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Overview

This module performs data manipulation operations to filter and merge power generation data with master data, focusing on EMGESA plants and calculating aggregated metrics.

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Data Sources

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Master Data (Excel)

  • Contains plant metadata and agent information
  • Includes plant types (Hydro, Thermal, etc.)
  • Links OFEI identifiers to plant names

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Generation Data (TXT)

  • dDEC file format with hourly generation
  • 24 columns representing hours (H1-H24)
  • Plant-level generation data

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Loading Master Data

import pandas as pd

# 1. Cargar Master Data con las columnas solicitadas
cols_master = [
    'Nombre visible Agente',
    'AGENTE (OFEI)',
    'CENTRAL (dDEC, dSEGDES, dPRU…)',
    'Tipo de central (Hidro, Termo, Filo, Menor)'
]

df_master = pd.read_excel(
    '/content/drive/MyDrive/Prueba_tecnica/Datos3/Datos Maestros VF.xlsx',
    usecols=cols_master
)

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Filtering by Agent and Type

# 2. Filtrar por Agente (EMGESA) y Tipo (H o T)
agentes = ['EMGESA', 'EMGESA S.A.']
tipos = ['H', 'T']

df_m_filt = df_master[
    (df_master['AGENTE (OFEI)'].isin(agentes)) &
    (df_master['Tipo de central (Hidro, Termo, Filo, Menor)'].isin(tipos))
].copy()

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Filter Criteria

  • Agents: EMGESA or EMGESA S.A.
  • Plant Types:
    • H = Hydro (Hidroeléctrica)
    • T = Thermal (Termoeléctrica)

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Loading Generation Data

# 3. Cargar el TXT
nombres_columnas = ['CENTRAL'] + [f'H{i}' for i in range(1, 25)]

df_ddec = pd.read_csv(
    '/content/drive/MyDrive/Prueba_tecnica/Datos3/dDEC1204.TXT',
    header=None,           # Le decimos que la primera fila NO son títulos
    names=nombres_columnas, # Asignamos nuestros propios títulos
    quotechar='"',         # Maneja los nombres que vienen entre comillas como "ALBAN"
    skipinitialspace=True, # Elimina espacios después de comas
    encoding='latin-1'     # Evita errores por caracteres especiales
)

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Data Cleaning for Merge

# 4. Limpieza de texto para asegurar el cruce
df_m_filt['CENTRAL (dDEC, dSEGDES, dPRU…)'] = \
    df_m_filt['CENTRAL (dDEC, dSEGDES, dPRU…)'].str.strip().str.upper()

df_ddec['CENTRAL'] = df_ddec['CENTRAL'].str.strip().str.upper()
Text normalization ensures successful joins by:
  • Removing leading/trailing whitespace
  • Converting to uppercase
  • Standardizing plant names

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Merging Datasets

# 5. Realizar el Merge
df_merged = pd.merge(
    df_m_filt,
    df_ddec,
    left_on='CENTRAL (dDEC, dSEGDES, dPRU…)',
    right_on='CENTRAL'
)

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Join Operation

  • Type: Inner join
  • Key: Plant name (CENTRAL)
  • Result: Combined dataset with master data + generation data

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Calculating Aggregates

# 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)
The Suma_Total column represents the total daily generation across all 24 hours.

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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()
This filters out plants with no generation activity during the day.

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Final Dataset Structure

ColumnSourceDescription
Nombre visible AgenteMasterDisplay name
AGENTE (OFEI)MasterOfficial agent name
CENTRALMaster/dDECPlant identifier
Tipo de centralMasterPlant type (H/T)
H1-H24dDECHourly generation (MW)
Suma_TotalCalculatedDaily total generation

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Sample Output

   Nombre visible Agente  AGENTE (OFEI)  CENTRAL  Tipo  H1   H2  ... Suma_Total
0                EMGESA  EMGESA S.A.   BETANIA     H  364  364  ...      8736
1                EMGESA  EMGESA S.A.  ELQUIMBO     H   85   85  ...      2040
2                EMGESA  EMGESA S.A.    GUAVIO     H    0    0  ...      4325
3                EMGESA  EMGESA S.A.     PAGUA     H  600  600  ...     14400
The resulting dataset contains 16 rows of EMGESA plants (H or T type) with active generation.

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