feat: add more noise to rooftop example

Author: Hugh Cutcher

docs/notebooks/3.4 Rooftop PV Tuning.ipynb

@@ -36,10 +36,7 @@
    "id": "bba9a869-8fbe-450e-8a16-9cff24613e73",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# !pip install solcast plotly\n",
-    "# !pip install -U kaleido"
-   ]
+   "source": "# !pip install solcast plotly kaleido"
   },
   {
    "cell_type": "code",
@@ -151,7 +148,7 @@
     "    api_key: str,\n",
     "):\n",
     "    pre_tuned = get_rooftop_data(\n",
-    "        latitude, longitude, site_parameter_estimates, months, period, api_key\n",
+    "        latitude, longitude, site_parameter_estimates, months, PERIOD, api_key\n",
     "    ).tz_convert(None)\n",
     "    pre_tuned.columns = [\"solcast_pretuned_estimate\"]\n",
     "    return pre_tuned\n",
@@ -202,7 +199,7 @@
     "            site_parameter_estimates[\"capacity\"],\n",
     "            args=(\n",
     "                est[\"pv_power_rooftop\"].loc[meas.index].values,\n",
-    "                meas[power_column_name].values,\n",
+    "                meas[PV_POWER_COLUMN].values,\n",
     "            ),\n",
     "        )\n",
     "        capacity_est = np.round(res.x[0] * site_parameter_estimates[\"capacity\"], 0)\n",
@@ -346,7 +343,9 @@
    "id": "b881add6-c456-4955-96ea-3104af0a22a0",
    "metadata": {},
    "source": [
-    "## 1a: Input user Data"
+    "## 1a: Input user Data\n",
+    "\n",
+    "Replace the variables in the following cell with your own values"
    ]
   },
   {
@@ -356,28 +355,28 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "api_key = os.environ[\"API_KEY\"]\n",
+    "API_KEY = os.environ[\"API_KEY\"]\n",
     "measurement_data = pd.read_csv(\n",
     "    \"https://solcast.github.io/solcast-api-python-sdk/notebooks/data/3.4_sample_measurements.csv\",\n",
     "    index_col=[0],\n",
     "    parse_dates=True,\n",
     ")  # Make sure this is in kW\n",
-    "power_column_name = (\n",
+    "PV_POWER_COLUMN = (\n",
     "    \"pv_power_rooftop\"  # Set this to the name of the power column in measurement_data\n",
     ")\n",
-    "longitude = 151.215297\n",
-    "latitude = -33.856784\n",
-    "period = \"PT60M\"\n",
+    "LONGITUDE = 151.215297\n",
+    "LATITUDE = -33.856784\n",
+    "PERIOD = \"PT60M\"\n",
     "\n",
     "# Set this to true if User data is in local timezone and not UTC\n",
-    "local_timezone = False\n",
+    "LOCAL_TIMEZONE = False\n",
     "\n",
     "# Optional inputs. Only supply these if you have a high level of confidence they are close to the true value.\n",
-    "user_input_azi = None\n",
-    "user_input_tilt = None\n",
+    "USER_INPUT_AZI = None\n",
+    "USER_INPUT_TILT = None\n",
     "\n",
     "# Save output to csv?\n",
-    "save_to_csv = False"
+    "SAVE_TO_CSV = False"
    ]
   },
   {
@@ -397,10 +396,10 @@
    "source": [
     "# setup estimates\n",
     "site_parameter_estimates = {}\n",
-    "if user_input_azi is not None:\n",
-    "    site_parameter_estimates[\"azimuth\"] = user_input_azi\n",
-    "if user_input_tilt is not None:\n",
-    "    site_parameter_estimates[\"tilt\"] = user_input_tilt"
+    "if USER_INPUT_AZI is not None:\n",
+    "    site_parameter_estimates[\"azimuth\"] = USER_INPUT_AZI\n",
+    "if USER_INPUT_TILT is not None:\n",
+    "    site_parameter_estimates[\"tilt\"] = USER_INPUT_TILT"
    ]
   },
   {
@@ -419,9 +418,9 @@
    ],
    "source": [
     "# align to UTC if required\n",
-    "utc_offset = longitude // 15\n",
+    "utc_offset = LONGITUDE // 15\n",
     "utc_offset = pd.Timedelta(f\"{utc_offset}h\")\n",
-    "if local_timezone:\n",
+    "if LOCAL_TIMEZONE:\n",
     "    measurement_data.index = measurement_data.index - utc_offset\n",
     "print(\n",
     "    \"We estimate your UTC offset is \", utc_offset, \". You can update this if incorrect.\"\n",
