Methodology

What this is #

loadcurve.eu shows European electricity data — consumption, wind and solar generation, total renewable generation, and temperature — across 35 bidding zones, hour by hour, from 2015 to the present. The raw inputs are public; the work is in the processing: consistent zone-level aggregation, honest metric definitions, degree-day and weather-normalisation derivations, and cleaned, auditable time series. This page documents exactly how each number is produced, and — just as importantly — what each number does not claim.

The site treats Europe as a single system rather than privileging any one country. There is no “home market”; every bidding zone is handled the same way.

For the bigger picture — what this project is, the stack and design decisions behind it, and who built it — see the About page.

Data sources & licence #

Three public sources feed the site:

• ENTSO-E Transparency Platform — consumption (actual total load) and generation by production type. Published by Europe's transmission system operators under EU Regulation 543/2013. The data items used here are on the Platform's list of data available for re-use, licensed CC-BY 4.0.
• Open-Meteo — hourly 2-metre air temperature, one representative point per zone (see Temperature & degree-days), licensed CC-BY 4.0.
• CAMS (Copernicus Atmosphere Monitoring Service), via Open-Meteo's air-quality API — hourly aerosol optical depth, same representative point per zone as temperature (see Aerosol optical depth (dust)). CAMS data is not CC-BY 4.0 — it carries its own Copernicus attribution requirement, satisfied separately in the footer.

The ENTSO-E and Open-Meteo sources are processed and aggregated here, not republished verbatim: hourly temperature is converted into degree-days, generation is summed into the metrics below, and series are cleaned and re-gridded. CC-BY requires indicating that changes were made — they were, as described throughout this page. Aerosol optical depth is aggregated into a daily mean (see Aerosol optical depth (dust)) but is otherwise used as published by CAMS.

The footer carries the required attribution with links on every page:

EN: Weather data by Open-Meteo.com (CC BY 4.0) · Electricity data: ENTSO-E Transparency Platform (CC BY 4.0) · Aerosol data: CAMS (Copernicus Atmosphere Monitoring Service) — both processed/aggregated.

HU: Időjárási adatok: Open-Meteo.com (CC BY 4.0) · Villamosenergia-adatok: ENTSO-E Transparency Platform (CC BY 4.0) · Aeroszol-adatok: CAMS (Copernicus légkörfigyelő szolgálat) — feldolgozva/aggregálva.

(Links are not optional under CC-BY: link Open-Meteo to open-meteo.com and the CC-BY licence, and ENTSO-E to its Transparency Platform and the licence. The CAMS/Copernicus link points to the Copernicus Atmosphere Monitoring Service.)

The current build uses load + generation + temperature + aerosol optical depth; no day-ahead price data is ingested yet.

All timestamps are handled in both local zone time and UTC (see Day basis). The site is rebuilt automatically every hour.

Coverage #

35 bidding zones, hourly resolution, from 1 January 2015 to the present. Multi-zone countries are kept at bidding-zone granularity (for example Italy, Norway, Sweden and Denmark are split into their constituent zones); single-zone countries are represented by one zone. Coverage is not uniform across the whole period — see Known limitations and Deliberate exclusions.

Zone aggregation #

For each zone, hourly load and generation series are aligned on a common hourly index, de-duplicated (keeping the first value where the source reports duplicates), and resampled to a clean hourly grid. Generation arrives as a breakdown by production type; the relevant types are summed into the published series (see Metrics). Country- and Europe-level figures are sums of the constituent zones for the selected period.

The map, the three Europe headline numbers beneath it, and the records list all read the same period selector, which defaults to the live “now” snapshot (see Records). The map's optional Play animation does not follow that selector: it always animates a fixed recent window — up to the last 48 hours where zone-reporting coverage is at least 90% of its recent maximum, so it never ends on a still-incomplete, freshly-published hour. A full historical timelapse across the ten-year archive is a possible future feature, not implemented here.

Temperature & degree-days #

Each zone is assigned one representative weather point (a single latitude/longitude, typically a major load centre), from which hourly temperature is drawn. This is a deliberate simplification: it is not a population-weighted or area-weighted temperature for the whole zone, and it will diverge from a zone-wide average in large or climatically varied zones.

Degree-days are computed from the hourly temperature, not from a daily mean. For each hour, the heating contribution is max(0, 18 °C − T) and the cooling contribution is max(0, T − 22 °C); these are averaged over the hours of each day. Computing degree-days hour by hour (rather than from the daily-mean temperature) is more accurate in shoulder seasons, when temperature crosses the base threshold within a single day.

