Update data/ashrae_tables.py

data/ashrae_tables.py

@@ -189,6 +189,77 @@ class ASHRAETables:
             0.9: 1.15    # Dark surfaces
         }
 
+    def _load_heat_gain_table(self) -> pd.DataFrame:
+        """
+        Load heat gain table for internal sources based on ASHRAE Handbook—Fundamentals (2017, Chapter 18).
+        Consolidates occupancy, lighting, and equipment heat gains into a single table.
+        
+        Returns:
+            pd.DataFrame: DataFrame with columns 'category', 'subcategory', 'sensible' (W), 'latent' (W).
+        """
+        # Get occupancy and equipment heat gain tables
+        occupancy_df = self._load_occupancy_heat_gain_table()
+        equipment_df = self._load_equipment_heat_gain_table()
+    
+        # Prepare data for consolidated table
+        data = []
+    
+        # People: Map occupancy types to heat gains
+        for _, row in occupancy_df.iterrows():
+            data.append({
+                'category': 'people',
+                'subcategory': row['occupancy_type'],
+                'sensible': row['sensible_gain'],
+                'latent': row['latent_gain']
+            })
+        
+        # Lighting: Assume 1 W sensible gain per Watt, no latent gain
+        data.append({
+            'category': 'lighting',
+            'subcategory': 'general',
+            'sensible': 1.0,  # 1 W/W (100% sensible heat)
+            'latent': 0.0
+        })
+    
+        # Equipment: Use a generic value (adjustable in cooling_load.py via radiation_fraction)
+        # Aggregate equipment gains as a baseline (e.g., average or typical office equipment)
+        equipment_sensible = equipment_df['sensible_gain'].mean()  # Example: mean sensible gain
+        equipment_latent = equipment_df['latent_gain'].mean()      # Example: mean latent gain
+        data.append({
+            'category': 'equipment',
+            'subcategory': 'office',
+            'sensible': 1.0,  # 1 W/W sensible, scalable by power in cooling_load.py
+            'latent': 0.0     # Assume no latent gain for generic equipment
+        })
+    
+        return pd.DataFrame(data)
+
+    def get_heat_gain(self, source: str, subcategory: Optional[str] = None) -> Tuple[float, float]:
+        """
+        Get sensible and latent heat gain for an internal source.
+        
+        Args:
+            source (str): Source type ('people', 'lighting', 'equipment').
+            subcategory (str, optional): Subcategory (e.g., 'Seated, resting' for people, 'general' for lighting).
+    
+        Returns:
+            Tuple[float, float]: Sensible and latent heat gain values (Watts).
+    
+        Raises:
+            ValueError: If source or subcategory is invalid.
+        """
+        if source not in self.heat_gain['category'].values:
+            raise ValueError(f"Invalid source: {source}")
+        if subcategory:
+            if subcategory not in self.heat_gain[self.heat_gain['category'] == source]['subcategory'].values:
+                raise ValueError(f"Invalid subcategory for {source}: {subcategory}")
+            row = self.heat_gain[(self.heat_gain['category'] == source) & (self.heat_gain['subcategory'] == subcategory)]
+        else:
+            row = self.heat_gain[self.heat_gain['category'] == source]
+        if row.empty:
+            raise ValueError(f"No data found for source: {source}, subcategory: {subcategory}")
+        return float(row['sensible'].iloc[0]), float(row['latent'].iloc[0])
+    
     def interpolate_cltd(self, latitude: float, cltd_table_low: pd.DataFrame, cltd_table_high: pd.DataFrame, lat_low: float, lat_high: float) -> pd.DataFrame:
         """
         Interpolate CLTD or SCL values between two latitudes.