+    """
+    Calculate base load metrics from energy usage data.
+
+    Takes lowest 10 totals per day to determine base load.
+    Returns watts, kwh, and percentage metrics.
+    """
+    metrics_df = (
+        lf.filter(pl.col("total") >= 0)
+        .sort("timestamp")
+        .group_by_dynamic("timestamp", every="1d")
+        .agg(
+            [
+                pl.col("total").sum().alias("total_daily_usage"),
+                (pl.col("total").sort().head(10).mean() * 4 * 24).alias("base_load_daily_kwh"),
+            ]
+        )
+        .with_columns(
+            [
+                (pl.col("base_load_daily_kwh") / pl.col("total_daily_usage") * 100).alias(
+                    "base_percentage"
+                )
+            ]
+        )
+        .select(
+            [
+                pl.col("base_load_daily_kwh").mean().alias("avg_daily_kwh"),
+                (pl.col("base_load_daily_kwh") * 1000 / 24).mean().alias("avg_watts"),
+                pl.col("base_percentage").mean().alias("avg_percentage"),
+            ]
+        )
+        .collect()  # TODO add validation for input data: correct format, not null, etc.
+    )
+
+    return BaseLoadMetrics(
+        base_load_watts=metrics_df[0, "avg_watts"],
+        daily_usage_kwh=metrics_df[0, "avg_daily_kwh"],
+        base_percentage=metrics_df[0, "avg_percentage"],
+    )
+
+
+def main(file_path: str) -> BaseLoadMetrics:
+    """Process energy data and return base load metrics"""
+    lf = load_data(file_path)
+    return calculate_base_load(lf)
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