The bases are 18 °C for heating (HDD) and 22 °C for cooling (CDD). The 18–22 °C band is a deliberate dead zone in which neither heating nor cooling demand is attributed — a comfort range rather than a single switch point.

Aerosol optical depth (dust) #

Aerosol optical depth (AOD) is a dimensionless measure of how much atmospheric aerosol — dust, smoke, haze — attenuates sunlight passing through the column of air above a point; higher values mean hazier skies and less sunlight reaching the ground. It is available in the free explorer as a plottable metric (for example against solar generation or wind+solar share), to see episodes such as Saharan dust intrusions visibly suppress solar output.

The source is CAMS (Copernicus Atmosphere Monitoring Service), via Open-Meteo's air-quality API, using the same representative point per zone as temperature (see Temperature & degree-days above), so the two series line up spatially.

Coverage starts 4 August 2022, uniformly across all zones — this is the start of the CAMS historical archive itself, not a gap in any particular zone. Earlier dates are stored as absent (never zero); charts that include AOD show a coverage note and simply have no points before that date.

The daily figure is a plain 24-hour mean of the hourly values. This dilutes the daytime signal somewhat, since night-time AOD has no effect on solar output — the sharpest dust→solar relationship is visible on the single-day hourly drill-down, not the daily aggregate. A daytime-weighted daily mean is a possible future refinement, not implemented here.

Metrics #

There are two distinct renewable metrics on the site. They are not interchangeable, and they are named to keep them apart.

Wind+solar share (variable renewables) #

(wind + solar) / load for the selected zone(s) and period. This is the variable, weather-driven share of demand — the metric that the Dunkelflaute view and the “highest / lowest wind+solar hour” records are built on. It deliberately excludes hydro and all dispatchable renewables, because Dunkelflaute (“dark doldrums”) is specifically about low wind and low sun; folding hydro in would defeat the concept.

Total renewable share (all sources) #

renewable / load, where renewable is the sum of these ENTSO-E production types (exact match, not substring):

Biomass (B01), Geothermal (B09), Hydro Run-of-river and poundage (B11), Hydro Water Reservoir (B12), Other renewable (B15), Solar (B16), Wind Offshore (B18), Wind Onshore (B19).

Two exclusions are deliberate and worth stating plainly:

• Hydro Pumped Storage (B10) is excluded. Its output comes from water previously pumped uphill using grid electricity (often non-renewable); counting it would overstate renewable generation. It also shares the word “Hydro” with the reservoir and run-of-river types, so the matching is exact rather than a “Hydro” substring, specifically to keep B10 out.
• Waste (B17) is excluded. It is only partly renewable, so it is left out rather than counted in full.

This total-renewable metric is what drives the cross-country comparisons — the map's renewable layer, the headline “renewable share” figure, and the “greenest country” reading. It is why hydro-dominated zones (much of Norway, Sweden, Austria) read as highly renewable here, as they should.

Nuclear is not counted as renewable and does not appear in this metric at all. A zone can therefore be low-carbon in practice while still showing a low renewable share here — France is the clearest case: its generation is dominated by nuclear, so its total-renewable share reads as modest even though its carbon intensity is among the lowest in Europe. “Renewable” and “low-carbon” are not the same question; the Low-carbon share & CO₂ intensity section below is the place that addresses the latter.

A shared caveat on both shares #

Both shares use consumption (load) as the denominator, not total generation. They therefore measure renewable (or wind+solar) generation relative to local demand, not the renewable fraction of the local generation mix. In a zone that exports heavily from renewables the ratio can approach or exceed 100%; in a net importer it reads lower than the physical electricity actually consumed there. The figures do not account for cross-border flows.

Low-carbon share & CO₂ intensity #

Two further metrics sit alongside the renewable shares above. Neither is a renewable metric — they answer a different question: how much of this zone's electricity comes from low-carbon sources, and how carbon-intensive is the mix actually generated here?

Low-carbon share #

(renewable + nuclear) / load. Nuclear is not renewable — it does not appear in the renewable-share metric above, and it never enters renewable_mwh — but it is low-carbon on a lifecycle basis (see factors below). This is exactly why this metric exists: a zone like France, Hungary or Slovakia can read as low on total-renewable share while still being among the lowest-carbon zones on the continent, because nuclear supplies most of its low-carbon generation rather than wind, solar or hydro. The two metrics are deliberately kept apart so that "renewable" and "low-carbon" are never conflated.

