diff --git a/Notebooks/70_Server_result_analysis.ipynb b/Notebooks/70_Server_result_analysis.ipynb
index 680ab0e..fd3c302 100644
--- a/Notebooks/70_Server_result_analysis.ipynb
+++ b/Notebooks/70_Server_result_analysis.ipynb
@@ -29,17 +29,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pickle"
+    "import pickle\n",
+    "from pathlib import Path"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "id": "612d3146-a2c4-4018-9573-edddc307c834",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from helpers import get_scaled_df, data_to_gpr"
+    "from helpers import get_scaled_df, data_to_gpr, ScalerHelper"
    ]
   },
   {
@@ -54,20 +55,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 29,
    "id": "27456734-f026-429f-a10e-b27142a46b49",
    "metadata": {},
    "outputs": [],
    "source": [
-    "scaler = pickle.load(open(\"controller_scaler.pkl\", 'rb'))\n",
-    "model = pickle.load(open(\"controller_model.pkl\", 'rb'))\n",
-    "df = pd.read_pickle(\"controller_df.pkl\")\n",
-    "X_log = pickle.load(open(\"controller_X_log.pkl\", 'rb'))"
+    "sim_path = Path(\"../Data/Simulation_results/Second batch/\", \"4_sparse_GP_500pts_12_averageYear\")\n",
+    "scaler = pickle.load(open(Path(sim_path, \"controller_scaler.pkl\"), 'rb'))\n",
+    "scaler_helper = ScalerHelper(scaler)\n",
+    "model = pickle.load(open(Path(sim_path, \"controller_model.pkl\"), 'rb'))\n",
+    "df = pd.read_pickle(Path(sim_path, \"controller_df.pkl\"))\n",
+    "X_log = pickle.load(open(Path(sim_path,\"controller_X_log.pkl\"), 'rb'))\n",
+    "model_log = pickle.load(open(Path(sim_path,\"controller_model_log.pkl\"), 'rb'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "id": "11fc2b92-5c79-40ff-8c3e-987143858e57",
    "metadata": {},
    "outputs": [],
@@ -92,7 +96,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 31,
+   "id": "c9ff2027-72b0-4218-8ef8-95c6004e62a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "341"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(model_log)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "74de9917-1c6e-46f7-a1c8-e9fbec7d0232",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.60701006])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler_helper.scale_output(21)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "id": "f882da29-3e5c-4262-a88e-7e52c1d7c3a5",
    "metadata": {},
    "outputs": [
@@ -129,112 +175,112 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>3.629534</td>\n",
-       "      <td>-0.043478</td>\n",
-       "      <td>0.467424</td>\n",
-       "      <td>0.468231</td>\n",
-       "      <td>0.814622</td>\n",
-       "      <td>0.821869</td>\n",
-       "      <td>0.829368</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.196272</td>\n",
+       "      <td>0.439666</td>\n",
+       "      <td>0.446585</td>\n",
+       "      <td>0.611804</td>\n",
+       "      <td>0.610780</td>\n",
+       "      <td>0.609699</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>3.554404</td>\n",
-       "      <td>0.026087</td>\n",
-       "      <td>0.472246</td>\n",
-       "      <td>0.467424</td>\n",
-       "      <td>0.843619</td>\n",
-       "      <td>0.814622</td>\n",
-       "      <td>0.821869</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.194079</td>\n",
+       "      <td>0.430661</td>\n",
+       "      <td>0.439666</td>\n",
+       "      <td>0.609555</td>\n",
+       "      <td>0.611804</td>\n",
+       "      <td>0.610780</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>3.455959</td>\n",
-       "      <td>0.060870</td>\n",
-       "      <td>0.372524</td>\n",
-       "      <td>0.472246</td>\n",
-       "      <td>0.878315</td>\n",
-       "      <td>0.843619</td>\n",
-       "      <td>0.814622</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.199561</td>\n",
+       "      <td>0.433354</td>\n",
+       "      <td>0.430661</td>\n",
+       "      <td>0.607093</td>\n",
+       "      <td>0.609555</td>\n",
+       "      <td>0.611804</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>3.357513</td>\n",
-       "      <td>0.095652</td>\n",
-       "      <td>0.300814</td>\n",
-       "      <td>0.372524</td>\n",
-       "      <td>0.878323</td>\n",
-       "      <td>0.878315</td>\n",
-       "      <td>0.843619</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.205044</td>\n",
+       "      <td>0.439900</td>\n",
+       "      <td>0.433354</td>\n",
+       "      <td>0.606996</td>\n",
+       "      <td>0.607093</td>\n",
+       "      <td>0.609555</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>3.259067</td>\n",
-       "      <td>0.130435</td>\n",
-       "      <td>0.249037</td>\n",
-       "      <td>0.300814</td>\n",
-       "      <td>0.878327</td>\n",
-       "      <td>0.878323</td>\n",
-       "      <td>0.878315</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.210526</td>\n",
+       "      <td>0.447242</td>\n",
+       "      <td>0.439900</td>\n",
+       "      <td>0.606835</td>\n",
+       "      <td>0.606996</td>\n",
+       "      <td>0.607093</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>3.044041</td>\n",
-       "      <td>0.160870</td>\n",
-       "      <td>0.216228</td>\n",
-       "      <td>0.249037</td>\n",
-       "      <td>0.878321</td>\n",
-       "      <td>0.878327</td>\n",
-       "      <td>0.878323</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.219298</td>\n",
+       "      <td>0.454680</td>\n",
+       "      <td>0.447242</td>\n",
+       "      <td>0.606902</td>\n",
+       "      <td>0.606835</td>\n",
+       "      <td>0.606996</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>2.829016</td>\n",
-       "      <td>0.191304</td>\n",
-       "      <td>0.198559</td>\n",
-       "      <td>0.216228</td>\n",
-       "      <td>0.878326</td>\n",
-       "      <td>0.878321</td>\n",
-       "      <td>0.878327</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.228070</td>\n",
+       "      <td>0.462675</td>\n",
+       "      <td>0.454680</td>\n",
+       "      <td>0.606797</td>\n",
+       "      <td>0.606902</td>\n",
+       "      <td>0.606835</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>2.613990</td>\n",
-       "      <td>0.221739</td>\n",
-       "      <td>0.193012</td>\n",
-       "      <td>0.198559</td>\n",
-       "      <td>0.878315</td>\n",
-       "      <td>0.878326</td>\n",
-       "      <td>0.878321</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.236842</td>\n",
+       "      <td>0.471004</td>\n",
+       "      <td>0.462675</td>\n",
+       "      <td>0.606870</td>\n",
+       "      <td>0.606797</td>\n",
+       "      <td>0.606902</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>2.398964</td>\n",
-       "      <td>0.252174</td>\n",
-       "      <td>0.246331</td>\n",
-       "      <td>0.193012</td>\n",
-       "      <td>0.878319</td>\n",
-       "      <td>0.878315</td>\n",
-       "      <td>0.878326</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.245614</td>\n",
+       "      <td>0.607318</td>\n",
+       "      <td>0.471004</td>\n",
+       "      <td>0.607081</td>\n",
+       "      <td>0.606870</td>\n",
+       "      <td>0.606797</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "          0         1         2         3         4         5         6\n",
-       "0  3.629534 -0.043478  0.467424  0.468231  0.814622  0.821869  0.829368\n",
-       "1  3.554404  0.026087  0.472246  0.467424  0.843619  0.814622  0.821869\n",
-       "2  3.455959  0.060870  0.372524  0.472246  0.878315  0.843619  0.814622\n",
-       "3  3.357513  0.095652  0.300814  0.372524  0.878323  0.878315  0.843619\n",
-       "4  3.259067  0.130435  0.249037  0.300814  0.878327  0.878323  0.878315\n",
-       "5  3.044041  0.160870  0.216228  0.249037  0.878321  0.878327  0.878323\n",
-       "6  2.829016  0.191304  0.198559  0.216228  0.878326  0.878321  0.878327\n",
-       "7  2.613990  0.221739  0.193012  0.198559  0.878315  0.878326  0.878321\n",
