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Overview

This module performs analysis on the merged power plant dataset to identify active generation, calculate daily totals, and produce a final filtered dataset for reporting.

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Analysis Pipeline

The analysis follows these steps:
  1. Calculate hourly sums across all hours
  2. Filter plants with generation > 0
  3. Produce final dataframe with aggregated metrics

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Hourly Sum Calculation

# 6. Calcular Suma Horizontal (H1 a H24)
columnas_horas = [f'H{i}' for i in range(1, 25)]
df_merged['Suma_Total'] = df_merged[columnas_horas].sum(axis=1)

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Calculation Logic

  • Columns: H1 through H24 (24 hourly columns)
  • Operation: Row-wise sum using axis=1
  • Result: Total daily generation per plant in MWh

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Example Calculation

For a plant with constant 364 MW generation: 
H1 + H2 + ... + H24 = 364 × 24 = 8,736 MWh

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Filtering Active Plants

# 7. Seleccionar solo plantas con suma mayor a cero
df_resultado = df_merged[df_merged['Suma_Total'] > 0].copy()

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Filter Criteria

  • Condition: Suma_Total > 0
  • Purpose: Remove plants with no generation during the analysis period
  • Result: Dataset containing only active plants

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Final Dataframe Structure

The analysis produces a comprehensive dataset combining:
  • Master data (agent info, plant type)
  • Hourly generation data (H1-H24)
  • Calculated metrics (Suma_Total)

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Column Schema

ColumnTypeDescription
Nombre visible AgentestringDisplay name (e.g., “EMGESA”)
AGENTE (OFEI)stringOfficial agent identifier
CENTRAL (dDEC, dSEGDES, dPRU…)stringMaster data plant name
Tipo de central (Hidro, Termo, Filo, Menor)stringPlant type (H/T)
CENTRALstringGeneration data plant name
H1-H24floatHourly generation values (MW)
Suma_TotalfloatDaily total generation (MWh)

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Analysis Results

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Sample Output

df_resultado

   Nombre visible Agente AGENTE (OFEI) CENTRAL (dDEC, dSEGDES, dPRU…) 
0                 EMGESA   EMGESA S.A.                        BETANIA   
1                 EMGESA   EMGESA S.A.                        BETANIA   
2                 EMGESA   EMGESA S.A.                        BETANIA   
6                 EMGESA   EMGESA S.A.                       ELQUIMBO   
7                 EMGESA   EMGESA S.A.                       ELQUIMBO   
8                 EMGESA   EMGESA S.A.                         GUAVIO   
9                 EMGESA   EMGESA S.A.                         GUAVIO   
...

   Tipo de central  CENTRAL  H1   H2   H3   H4  ...  H20   H21  H22  H23  H24  Suma_Total
0               H  BETANIA  364  364  364  364  ...  364   364  364  364  364        8736
1               H  BETANIA  364  364  364  364  ...  364   364  364  364  364        8736
2               H  BETANIA  364  364  364  364  ...  364   364  364  364  364        8736
6               H ELQUIMBO   85   85   85   85  ...   85    85   85   85   85        2040
7               H ELQUIMBO   85   85   85   85  ...   85    85   85   85   85        2040
8               H   GUAVIO    0    0    0    0  ...  1002  780  140    0    0        4325
9               H   GUAVIO    0    0    0    0  ...  1002  780  140    0    0        4325
...

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Key Statistics

  • Total Records: 16 rows
  • Unique Plants: 4 (BETANIA, ELQUIMBO, GUAVIO, PAGUA)
  • All Plants: Type H (Hydroelectric)
  • Agent: EMGESA / EMGESA S.A.

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Generation Patterns

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Constant Generation (BETANIA, ELQUIMBO, PAGUA)

These plants show stable generation across all hours: 
BETANIA:  364 MW × 24h = 8,736 MWh
ELQUIMBO:  85 MW × 24h = 2,040 MWh
PAGUA:    600 MW × 24h = 14,400 MWh

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Variable Generation (GUAVIO)

GUAVIO shows peaking behavior:
  • Off-peak hours (H1-H15): 0 MW
  • Peak hours (H16-H22): 120-1002 MW
  • Night hours (H23-H24): 0 MW
  • Daily total: 4,325 MWh
This pattern indicates pumped-storage or peaking hydroelectric operation.

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Usage Example

# Get plants sorted by total generation
top_plants = df_resultado.groupby('CENTRAL')['Suma_Total'].first().sort_values(ascending=False)

print(top_plants)
# Output:
# PAGUA       14400.0
# BETANIA      8736.0
# GUAVIO       4325.0
# ELQUIMBO     2040.0

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Output Dataset

The df_resultado dataframe contains:
  • All EMGESA plants with active generation
  • Complete hourly generation profiles
  • Calculated daily totals
  • Master data enrichment (agent names, plant types)
This dataset is ready for further analysis, visualization, or reporting.

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