Tuning

.gitignore

@@ -1 +1,2 @@
-.venv/
+.venv/
+*.sqlite3

README.md

@@ -1,5 +1,10 @@
 # Agile Predict v1.0.0
 
-This model forecasts Octopus Agile electricity prices up to 14 days in advance using a Machine Learning model trained on data from the Balancing Mechanism Reporting System (BRMS), National Grid Electricity Supply Operator (NG ESO) and weather data from open-meteo.com.
+This model forecasts Octopus Agile electricity prices up to 14 days in advance using a Machine Learning model trained
+on data from the Balancing Mechanism Reporting System (<a href="https://bmrs.elexon.co.uk/">BRMS</a>), National Grid 
+Electricity Supply Operator (<a href="https://www.nationalgrideso.com/data-portal">NG ESO</a>) and weather data from 
+<a href="https://open-meteo.com"> open-meteo.com</a>.<p>
+
+Because the app is hosted on a <a href= "http://fly.io">fly.io</a> hobby account the database is only updated when the page
+is accessed and if there has been an hour since the last update. This may sometimes cause slight delays in loading. <p>
 
-Because the app is hosted on a fly.io hobby account the database is only updated when the page is accessed and if there has been an hour since the last update. This may sometimes cause slight delays in loading. 

prices/management/commands/update.py

@@ -7,6 +7,7 @@
 from requests.exceptions import HTTPError
 from urllib import parse
 from datetime import datetime
+from sklearn.metrics import mean_squared_error as MSE
 
 from django.core.management.base import BaseCommand
 from ...models import History, PriceHistory, Forecasts, ForecastData
@@ -459,11 +460,21 @@ def handle(self, *args, **options):
         X = hist.iloc[-48 * 28 :]
         y = prices["day_ahead"].loc[X.index]
 
-        print(X)
-        print(y)
+        model = xg.XGBRegressor(
+            objective="reg:squarederror",
+            booster="dart",
+            # max_depth=0,
+            gamma=0.3,
+            eval_metric="rmse",
+        )
 
-        model = xg.XGBRegressor(objective="reg:squarederror", booster="dart", max_depth=0)
-        model.fit(X, y)
+        model.fit(X, y, verbose=True)
+        model_day_ahead = pd.Series(index=y.index, data=model.predict(X))
+
+        model_agile = day_ahead_to_agile(model_day_ahead)
+        rmse = MSE(model_agile, prices["agile"].loc[X.index]) ** 0.5
+
+        print(f"RMS Error: {rmse: 0.2f} p/kWh")
 
         cols = X.columns
         fc["day_ahead"] = model.predict(fc[cols])

templates/base.html

@@ -28,6 +28,14 @@ <h1><a href="{% url 'graph' %}">Agile Predict v{{APP_VERSION_NUMBER}}</a></h1>
         <div style="margin-left: 20px";"margin-right: 20px";>
             Because the app is hosted on a <a href= "http://fly.io">fly.io</a> hobby account the database is only updated when the page
             is accessed and if there has been an hour since the last update. This may sometimes cause slight delays in loading. <p>
-        </div>        
-    </body>
+        </div>    
+        <h2 style="margin-left: 20px";"margin-right: 20px">API Access</h2>    
+        <div style="margin-left: 20px";"margin-right: 20px";>
+            RESTful API access to the three most recent price forecasts is available via <a href="http://prices.fly.dev/api">prices.fly.dev/api</a>
+        </div>
+        <h2 style="margin-left: 20px";"margin-right: 20px">Development</h2>    
+        <div style="margin-left: 20px";"margin-right: 20px";>
+            Contributions to the project are welcome through <a href="https://github.com/fboundy/agile_predict">GitHub</a>
+        </div>
+        </body>
 </html>