@@ -450,13 +449,13 @@
    "source": [
     "# required for all grid searches\n",
     "KWARGS = {\n",
-    "    \"latitude\": latitude,\n",
-    "    \"longitude\": longitude,\n",
+    "    \"latitude\": LATITUDE,\n",
+    "    \"longitude\": LONGITUDE,\n",
     "    \"meas\": measurement_data,\n",
     "    \"months\": months,\n",
     "    \"site_parameter_estimates\": site_parameter_estimates,\n",
-    "    \"period\": period,\n",
-    "    \"api_key\": api_key,\n",
+    "    \"period\": PERIOD,\n",
+    "    \"api_key\": API_KEY,\n",
     "}"
    ]
   },
@@ -543,11 +542,11 @@
    ],
    "source": [
     "# take an initial capacity estimate and show an initial estimation with no tuning\n",
-    "capacity_initial_estimate = measurement_data[power_column_name].max()\n",
+    "capacity_initial_estimate = measurement_data[PV_POWER_COLUMN].max()\n",
     "site_parameter_estimates[\"capacity\"] = capacity_initial_estimate\n",
     "\n",
     "pre_tuned = get_solcast_untuned(\n",
-    "    latitude, longitude, site_parameter_estimates, months, api_key\n",
+    "    LATITUDE, LONGITUDE, site_parameter_estimates, months, API_KEY\n",
     ")\n",
     "display_evaluation_plots(measurement_data, pre_tuned)"
    ]
@@ -767,15 +766,15 @@
     }
    ],
    "source": [
-    "if user_input_azi is None:\n",
+    "if USER_INPUT_AZI is None:\n",
     "    # Use these defaults or update the range if you prefer\n",
     "    initial_azi_min = -180\n",
     "    initial_azi_max = 180\n",
     "    azi_delta = 45\n",
     "else:\n",
     "    azi_delta = 45\n",
-    "    initial_azi_min = user_input_azi - azi_delta\n",
-    "    initial_azi_max = user_input_azi + azi_delta\n",
+    "    initial_azi_min = USER_INPUT_AZI - azi_delta\n",
+    "    initial_azi_max = USER_INPUT_AZI + azi_delta\n",
     "\n",
     "# Initial wide search\n",
     "initial_azimuth_search = np.arange(\n",
@@ -962,15 +961,15 @@
     }
    ],
    "source": [
-    "if user_input_tilt is None:\n",
+    "if USER_INPUT_TILT is None:\n",
     "    # Use these defaults or update the range if you prefer\n",
     "    initial_tilt_min = 0\n",
     "    initial_tilt_max = 90\n",
     "    tilt_delta = 10\n",
     "else:\n",
     "    tilt_delta = 10\n",
-    "    initial_tilt_min = user_input_tilt - tilt_delta\n",
-    "    initial_tilt_max = user_input_tilt + tilt_delta\n",
+    "    initial_tilt_min = USER_INPUT_TILT - tilt_delta\n",
+    "    initial_tilt_max = USER_INPUT_TILT + tilt_delta\n",
     "\n",
     "\n",
     "# Initial wide search\n",
@@ -1189,23 +1188,23 @@
    ],
    "source": [
     "est_final = get_rooftop_data(\n",
-    "    latitude, longitude, site_parameter_estimates, months, period, api_key\n",
+    "    LATITUDE, LONGITUDE, site_parameter_estimates, months, PERIOD, API_KEY\n",
     ").tz_convert(None)[\"pv_power_rooftop\"]\n",
     "\n",
     "est_df = pd.DataFrame(\n",
     "    data={\n",
-    "        \"meas\": measurement_data[power_column_name],\n",
+    "        \"meas\": measurement_data[PV_POWER_COLUMN],\n",
     "        \"solcast_estimate\": est_final,\n",
     "        \"solcast_pre_tuned\": pre_tuned[\"solcast_pretuned_estimate\"],\n",
     "    },\n",
     "    index=measurement_data.index,\n",
     ")\n",
-    "if save_to_csv:\n",
+    "if SAVE_TO_CSV:\n",
     "    est_df.to_csv(\"Solcast_Tuned_Rooftop_PV.csv\")\n",
     "\n",
     "print(\"Tuned Parameter Estimates:\", site_parameter_estimates)\n",
     "\n",
-    "results = calculate_stats(measurement_data[power_column_name], est_final, df=True)\n",
+    "results = calculate_stats(measurement_data[PV_POWER_COLUMN], est_final, df=True)\n",
     "display_evaluation_plots(meas=measurement_data, est=est_final, untuned=pre_tuned)"
    ]
   }