CO₂ intensity #

Reported in gCO₂eq/kWh, computed as:

CO₂ intensity = Σ(generation × emission factor) / Σ(generation)

summed over whichever period is selected — never the average of hourly or daily intensities. Averaging intensities would distort the figure (a low-generation, high-intensity hour would count as much as a high-generation hour), so the building blocks (total emissions and total qualifying generation) are aggregated first, and the ratio is taken last, at every level from a single hour up to a multi-year period.

This is a production-based figure: it is the intensity of the electricity mix generated in the zone, not the electricity consumed there. loadcurve.eu has no cross-border flow data, so a consumption-based (flow-traced) figure — the kind published by tools like Electricity Maps — cannot be computed here. A heavily-importing zone with a clean local mix will show a low figure even if some of the power it actually consumes was generated elsewhere and is dirtier; this is the same caveat already stated for the renewable shares (see above), applied to carbon intensity.

Emission factors #

Factors are lifecycle (cradle-to-grave) gCO₂eq/kWh, applied per ENTSO-E production type (exact match, never a substring — the same discipline as the renewable-type matching above). Most figures are the IPCC Fifth Assessment Report (AR5, 2014) lifecycle medians, the same default set published by Electricity Maps: nuclear 12, wind onshore 11, wind offshore 12, hydro 24, solar 45, geothermal 38, gas 490, hard coal 820. A few types have no clean AR5 figure and are documented estimates instead: lignite/brown coal (1080, scaled from the AR5 hard-coal median — lignite plants run materially less efficiently than hard coal), oil (650, from the UK Parliamentary Office of Science and Technology, the same non-AR5 source Electricity Maps itself uses), oil shale, peat, coal-derived gas, marine, "other renewable" and waste.

Biomass (230 g/kWh) is explicitly flagged as contested. It is the AR5/Electricity Maps figure, used here for consistency with the rest of the table — but biomass accounting varies enormously by convention, from near-zero (treating combustion as carbon-neutral at the point of burning) to well over 700 g/kWh (full land-use and harvest-cycle accounting). This site picks one number and states it plainly rather than picking the most flattering one.

Excluded from both the numerator and the denominator: Hydro Pumped Storage (B10) — it is storage, not net generation, so including it would double-count electricity that was itself drawn from the grid earlier — and "Other" (unspecified mix) — its composition is unknown, so folding it in at any factor would either inflate or dilute the intensity with an uncosted guess. Both exclusions mean the qualifying-generation denominator can be smaller than total reported generation for a zone; the figure is the intensity of the costed mix, not a claim about 100% of generation.

As with every other metric on this site: where a zone's generation mix is missing or incomplete for an hour, the low-carbon share and CO₂ intensity are shown as absent, never as a false zero.

Residual load & system stress #

This section covers residual load itself, the seasonally-normalised “tight hour” threshold built on it, the records and yearly trend that follow from it, and cross-zone concurrency. One point applies to all four, worth stating up front and repeating below: residual load is not a system margin. It says how much demand wind and solar did not cover — nothing about how much spare capacity actually remained.

Residual load #

load − wind − solar, per zone, per hour, in MWh (shown in GW on the yearly charts below, for readability). It measures how much demand had to be met by sources other than wind and solar in that hour.

It does not measure spare capacity or reserve. Hydro, nuclear, imports and every other dispatchable source are excluded from the calculation, so a high residual-load hour can still be entirely comfortable if those had slack to give — it is not a system margin. As with every other derived figure on this site, it is shown as absent, never as a false zero, whenever load, wind or solar is missing for that hour. It is available as a plottable axis in the free explorer, alongside every other series.

Tight hours #

An hour is “tight” for a zone when its residual load is at or above the 90th percentile of that zone's own residual load for that (month, local hour) — never an absolute cutoff, always unusual for that particular season and time of day, for that particular zone.

The threshold is computed the same way as the site's seasonal wind+solar bands: per (zone, month, local hour), over a rolling 5-year window, with any cell that has fewer than 30 hourly observations (n < 30) skipped rather than given an unreliable threshold. A whole-history top decile would simply flag “winter, early evening” for every zone, every year — true, but uninformative. Normalising by month and local hour instead means a tight hour is unusual for that hour and time of year, in that zone — the same reasoning behind the site's seasonal wind+solar highs and lows, applied here to residual load.

“Tight” is a relative, seasonal label, not an absolute reading of stress — and, as above, it is not a system margin.