-       "8  2.398964  0.252174  0.246331  0.193012  0.878319  0.878315  0.878326"
+       "     0         1         2         3         4         5         6\n",
+       "0 -1.0 -0.196272  0.439666  0.446585  0.611804  0.610780  0.609699\n",
+       "1 -1.0 -0.194079  0.430661  0.439666  0.609555  0.611804  0.610780\n",
+       "2 -1.0 -0.199561  0.433354  0.430661  0.607093  0.609555  0.611804\n",
+       "3 -1.0 -0.205044  0.439900  0.433354  0.606996  0.607093  0.609555\n",
+       "4 -1.0 -0.210526  0.447242  0.439900  0.606835  0.606996  0.607093\n",
+       "5 -1.0 -0.219298  0.454680  0.447242  0.606902  0.606835  0.606996\n",
+       "6 -1.0 -0.228070  0.462675  0.454680  0.606797  0.606902  0.606835\n",
+       "7 -1.0 -0.236842  0.471004  0.462675  0.606870  0.606797  0.606902\n",
+       "8 -1.0 -0.245614  0.607318  0.471004  0.607081  0.606870  0.606797"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -245,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "id": "af6d8095-e98c-4f1a-a145-aa939f4eff7b",
    "metadata": {},
    "outputs": [],
@@ -256,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "id": "b4dc8f43-b3f5-4c8d-bf61-3e54432dccd4",
    "metadata": {},
    "outputs": [
@@ -297,73 +343,73 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.307018</td>\n",
+       "      <td>-0.307018</td>\n",
        "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-0.791304</td>\n",
-       "      <td>-0.791304</td>\n",
-       "      <td>-0.091754</td>\n",
-       "      <td>-0.091754</td>\n",
-       "      <td>-0.091754</td>\n",
-       "      <td>0.476810</td>\n",
-       "      <td>0.556686</td>\n",
-       "      <td>0.677723</td>\n",
        "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.968013</td>\n",
+       "      <td>0.956969</td>\n",
+       "      <td>0.765691</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.307018</td>\n",
+       "      <td>-0.307018</td>\n",
+       "      <td>-0.993927</td>\n",
        "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-0.791304</td>\n",
-       "      <td>-0.791304</td>\n",
-       "      <td>-0.091754</td>\n",
-       "      <td>-0.091754</td>\n",
-       "      <td>-0.091754</td>\n",
-       "      <td>0.412944</td>\n",
-       "      <td>0.476810</td>\n",
-       "      <td>0.556686</td>\n",
-       "      <td>0.677723</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.140832</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.968013</td>\n",
+       "      <td>0.956969</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.321272</td>\n",
+       "      <td>-0.307018</td>\n",
+       "      <td>-0.987854</td>\n",
+       "      <td>-0.993927</td>\n",
        "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-0.821739</td>\n",
-       "      <td>-0.791304</td>\n",
-       "      <td>-0.091754</td>\n",
-       "      <td>-0.091754</td>\n",
-       "      <td>-0.091754</td>\n",
-       "      <td>0.357670</td>\n",
-       "      <td>0.412944</td>\n",
-       "      <td>0.476810</td>\n",
-       "      <td>0.556686</td>\n",
+       "      <td>-0.045578</td>\n",
+       "      <td>0.140832</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.968013</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-0.852174</td>\n",
-       "      <td>-0.821739</td>\n",
-       "      <td>-0.091754</td>\n",
-       "      <td>-0.091754</td>\n",
-       "      <td>-0.091754</td>\n",
-       "      <td>0.308173</td>\n",
-       "      <td>0.357670</td>\n",
-       "      <td>0.412944</td>\n",
-       "      <td>0.476810</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.335526</td>\n",
+       "      <td>-0.321272</td>\n",
+       "      <td>-0.981781</td>\n",
+       "      <td>-0.987854</td>\n",
+       "      <td>-0.993927</td>\n",
+       "      <td>-0.206770</td>\n",
+       "      <td>-0.045578</td>\n",
+       "      <td>0.140832</td>\n",
+       "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-0.882609</td>\n",
-       "      <td>-0.852174</td>\n",
-       "      <td>-0.091754</td>\n",
