Updated Notebook comments

Notebooks/70_Server_result_analysis.ipynb

@@ -169,6 +169,14 @@
     "### Compute the time index"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0a39bb25-f673-488b-99a4-57ee0d789f54",
+   "metadata": {},
+   "source": [
+    "The time index is computed by adding the elapsed time (`sample nr.` * `Tsample`) to the dataset start time. Since the CARNOT weather set represents the year 2010, the starting time is taken as 2010-01-01 at midnight."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 14,
@@ -278,6 +286,14 @@
     "### Get reference temperature"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "8b53a7c0-966f-4c75-8857-1a96ec3135b0",
+   "metadata": {},
+   "source": [
+    "The reference temperature is computed according to the SIA norm, as a function of the last 48h of outside temperature (at a `Tsample` of 15min this comes out to 2 * 96 sample points)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -292,6 +308,14 @@
     "df_tref = df_tref.shift(1) # The reference at time t is computed using info up to t-1"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "5d2e03c9-044a-454b-8a6b-c1a2a6adf6cc",
+   "metadata": {},
+   "source": [
+    "Compute mean value and standard deviation of tracking error:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 17,
@@ -358,6 +382,14 @@
     "}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b0fc6fbc-ed75-4724-a5cb-8896a89f5039",
+   "metadata": {},
+   "source": [
+    "Scale the dataset and compute the autoregressive inputs that are passed to the GP:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 20,
@@ -548,6 +580,14 @@
     "df_output = df_gpr[dict_cols['y'][1]]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "ddbfd08f-7d5d-4f2e-b43e-a282a0503931",
+   "metadata": {},
+   "source": [
+    "Load the only trained model in the GP case, and the first trained model in the SVGP case:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 23,
@@ -571,6 +611,14 @@
     "    m = model"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c18135f0-06d8-44e3-b86c-4a84b2508f79",
+   "metadata": {},
+   "source": [
+    "Plot the multistep prediction performance for 25 consecutive points:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 25,
@@ -1091,6 +1139,14 @@
     "output_notebook()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "3ec059bc-9ce6-4271-b5cd-5003d1330a0b",
+   "metadata": {},
+   "source": [
+    "### Plot evolution of reference/measured temperature"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 22,
@@ -1393,6 +1449,14 @@
     "plt.savefig(f\"../Thesis/Plots/{sim_id}_abserr.pdf\", bbox_inches='tight')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4b0595d2-59d7-4536-b8a3-8c8aced19dc4",
+   "metadata": {},
+   "source": [
+    "### Plot evolution of hyperparameters"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 29,