System-stress records & yearly trend #

The tightest hour is the all-time Europe-wide maximum residual load, with its UTC timestamp, alongside the highest residual-load hour of each year. It uses the same absent-data handling, reporting-lag frontier and zone-coverage guard as every other Europe-wide record on this site (see Records). The yearly chart can show that same hour as a share of consumption (residual load ÷ demand) instead of GW — more comparable across periods and zones than an absolute figure, though it is still the size of the gap, not spare capacity. A zone selector scopes the same chart to a single zone's own tightest-hour history instead of the Europe-wide figure, Europe being the default.

Layered residual load #

The residual load above subtracts only wind and solar. The site also computes a cumulative version that peels off low-carbon sources one tranche at a time, to show which technology, if any, closes the gap between demand and clean supply. Four layers, each subtracting more than the last, all in actual generated MWh:

• wind + solar — load − wind − solar (the base residual load above).
• hydro — additionally subtracts run-of-river and reservoir hydro (B11, B12). Pumped storage (B10) is excluded here for the same reason it is excluded from the renewable share: its output is grid electricity pumped uphill earlier, not fresh generation.
• nuclear — additionally subtracts nuclear.
• all low-carbon — subtracts the full renewable set (not only hydro) plus nuclear.

The layers are production-based: each subtracts the electricity a technology actually generated in that hour, from ENTSO-E generation by type. This is the key limit to keep in mind — it is not a margin or an adequacy calculation. A layer says how much demand a given set of clean sources did not cover that hour; it says nothing about how much of each source could have been dispatched but was not.

Where a zone structurally never operates a technology — most zones have no nuclear, some have no hydro — that technology contributes zero to the subtraction, which is correct: there is nothing to subtract. Where a zone does operate it but the value is missing for an hour, that hour is left absent rather than treated as a false zero, as everywhere else on the site.

A tight hour at any layer uses the same seasonal threshold as the base tight-hour definition above: the 90th percentile of that layer's own residual for the zone's (month, local hour), over a rolling 5-year window, with cells of fewer than 30 observations skipped.

Two views are built on these layers. On the time-of-day chart, a layer selector switches both the residual curve and the tight-hour markers between layers, so you can see, for one zone and day, how much of the residual each tranche of clean supply removes. And the cross-zone matrix is computed per layer, so the same pair of zones can be tightly coupled at the wind+solar layer and decoupled once hydro or nuclear is added — which is the point of layering: it shows which source breaks the coupling.

Unlike simply counting how many zones were tight at once, the matrix reads pairwise and directionally. For an ordered pair of zones (A, B) in a given year, it shows the share of A's tight hours in which B was also tight. This is asymmetric on purpose: a small zone that is tight whenever a large neighbour is tight is not the same as the reverse, and averaging the two directions would hide exactly the dependency that matters. Only hours where both zones were reporting, and had a threshold, are counted.

One wording caveat, stated plainly because it is easy to get wrong: the top layer's remainder is not “fossil generation”. After subtracting all counted low-carbon sources, what is left is demand met by everything else — fossil fuels, but also waste and unspecified-mix generation, the discharge of pumped storage, and net imports (the figures do not track cross-border flows). It is the demand no counted low-carbon source covered, not a measurement of fossil output.

Dispatchable capacity margin #

Residual load above says how much demand wind and solar did not cover. This figure goes one step further and asks whether the rest of the system could plausibly have covered it: available dispatchable capacity minus residual load. Available dispatchable capacity is installed capacity summed across every non-wind/solar generation type, minus whatever capacity is currently reported as unavailable in ENTSO-E's outage feed. margin = available dispatchable − residual load; a ratio (residual load ÷ available dispatchable) is also available. A negative margin means demand exceeded what domestic dispatchable capacity alone could plausibly supply that hour — it does not mean a blackout occurred (see the caveats below).

Data sources: ENTSO-E's unavailability of generation units feed (15.1.A&B, document type A80) for outages, and installed generation capacity per production type (14.1.A) for the nominal ceiling. Both are reconstructed to an hourly series at build time: installed capacity minus the sum of currently-reported outages across every dispatchable type, at every hour.

Three caveats apply, shown with the chart itself:

• Imports are excluded. This is a domestic dispatchable-capacity proxy, not a full adequacy calculation — cross-border flows are not tracked on this site (see Known limitations). A negative margin means domestic dispatchable capacity alone would not cover residual load, not that imports could not either.
• It is an upper bound, never an exact figure. The reconstruction is only as complete as what generators reported to MAVIR/ENTSO-E. An outage that is unreported, or reported late, is invisible to this calculation until it appears in the feed — so the true margin can be tighter than what is shown here, never wider.
• It is a capacity ceiling, not deliverable energy. Hydro reservoirs, pumped storage and similar sources count as "available" at their full nameplate capacity whenever they are not reported as unavailable, regardless of whether water, fuel or economics would actually let them run at that level for that long. The figure answers "how much capacity exists on paper," not "how much energy could actually be delivered."