-       "      <td>-0.091754</td>\n",
-       "      <td>-0.091754</td>\n",
-       "      <td>0.263350</td>\n",
-       "      <td>0.308173</td>\n",
-       "      <td>0.357670</td>\n",
-       "      <td>0.412944</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.349781</td>\n",
+       "      <td>-0.335526</td>\n",
+       "      <td>-0.975709</td>\n",
+       "      <td>-0.981781</td>\n",
+       "      <td>-0.987854</td>\n",
+       "      <td>-0.345365</td>\n",
+       "      <td>-0.206770</td>\n",
+       "      <td>-0.045578</td>\n",
+       "      <td>0.140832</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -380,124 +426,124 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5136</th>\n",
-       "      <td>3.559585</td>\n",
-       "      <td>3.414508</td>\n",
-       "      <td>-0.147826</td>\n",
-       "      <td>-0.204348</td>\n",
-       "      <td>0.491535</td>\n",
-       "      <td>0.513060</td>\n",
-       "      <td>0.538535</td>\n",
-       "      <td>0.839457</td>\n",
-       "      <td>0.849824</td>\n",
-       "      <td>0.863174</td>\n",
-       "      <td>0.875588</td>\n",
+       "      <th>35028</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.219298</td>\n",
+       "      <td>-0.224781</td>\n",
+       "      <td>0.456299</td>\n",
+       "      <td>0.459792</td>\n",
+       "      <td>0.463963</td>\n",
+       "      <td>0.608831</td>\n",
+       "      <td>0.608839</td>\n",
+       "      <td>0.609613</td>\n",
+       "      <td>0.610534</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5137</th>\n",
-       "      <td>3.582902</td>\n",
-       "      <td>3.559585</td>\n",
-       "      <td>-0.113043</td>\n",
-       "      <td>-0.147826</td>\n",
-       "      <td>0.479890</td>\n",
-       "      <td>0.491535</td>\n",
-       "      <td>0.513060</td>\n",
-       "      <td>0.829368</td>\n",
-       "      <td>0.839457</td>\n",
-       "      <td>0.849824</td>\n",
-       "      <td>0.863174</td>\n",
+       "      <th>35029</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.211623</td>\n",
+       "      <td>-0.219298</td>\n",
+       "      <td>0.451822</td>\n",
+       "      <td>0.456299</td>\n",
+       "      <td>0.459792</td>\n",
+       "      <td>0.609699</td>\n",
+       "      <td>0.608831</td>\n",
+       "      <td>0.608839</td>\n",
+       "      <td>0.609613</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5138</th>\n",
-       "      <td>3.606218</td>\n",
-       "      <td>3.582902</td>\n",
-       "      <td>-0.078261</td>\n",
-       "      <td>-0.113043</td>\n",
-       "      <td>0.468231</td>\n",
-       "      <td>0.479890</td>\n",
-       "      <td>0.491535</td>\n",
-       "      <td>0.821869</td>\n",
-       "      <td>0.829368</td>\n",
-       "      <td>0.839457</td>\n",
-       "      <td>0.849824</td>\n",
+       "      <th>35030</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.203947</td>\n",
+       "      <td>-0.211623</td>\n",
+       "      <td>0.446585</td>\n",
+       "      <td>0.451822</td>\n",
+       "      <td>0.456299</td>\n",
+       "      <td>0.610780</td>\n",
+       "      <td>0.609699</td>\n",
+       "      <td>0.608831</td>\n",
+       "      <td>0.608839</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5139</th>\n",
-       "      <td>3.629534</td>\n",
-       "      <td>3.606218</td>\n",
-       "      <td>-0.043478</td>\n",
-       "      <td>-0.078261</td>\n",
-       "      <td>0.467424</td>\n",
-       "      <td>0.468231</td>\n",
-       "      <td>0.479890</td>\n",
-       "      <td>0.814622</td>\n",
-       "      <td>0.821869</td>\n",
-       "      <td>0.829368</td>\n",
-       "      <td>0.839457</td>\n",
+       "      <th>35031</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.196272</td>\n",
+       "      <td>-0.203947</td>\n",
+       "      <td>0.439666</td>\n",
+       "      <td>0.446585</td>\n",
+       "      <td>0.451822</td>\n",
+       "      <td>0.611804</td>\n",
