peak shaving, predicting t-168h value.

v2/architecture.py

@@ -110,23 +110,17 @@ class Decider:
     # the method returns a pd.Series of Decisions as integers.
     def decide(self, prod_pred, cons_pred, fees, battery_model):
         return Decision.PASSIVE
-        next_prod = prod_pred[0]
-        next_cons = cons_pred[0]
-        deficit = next_cons - next_prod
-        soc = battery_model.soc
-        if soc > 0.9:
+        # 15 minutes demand charge window divided by 5 minute timestep, TODO make it more principled
+        peak_shaving_window = 3
+
+        # TODO is there a kWh kW confusion here?:
+        step_in_hour = self.precalculated_supplier.time_index.freq.n / 60 # [hour], the length of a time step.
+        deficit_kw = (cons_pred[:peak_shaving_window] - prod_pred[:peak_shaving_window]).clip(min=0)
+        deficit_kwh = (step_in_hour * deficit_kw).sum()
+        if deficit_kwh > self.precalculated_supplier.peak_demand:
             return Decision.DISCHARGE
-        elif soc < 0.1 and OLCSO:
+        else:
             return Decision.PASSIVE
-        return 
-
-        # assert len(prod_pred) == len(cons_pred) == self.input_window_size
-        self.random_seed += 1
-        self.random_seed %= 3 # dummy rotates between Decisions
-        self.random_seed = Decision.DISCHARGE
-        return self.random_seed
-        # dummy decider always says DISCHARGE:
-        # return pd.Series([Decision.DISCHARGE] * self.output_window_size, dtype=int)
 
 
 # even mock-er class than usual.
@@ -259,7 +253,7 @@ def simulator(battery_model, prod_cons, decider):
     fifteen_minute_surdemands_in_kwh = (fifteen_minute_demands_in_kwh - decider.precalculated_supplier.peak_demand).clip(lower=0)
     demand_charges = fifteen_minute_surdemands_in_kwh * decider.precalculated_supplier.surcharge_per_kwh
     total_charge = consumption_charge_series.sum() + demand_charges.sum()
-    print(f"All in all we have paid {total_charge} to network.")
+    print(f"All in all we have paid the network {total_charge / 10 ** 6} MHUF.")
 
     if DO_VIS:
         demand_charges.plot()
@@ -286,7 +280,7 @@ def main():
     supplier.set_price_for_daily_interval(0, 3, 20)
     # peak_demand dimension is kWh, but it's interpreted as the full consumption
     # during a 15 minute timestep.
-    supplier.set_demand_charge(peak_demand=25, surcharge_per_kwh=500) # kWh in a 15 minutes interval, Ft/kWh
+    supplier.set_demand_charge(peak_demand=2.5, surcharge_per_kwh=500) # kWh in a 15 minutes interval, Ft/kWh
 
     parameters = SolarParameters()
 
@@ -294,10 +288,19 @@ def main():
     add_production_field(met_2021_data, parameters)
     all_data = interpolate_and_join(met_2021_data, cons_2021_data)
 
-    # we have perfect foresight, yet:
+    time_interval_min = all_data.index.freq.n
+    print("time_interval_min", time_interval_min)
+    time_interval_h = time_interval_min / 60
+
+    # we predict last week same time for consumption, and yesterday same time for production.
     all_data_with_predictions = all_data.copy()
-    all_data_with_predictions['Consumption_prediction'] = all_data_with_predictions['Consumption']
-    all_data_with_predictions['Production_prediction'] = all_data_with_predictions['Production']
+    cons_shift = 60 * 168 // time_interval_min
+    prod_shift = 60 * 24 // time_interval_min
+    all_data_with_predictions['Consumption_prediction'] = all_data_with_predictions['Consumption'].shift(periods=cons_shift)
+    all_data_with_predictions['Production_prediction'] = all_data_with_predictions['Production'].shift(periods=prod_shift)
+    # we predict zero before we have data, no big deal:
+    all_data_with_predictions['Consumption_prediction'][:cons_shift] = 0
+    all_data_with_predictions['Production_prediction'][:prod_shift] = 0
 
     precalculated_supplier = precalculate_supplier(supplier, all_data.index)
     # we delete the supplier to avoid accidentally calling it instead of precalculated_supplier
@@ -305,8 +308,6 @@ def main():
 
     all_data_with_predictions['Consumption_fees'] = precalculated_supplier.consumption_fees # [HUF / kWh]
 
-    time_interval_min = all_data.index.freq.n
-    time_interval_h = time_interval_min / 60
     battery_model = BatteryModel(capacity_Ah=600, time_interval_h=time_interval_h)
 
     # TODO this is super unfortunate: