feat: Introduce linear forecasting for SFC and auxiliary parameters and refine actual energy sales calculation.

src/modules/plant/run_plant_simulation.py

@@ -130,6 +130,40 @@ def getproyeksilinier(years, values, iterations=30, target_years=None):
     n_projection = iterations - n_hist
     return {start_year + i: _predict(start_year + i) for i in range(n_projection)}
 
+def linear_forecast_from_history(values):
+    """
+    Melakukan proyeksi linier (OLS) dari seluruh data history.
+    Mengembalikan nilai forecast untuk index berikutnya.
+    """
+    # ambil hanya nilai valid (>0)
+    y = [v for v in values if v is not None and v > 0]
+
+    n = len(y)
+    if n == 0:
+        return 0.0
+    if n == 1:
+        return y[0]  # belum bisa regresi
+
+    x = list(range(n))
+
+    sum_x = sum(x)
+    sum_y = sum(y)
+    sum_x2 = sum(i * i for i in x)
+    sum_xy = sum(i * y[i] for i in x)
+
+    denominator = (n * sum_x2 - sum_x ** 2)
+    if denominator == 0:
+        return y[-1]
+
+    b = (n * sum_xy - sum_x * sum_y) / denominator
+    a = (sum_y - b * sum_x) / n
+
+    # forecast untuk index berikutnya
+    next_x = n
+    forecast = a + b * next_x
+
+    # safety clamp
+    return max(forecast, 0.0)
 
 
 def main():
@@ -361,9 +395,9 @@ def main():
         discount_rate = get_param("discount_rate") / 100
         total_project_cost = get_param("total_project_cost")
         daya_mampu_netto = get_param("daya_mampu_netto")
-        auxiliary = get_param("auxiliary")
+        # auxiliary = get_param("auxiliary")
         susut_trafo = get_param("susut_trafo")
-        sfc = get_param("sfc")
+        # sfc = get_param("sfc")
 
         # Harga listrik berdasarkan tipe
         price_a = get_param("electricity_price_a")
@@ -417,6 +451,9 @@ def main():
         eaf_v = 0
         eaf_history = []
         cf_history = []
+        sfc_history = []
+        auxiliary_history = []
+
         # Prefetch master data CF dan EAF sekali saja di luar loop
         cur.execute("""
                     SELECT year as tahun, cf, eaf
@@ -463,7 +500,8 @@ def main():
             if data["is_actual"] == 1:
                 production_bruto = validate_number(data["production_bruto"])
                 production_netto = validate_number(data["production_netto"])
-                energy_sales = production_netto
+                energy_sales = validate_number(data.get("energy_sales")) if validate_number(
+                    data.get("energy_sales")) is not None else production_netto
                 fuel_consumption = validate_number(data["fuel_consumption"])
                 revenue_a = validate_number(data["revenue_a"])
                 revenue_b = validate_number(data["revenue_b"])
@@ -473,7 +511,25 @@ def main():
                 cost_bd_om = validate_number(data["cost_bd_om"])
                 cost_bd_pm_nonmi = validate_number(data["cost_bd_pm_nonmi"])
                 cost_bd_bd = validate_number(data["cost_bd_bd"])
+                sfc_history.append(fuel_consumption / production_bruto if production_bruto != 0 else 0)
+                auxiliary_history.append(
+                    (production_bruto - production_netto) / production_bruto * 100 if production_bruto != 0 else 0)
             else:
+
+                # last_3_sfc = sfc_history[-3:]
+                # avg_last_3_sfc = sum(last_3_sfc) / len(last_3_sfc) if len(last_3_sfc) > 0 else 0
+                # sfc = avg_last_3_sfc
+                # sfc_history.append(avg_last_3_sfc)
+                # last_3_auxiliary = auxiliary_history[-3:]
+                # avg_last_3_auxiliary = sum(last_3_auxiliary) / len(last_3_auxiliary) if len(last_3_auxiliary) > 0 else 0
+                # auxiliary = avg_last_3_auxiliary
+                # auxiliary_history.append(avg_last_3_auxiliary)
+
+                sfc = linear_forecast_from_history(sfc_history)
+                sfc_history.append(sfc)
+                auxiliary = linear_forecast_from_history(auxiliary_history)
+                auxiliary_history.append(auxiliary)
+                
                 production_netto = net_capacity_factor * 8760 * daya_mampu_netto / 100
                 production_bruto = production_netto / (100 - (auxiliary + susut_trafo)) * 100
                 energy_sales = production_netto