+       "      <td>0.610780</td>\n",
+       "      <td>0.609699</td>\n",
+       "      <td>0.608831</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5140</th>\n",
-       "      <td>3.652850</td>\n",
-       "      <td>3.629534</td>\n",
-       "      <td>-0.008696</td>\n",
-       "      <td>-0.043478</td>\n",
-       "      <td>0.467398</td>\n",
-       "      <td>0.467424</td>\n",
-       "      <td>0.468231</td>\n",
-       "      <td>0.811382</td>\n",
-       "      <td>0.814622</td>\n",
-       "      <td>0.821869</td>\n",
-       "      <td>0.829368</td>\n",
+       "      <th>35032</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.188596</td>\n",
+       "      <td>-0.196272</td>\n",
+       "      <td>0.431396</td>\n",
+       "      <td>0.439666</td>\n",
+       "      <td>0.446585</td>\n",
+       "      <td>0.612204</td>\n",
+       "      <td>0.611804</td>\n",
+       "      <td>0.610780</td>\n",
+       "      <td>0.609699</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5138 rows × 11 columns</p>\n",
+       "<p>35030 rows × 11 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "        SolRad  SolRad_1  OutsideTemp  OutsideTemp_1  SimulatedHeat  \\\n",
-       "3    -1.000000 -1.000000    -0.791304      -0.791304      -0.091754   \n",
-       "4    -1.000000 -1.000000    -0.791304      -0.791304      -0.091754   \n",
-       "5    -1.000000 -1.000000    -0.821739      -0.791304      -0.091754   \n",
-       "6    -1.000000 -1.000000    -0.852174      -0.821739      -0.091754   \n",
-       "7    -1.000000 -1.000000    -0.882609      -0.852174      -0.091754   \n",
-       "...        ...       ...          ...            ...            ...   \n",
-       "5136  3.559585  3.414508    -0.147826      -0.204348       0.491535   \n",
-       "5137  3.582902  3.559585    -0.113043      -0.147826       0.479890   \n",
-       "5138  3.606218  3.582902    -0.078261      -0.113043       0.468231   \n",
-       "5139  3.629534  3.606218    -0.043478      -0.078261       0.467424   \n",
-       "5140  3.652850  3.629534    -0.008696      -0.043478       0.467398   \n",
+       "       SolRad  SolRad_1  OutsideTemp  OutsideTemp_1  SimulatedHeat  \\\n",
+       "3        -1.0      -1.0    -0.307018      -0.307018      -1.000000   \n",
+       "4        -1.0      -1.0    -0.307018      -0.307018      -0.993927   \n",
+       "5        -1.0      -1.0    -0.321272      -0.307018      -0.987854   \n",
+       "6        -1.0      -1.0    -0.335526      -0.321272      -0.981781   \n",
+       "7        -1.0      -1.0    -0.349781      -0.335526      -0.975709   \n",
+       "...       ...       ...          ...            ...            ...   \n",
+       "35028    -1.0      -1.0    -0.219298      -0.224781       0.456299   \n",
+       "35029    -1.0      -1.0    -0.211623      -0.219298       0.451822   \n",
+       "35030    -1.0      -1.0    -0.203947      -0.211623       0.446585   \n",
+       "35031    -1.0      -1.0    -0.196272      -0.203947       0.439666   \n",
+       "35032    -1.0      -1.0    -0.188596      -0.196272       0.431396   \n",
        "\n",
-       "      SimulatedHeat_1  SimulatedHeat_2  SimulatedTemp  SimulatedTemp_1  \\\n",
-       "3           -0.091754        -0.091754       0.476810         0.556686   \n",
-       "4           -0.091754        -0.091754       0.412944         0.476810   \n",
-       "5           -0.091754        -0.091754       0.357670         0.412944   \n",
-       "6           -0.091754        -0.091754       0.308173         0.357670   \n",
-       "7           -0.091754        -0.091754       0.263350         0.308173   \n",
-       "...               ...              ...            ...              ...   \n",
-       "5136         0.513060         0.538535       0.839457         0.849824   \n",
-       "5137         0.491535         0.513060       0.829368         0.839457   \n",
-       "5138         0.479890         0.491535       0.821869         0.829368   \n",
