Use EIC codes as deterministic matching key before Duke fuzzy matching

Problem

PPM currently routes all cross-source matching through Duke fuzzy matching on 5 fields: NAME, FUELTYPE, COUNTRY, CAPACITY, GEOPOSITION. EIC codes — unique European plant identifiers — are loaded from OPSD and ENTSOE, carried through the pipeline, and aggregated as sets in reduce_matched_dataframe(), but never used in the matching decision itself.

This leads to a class of bugs where co-located plants with similar names but different fuels get incorrectly merged. The canonical example:

Eemshaven harbour, Netherlands

Plant	Operator	Fuel	Capacity	EIC	OPSD name
Eemshavencentrale	RWE	Hard Coal + biomass co-firing	2 × 780 MW	(unique)	Eemshavencentrale EM1/EM2
Eemscentrale	ENGIE	Natural Gas (peakers)	6 units, ~2200 MW	49W00000000008xG/K/O/S/W, etc.	Eemscentrale EC3–EC7, EC20

Duke merges them into one "Eemshaven" entry labelled Hard Coal because:

• Name similarity: "Eemshavencentrale" ≈ "Eemscentrale" (~0.86 JaroWinkler)
• Geo distance: ~400m apart on the same peninsula (max 5000m comparator)
• These two signals score well above the 0.965 threshold, overwhelming the fueltype mismatch (QGram low=0.09, high=0.7 — too weak to block)

The result: 2200 MW of Dutch gas peaker capacity is silently absorbed into a coal plant entry.

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Proposed solution: EIC-first matching

The European energy market already solved plant identity — EIC (Energy Identification Code) is a unique identifier assigned to every market participant and generation unit. ENTSOE and OPSD both provide EIC codes for most plants.

Architecture

Before (current):
  All sources ──→ Duke fuzzy match ──→ reduce

After (proposed):
  All sources ──→ Deterministic EIC join ──→ matched set 1 (high confidence)
                  │
                  └→ EIC-unmatched residual ──→ Duke fuzzy match ──→ matched set 2
                  
                  matched set 1 + matched set 2 ──→ reduce

Implementation

Best insertion point: compare_two_datasets() in matching.py (line ~76)

def compare_two_datasets(dfs, dukeargs=None, ...):
    dfs = list(map(read_csv_if_string, dfs))
    
    # ── NEW: Deterministic EIC matching ──────────────────────
    eic_matches = _match_by_eic(dfs[0], dfs[1])
    
    # Remove EIC-matched rows from Duke input
    remaining = [
        dfs[0].drop(eic_matches['idx_0']),
        dfs[1].drop(eic_matches['idx_1']),
    ]
    
    # Duke handles the rest (fuzzy)
    duke_matches = _duke_match(remaining, dukeargs, ...)
    
    # Combine
    return pd.concat([eic_matches, duke_matches])

The _match_by_eic() function would:

1. Inner join on non-null EIC values
2. Handle multi-EIC plants (some have multiple EIC codes per unit)
3. Return matches with a perfect confidence score (1.0)

Why this is better than fueltype blocking

• Positive identification vs negative filtering — we know what matches, not just what shouldn't
• No false negatives — fueltype blocking would prevent merging when sources disagree on fuel classification (e.g. "Biomass and biogas" vs "Hard Coal" for co-firing plants)
• Generalises — works for any co-located plants (same fuel, different owner; industrial CHP next to utility plant; etc.)
• Uses existing data — EIC is already in target_columns, loaded from ENTSOE (data.py:782), OPSD EU (Eic_Code), OPSD DE (Eic_Code_Plant), and GEM (WEIC)

Current EIC availability

Source	EIC field	Coverage
ENTSOE	projectID (= EIC)	~100% for EU grid-connected plants
OPSD EU	eic_code	~70% of entries
OPSD DE	eic_code_plant	~80% of DE entries
GEM	WEIC	partial
GEO, GPD, others	—	none

Even with partial coverage, EIC-first matching would deterministically resolve the majority of cross-source matches, leaving Duke to handle only the residual.

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Impact

• Eliminates co-located plant confusion (Eemshaven class of bugs) across all countries
• Enables reliable cross-source data enrichment (e.g. OPSD efficiency mapped to the correct ENTSOE plant)
• Reduces Duke's workload (fewer comparisons → faster matching)
• Makes the matching pipeline auditable — EIC matches can be verified independently

Related

• #286 — OPSD data quality (efficiency column, biomass co-firing): improves the data that flows into matching

Start in matching.py at compare_two_datasets() around line 76, then inspect reduce_matched_dataframe() and the EIC fields loaded in data.py around line 782. Trace target_columns and the OPSD and ENTSOE EIC names to define how matches and multi-EIC plants are represented; done means deterministic EIC matches are combined with residual Duke matches without losing existing aggregation behavior.