-       "5139         0.468231         0.479890       0.814622         0.821869   \n",
-       "5140         0.467424         0.468231       0.811382         0.814622   \n",
+       "       SimulatedHeat_1  SimulatedHeat_2  SimulatedTemp  SimulatedTemp_1  \\\n",
+       "3             1.000000         1.000000       1.000000         0.968013   \n",
+       "4            -1.000000         1.000000       0.140832         1.000000   \n",
+       "5            -0.993927        -1.000000      -0.045578         0.140832   \n",
+       "6            -0.987854        -0.993927      -0.206770        -0.045578   \n",
+       "7            -0.981781        -0.987854      -0.345365        -0.206770   \n",
+       "...                ...              ...            ...              ...   \n",
+       "35028         0.459792         0.463963       0.608831         0.608839   \n",
+       "35029         0.456299         0.459792       0.609699         0.608831   \n",
+       "35030         0.451822         0.456299       0.610780         0.609699   \n",
+       "35031         0.446585         0.451822       0.611804         0.610780   \n",
+       "35032         0.439666         0.446585       0.612204         0.611804   \n",
        "\n",
-       "      SimulatedTemp_2  SimulatedTemp_3  \n",
-       "3            0.677723         1.000000  \n",
-       "4            0.556686         0.677723  \n",
-       "5            0.476810         0.556686  \n",
-       "6            0.412944         0.476810  \n",
-       "7            0.357670         0.412944  \n",
-       "...               ...              ...  \n",
-       "5136         0.863174         0.875588  \n",
-       "5137         0.849824         0.863174  \n",
-       "5138         0.839457         0.849824  \n",
-       "5139         0.829368         0.839457  \n",
-       "5140         0.821869         0.829368  \n",
+       "       SimulatedTemp_2  SimulatedTemp_3  \n",
+       "3             0.956969         0.765691  \n",
+       "4             0.968013         0.956969  \n",
+       "5             1.000000         0.968013  \n",
+       "6             0.140832         1.000000  \n",
+       "7            -0.045578         0.140832  \n",
+       "...                ...              ...  \n",
+       "35028         0.609613         0.610534  \n",
+       "35029         0.608839         0.609613  \n",
+       "35030         0.608831         0.608839  \n",
+       "35031         0.609699         0.608831  \n",
+       "35032         0.610780         0.609699  \n",
        "\n",
-       "[5138 rows x 11 columns]"
+       "[35030 rows x 11 columns]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -516,7 +562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 33,
    "id": "cf601d07-0fff-4e2f-8ac8-9169f749b399",
    "metadata": {},
    "outputs": [],
@@ -527,35 +573,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 82,
    "id": "7f1858cc-c54b-4fb1-9f96-6f6e1e0ab9e4",
    "metadata": {},
    "outputs": [],
    "source": [
-    "start_idx = 600\n",
+    "start_idx = 500\n",
     "nb_predictions = 25\n",
     "N_pred = 20"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 83,
    "id": "ea0a68ba-c4f4-463f-9ee1-474461086ebf",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fca6d2dbe80>]"
+       "[<matplotlib.lines.Line2D at 0x7f77cc949a30>]"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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OHDgwirBE9t+mTTt4/fVazjtvpN+hiPSYaBL9KmB4h9fDgDUdL3DO1TvntoU+nw30MrMB0dwr4qdZsz6mpcVx7rlK9BJc0ST6d4CRZjbCzDKBi4EXO15gZkPMzEKfnxD6upuiuVfET88//xHDh/fjuOMG+x2KSI/p8oEp51yzmV0PzAHSgcedc0vN7JrQ+SnABcC1ZtYM7AAuds45wPPeHvpZRLpl27Ym5s6tpqjoKEL9FJFAiurJ2FA5ZnanY1M6fP4w8HC094okgpdf/pTGxmbNtpHA05OxkrJmzFjJgAG9GTt2mN+hiPQoJXpJSU1NLcya9TFnnXUYGRn6ZyDBpt9wSUmvvlpDfX2TplVKSlCil5Q0Y8ZH9O3bi9NOy/c7FJEep0QvKaWsrJL8/D9TWvo+ra2O6dM/9DskkR6n9eglZZSVVVJUNHf3JuDbtzdTVDQXQBuMSKCpRy8po7h4/u4k32779maKi+f7FJFIfCjRS8qoqanv1nGRoFCil5SRl+e9qUik4yJBoUQvKaOkZCwZGXsudZCTk0FJyVifIhKJDyV6SRkXXXQkvXtnkJOTgRnk5+dSWjpeA7ESeJp1Iynjtddq2bp1F9Onn6VtAyWlqEcvKaO8fDn9+mVy5pkj/A5FJK6U6CUlNDW1MH36h5xzzuH07t3L73BE4kqJXlLC3LlVbN68k4svPtLvUETiToleUkJ5+XL698/mW9/S2jaSepToJfC2b9/FCy+s5PzzjyAzM93vcETiToleAm/27E/Ytm2XyjaSspToJfDKy5czeHAO3/ymdpKS1KREL4G2dWsTL730KRdeOIr0dP26S2rSb74E2osvrqSxsVllG0lpSvQSaOXlyxk+vB8nnXSo36GI+EaJXgKrrm4Hc+ZUcdFFo0hLs65vEAkoJXoJrBkzVrJrV6vKNpLylOglcMrKKikoKOVHP5pDRoaxfHmd3yGJ+EqrV0qgdN4XtrnZaV9YSXnq0UugaF9YkXBK9BIo2hdWJJwSvQSK9oUVCadEL4HSti/snr/W2hdWUp0SvQTKJZd8idzcXmRnp2tfWJGQqGbdmNkE4AEgHZjqnPtdhOuOB94GLnLOPRc6VgVsBVqAZudcYQziFvH0+uu11NXtZNq073DJJV/yOxyRhNBlojezdOAR4HRgFfCOmb3onKv0uO4eYI7HlznFOfdZDOIV2aunn15Kbm4m55xzuN+hiCSMaEo3JwArnXOfOOeagHLgbI/rbgCmAxtiGJ9I1BoamnjuuQ+58MJR2hdWpINoEv1QoLbD61WhY7uZ2VDgXGCKx/0OmGtmC82sKNI3MbMiM6sws4qNGzdGEdae2p+GTEu7l4KCUsrKKru+SQJlxoyVbNu2i8svVz1epKNoEr3XalCu0+v7gdudcy0e157snDsWOBP4iZl9w+ubOOdKnXOFzrnCgQMHRhHWF9qfhqyursc5qK6up6ho7u5krzeB1PD000spKMhl7FhtMCLSUTSDsauA4R1eDwPWdLqmECg3M4ABwLfNrNk5N9M5twbAObfBzGbQVgp6Y78j7yDS05A33/waH3+8mf/+73/T2Nj2HtT+JgB6JD5IVq/eyiuvVHPnnSdppUqRTqLp0b8DjDSzEWaWCVwMvNjxAufcCOdcgXOuAHgOuM45N9PM+phZPwAz6wOMB5bE9Ccg8lOPGzZs5z//863dSb7d9u3NTJo0D1BvPyjKypbhHFx2md68RTrrskfvnGs2s+tpm02TDjzunFtqZteEznvV5dsNBmaEevoZwDTn3Mv7H/ae8vJyqa4OT/aDB+ewYcN2XOdCE1BTs5XvfW86r7xSo95+knPO8dRTS/na1w7l8MMP8jsckYQT1QNTzrnZzrkjnHOHOedKQsemeCV559yV7XPoQzN1jg59jGm/N9ZKSsaSk7Pne1ZOTgZ//OO4iI++Z2enM2vWp569fS2AlVwWLVpPZeUmLr98jN+hiCSkQDwZO3HiaEpLx5Ofnxv2NGSkN4GpU8/AIpRytQBWcnn66UqystL5/vdH+R2KSEIKzHr0EyeO9iy3tB8rLp5PTU09eXm5lJSMZeLE0RQXz/cs+fTvn41zDov0TiAJY9euFqZNW8ZZZx3GQQdl+x2OSEIKRI++KxMnjqaqqojW1lupqiranfy9evtpacamTY18+9vTefDBhRqoTXB///unfPbZDpVtRPYiMD36feHV2//tb8fy+eeN3HLLv3j55ard12qgNrGUlVXu/ossLc2oq9vhd0giCcuc15QUnxUWFrqKigpfYxg6dApr1mwLO56fn0tVVcQHfCUOOm8XCG3jLlqlUlKZmS2MtGhkSpRu9sXateFJHjRQmwi0XaBI9yjRRxBpWuaQIX3iHIl0pu0CRbpHiT4Cr4FagLq6Rm677TUN0vpI2wWKdI8SfQRec/MffPBUhgzJ4d57KyIuoCY9r6RkLOnpe0591XaBIpFpMLab8vL+TG3t1rDjGqSNn+rqLRQUPEZubiZbtzbt8WyESKra22BsSk+v3BerVoUneVB9OJ6mTFlMWprx/vtXkJ9/gN/hiCQ8lW66KVIdeNiwfnGOJDU1Njbz2GMfcPbZhyvJi0RJib6bIg3S5uZm0tDQ5ENEqaW8fDmbNu3g+uu/4ncoIklDib6bvAZpr776KJYtq2PChOnU1+/0O8TAcs7x0EPvMnr0wZxyyvCubxARQDX6feK1gNppp+Vz6aUvccwxT7FrVyurV2/TIGGMvf32WhYtWs+jj35LC86JdIMSfYxceOEo3n57LZMnfzFbSOvjxNbDD79Lbm6mdpES6SaVbmJo+vQPw47p0fzYWLeugWefXcEPfvBl+vbN9DsckaSiRB9DejS/55SWLmbXrlauu+4Yv0MRSTpK9DEUaerloYf2jXMkwbJrVwtTpixmwoQCjjiiv9/hiCQdJfoYijT1sqmpRb36fVBWVklBQSlZWfexdm0DY8YM8DskkaSkRB9DXlMv77rrazQ1tfLNb5ZTVbXF7xCTRvua8+1rCgH86U/vaU0hkX2gtW7ioKJiHaef/ixpaZCd3Yu1azX1sisFBaWe+/lqTSERb9p4xGeFhUO49dZC6up2smbNNq16GQUNbIvEjhJ9nDz22AdhxzT1MjKtOS8SO0r0caIeavdozXmR2FGijxNNveyeL395AC0tjgMOyNw9sK3Nv0X2jZZAiJOSkrEUFc0N29S6sbGZ6uotWnK3k7vu+v8ccEAWVVU/5sADs/0ORySpqUcfJ15TL3/zm5NpaXGccsozKuF08N57G5gx4yNuvPFYJXmRGND0Sp+9885aTj/9OTIz08jMTGfNGk29POecmbz2Wq168yLdoOmVCez44w/hppuOY+PGHaxeramX7767nhdeWMnNNx+nJC8SI0r0CeCJJ5aEHUvVqZe//vVbHHhgFj/72XF+hyISGEr0CUBTL9ssXLiOF1/8mFtuKeSAA7L8DkckMKJK9GY2wcxWmNlKM/vFXq473sxazOyC7t6byrTheJtf//otDjoom5/+9Fi/QxEJlC4TvZmlA48AZwKjgUvMLGyUMHTdPcCc7t6b6iKtetm3by927NjlQ0Tx075CZVravcya9QmnnZZHbq568yKxFE2P/gRgpXPuE+dcE1AOnO1x3Q3AdGDDPtyb0rymXl577dEsX17HBRe8SFNTi98h9givFSpfeumTlByEFulJ0TwwNRSo7fB6FXBixwvMbChwLnAqcHx37u3wNYqAIoC8vLwowgoWrw3HjzlmEFdf/Q++/vW/sW5dA7W1WwM19bK4eH7YA2Q7drQNQgfh5xNJFNH06M3jWOfJ9/cDtzvnOnc9o7m37aBzpc65Qudc4cCBA6MIK/iKio7mkkuOZMGCddTUbA3c1EsNQovERzSJfhUwvMPrYcCaTtcUAuVmVgVcADxqZudEea/sxVtvhTdXUKZeaoVKkfiIJtG/A4w0sxFmlglcDLzY8QLn3AjnXIFzrgB4DrjOOTczmntl74Lc6/3lL78adkwrVIrEXpeJ3jnXDFxP22yaZcAzzrmlZnaNmV2zL/fuf9ipI8i93uXL6wAYMqSPVqgU6UFRrV7pnJsNzO50bEqEa6/s6l6JXqRVL7/3vf/jU0SxsXz5Jh54YBE/+tF/8NhjZ/gdjkig6cnYBNd56uXw4f0YNeog/vzn95k7t8rv8PaJc44bb/wXffr0UplGJA60Hn0S6Dz1cvPmRsaN+3+ce+5MXn31Ik488RAfo+u+WbM+Yc6cKu677xQGDerjdzgigadlipPUunUNnHzyNNavbyA3N4t16xqSYo79zp3NjBnzJJmZaSxefAW9eqX7HZJIIGiZ4gAaMqQP11//FRoamlm7tiFp5tjfd99CPv54Mw88cKqSvEicKNEnsQceWBR2LFHn2JeVVTJs2BTuuGMevXtnsGHDdr9DEkkZqtEnsWSZY9++pk37zKEdO5opKpoLkNBlJpGgUI8+iUWaSz9kSGINcHqtaZOof3mIBJESfRLzWt7YDLZs2cmCBWt9iipcsvzlIRJUSvRJzGt548mTxzF4cB9OO+0ZfvnL+bvXei8oKPVlkLaxsZlevbx/zYLwdK9IMtD0ygBavXorJ5zwf1mzpmGP4zk5GXFfYuDHP57D1KkfkJWVzs6dXyxu6kcsIkGm6ZUpZujQfqSlha8QHe+6+OOPf8DUqR8wadKJ/OUvZ+zxl4eSvEj8aNZNQK1evc3zeLzq4osWree6617hW9/K5+67TyY9PU2JXcQn6tEHVKT696GH9u3x711Xt4Pzz3+BQYNymDbtO6Sn69dMxE/6FxhQkTYc//zzRv7nfz7eY1PuWA3UlpVVkp//Zw4++BGqquq56qr/YODAnP3+uiKyf1S6Caj2Mklx8XxqaurJy8vlZz87lr/+tZKzzppBRkYazc2twBdLJ3S8r7s6PxQF8PvfL+Dwww9UyUbEZ5p1k2IaG5sZOPARtm3bFXYuPz+Xqqqiffq6w4dPYdWq8HGB/fmaIhI9zbqR3bKzM2hoCE/y8MVAbXfKOs45ysoqPZN8x68pIv5R6SYF5eXlUl3tnYDHjp1GRcX63XPeO5d1ysoqd5eDDjmkL/37Z7NkyWdkZqbR1NTq+b1ExF/q0acgr4HarKx0xo0bzltvrdnjwSZom39/882vcf/9FRQVzaW6uh7nYM2abSxZ8hmXXz6aqVPPCPua2uhbJDEo0acgr6UT/vKXM3j11Ysi3rNhw3Zuuum1sMXJAF5/fRWXXTYm7GvqoSiRxKDBWNlDQUGpZ1ln0KCciGvIm0Fr6609HZqI7IUGYyVqXmWdnJwMJk8eR36+d71ddXiRxKZEL3vwKuu0l2AivQmoDi+S2FS6kW7pOOsmGTYjF0kVeyvdKNGLiASAavQiIilMiV5EJOCU6EVEAk6JXkQk4JToRUQCLiFn3ZjZRqDa7zh8MgD4zO8gEozaxJvaJVwqt0m+c26g14mETPSpzMwqIk2RSlVqE29ql3BqE28q3YiIBJwSvYhIwCnRJ55SvwNIQGoTb2qXcGoTD6rRi4gEnHr0IiIBp0QvIhJwSvRxZmZVZvaBmb1nZhWhYxea2VIzazWzwk7X32FmK81shZmd4U/UPS9Cu/zBzJab2ftmNsPMDuxwfeDbJUKb/CbUHu+Z2VwzO7TD9SnZJh3O3WpmzswGdDgW+DaJinNOH3H8AKqAAZ2OfQkYBbwGFHY4PhpYDGQBI4CPgXS/f4Y4tst4ICP0+T3APanULhHaJLfD5z8FpqR6m4SODwfm0Pag5YBUapNoPtSjTwDOuWXOuRUep84Gyp1zO51znwIrgRPiG51/nHNznXPtu5G/DQwLfZ6y7eKc67ihbx+gfTZFyrZJyH3Az/miPUBtspsSffw5YK6ZLTSzoi6uHQrUdni9KnQsiLpqlx8Cfw99nirt4tkmZlZiZrXAROBXocMp2yZmdhaw2jm3uNO1qdImXcro+hKJsZOdc2vMbBDwDzNb7px7I8K15nEsqPNhI7aLmRUDzUBZ6NpUaRfPNnHOFQPFZnYHcD3wn6RwmwDFtJX5OkuVNumSevRx5pxbE/rvBmAGe/9TchVttcd2w4A1PRedfyK1i5ldAXwXmOhChVdSpF2i+F2ZBpwf+jxV2+SbtNXfF5tZFW0/9yIzG0KKtEk0lOjjyMz6mFm/9s9p64Us2cstLwIXm1mWmY0ARgILej7S+IrULmY2AbgdOMs5t73DLYFvl720ycgOl50FLA99nqpt8o5zbpBzrsA5V0Bbcj/WObeOFGiTaKl0E1+DgRlmBm1tP80597KZnQs8BAwEXjKz95xzZzjnlprZM0AlbaWLnzjnWvwKvgdFapeVtM2Y+Efo3NvOuWtSpF0itcl0MxsFtNI2w+QagFRuk0gXp0ibREVLIIiIBJxKNyIiAadELyIScEr0IiIBp0QvIhJwSvQiIgGnRC8iEnBK9CIiAfe/b6NZoLUJx8MAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -573,23 +619,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 90,
    "id": "466c5e48-c0f1-4388-8509-201bdae017ad",
    "metadata": {},
    "outputs": [],
    "source": [
-    "m = model"
+    "m = model_log[-1]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 91,
    "id": "6e46108b-f600-4ed7-bfd2-ca1d50d44dcf",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "text/plain": [
+       "Text(0.5, 1.0, 'Prediction over 20 steps')"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -601,7 +657,7 @@
     }
    ],
    "source": [
-    "plt.figure()\n",
+    "fig = plt.figure()\n",
     "\n",
     "y_name = dict_cols['y'][1][0]\n",
     "for idx in range(start_idx, start_idx + nb_predictions):\n",
@@ -618,7 +674,73 @@
     "    plt.plot(df_iter.index, mean_iter.numpy(), '.-', label = 'predicted', color = 'orange')\n",
     "plt.plot(df_output.iloc[start_idx:start_idx + nb_predictions + N_pred], 'o-', label = 'measured', color = 'darkblue')\n",
     "plt.title(f\"Prediction over {N_pred} steps\")\n",
-    "plt.savefig(f\"prediction_{N_pred}_steps.png\")"
+    "#plt.ylim([0.55, 0.65])\n",
+    "#plt.savefig(f\"prediction_{N_pred}_steps.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "9d3635fb-cd7d-474e-aa6d-161ba89c5178",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([9.78609652, 8.55323373, 2.90362544, 4.93180936, 2.90045941,\n",
+       "       3.29581621, 6.82360381])"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gpflow.utilities.parameter_dict(m)['.kernel.lengthscales'].numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "10b690d6-3a28-452e-81fe-d99757da6fee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[17.2, 17.2, 17.2, 17.2, 24.2]])"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.params = np.empty((1, 5))\n",
+    "np.params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "632bc0e2-745b-40c8-846b-842e5f2eba52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([20.88013634, 21.10070421])"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler_helper.inverse_scale_output([0.5975, 0.6150])"
    ]
   },
   {