Note on Hydro Pumped Storage (B10): pumped storage counts as dispatchable capacity here — a source of controllable, on-demand output — even though it is excluded from the renewable share and from the residual-load layers above (where counting it would double-count electricity drawn from the grid earlier). The two treatments are deliberately different: one measures net generation, the other measures capacity that can be called on.

Coverage: twenty zones, each independently qualified by its own outage-reporting completeness, from 2019 onward — earlier years are too thinly reported everywhere to reconstruct reliably: DE, FR, ES, PT, NL, AT, CH, PL, HU, CZ, RO, BE, BG, FI, DK-2, DK-1, SI, SK, NO-4 and NO-5. Five of these — DK-1, SI, SK, NO-4 and NO-5 — have measurably thinner outage-report coverage than the rest (5–25% of hours since 2019 with no active outage-report coverage, versus under 5% for the other fifteen) and carry an extra "reporting is incomplete for this zone" caveat alongside the three above. Six — PT, AT, CH, RO, NO-4 and NO-5 — draw more than half their dispatchable installed capacity from hydro and pumped storage, where "available" is an even weaker proxy for deliverable energy than for thermal or nuclear plant (see the note on Hydro Pumped Storage below); those carry an extra energy-limited caveat.

Fifteen zones are not covered, for three different reasons. Four (GR, HR, NO-1, NO-3) report outages too thinly to trust — at least a quarter of hours since 2019 have no active outage-report coverage, and for Croatia (HR) that is true of all of them: zero outage records exist for that zone in this window. Ten — Italy's six bidding zones (IT-NORD, IT-CNOR, IT-CSUD, IT-SUD, IT-SICI, IT-SARD) and Sweden's four price zones (SE-1, SE-2, SE-3, SE-4) — are excluded for a structural reason instead: ENTSO-E only publishes installed generation capacity (14.1.A) for Italy and Sweden at the whole-country level, not per bidding zone, so there is no legitimate zone-level denominator to compute a margin against; splitting the country total across zones by assumption would fabricate a number, which this site does not do. (Two of the ten, SE-2 and SE-4, would also have failed the reporting-completeness bar on their own.) The remaining zone, NO-2, is excluded despite clean reporting and a real installed-capacity figure: 99.5% of its dispatchable capacity is hydro and pumped storage, leaving essentially no non-energy-limited ceiling for the metric to measure against.

A concrete, dated illustration of the upper-bound caveat above: the August 2026 Danube-drought shutdown at the Paks nuclear plant was a forced, cooling-water-driven event, but it was filed under "Planned maintenance" and appeared in the outage feed with a multi-day lag. The metric still captures the resulting drop in available capacity — actual generation and the outage feed agree on the event itself — but the planned/forced classification and the reporting lag are exactly the kind of imprecision the upper-bound caveat describes.

Weather-normalised consumption trend #

This is the site's attempt to answer: once you remove the effect of weather, is underlying electricity consumption rising or falling?

For each zone, daily consumption is regressed on heating and cooling degree-days using ordinary least squares:

load ≈ b₀ + b₁·HDD + b₂·CDD

The fitted coefficients give the estimated weather sensitivity. Each day's consumption is then adjusted to a common reference weather — the mean HDD and CDD — by removing the weather-driven deviation:

normalised = load − b₁·(HDD − meanHDD) − b₂·(CDD − meanCDD)

The normalised daily values are averaged per year to produce the trend line. In effect: what would consumption have been if every day had had average heating and cooling demand?

The regression is fitted on each zone's full available history (roughly 7–11 years of data per zone), and the result is shown for the selected period only — so the trend's shape and level are stable regardless of what period you have selected for display.

In one sentence: the trend is weather-normalised — the effect of temperature is removed using heating and cooling degree-days (HDD/CDD) via a linear regression on each zone's full history — so the remaining movement reflects factors other than the weather.

Disclaimer shown with the chart: a deliberately simple linear model (consumption ~ HDD + CDD); it does not account for weekdays, holidays, economic cycles, or growth in renewable capacity. It is an indicative trend, not a forecast.

What this trend does and does not claim #

It removes the influence of temperature. It does not isolate any single other driver. Whatever remains — economic activity, the pandemic, efficiency gains, electrification of heating and transport — is left in. A falling normalised line means “consumption fell for reasons other than the weather,” not “the economy shrank” or “efficiency improved”; those are among the possible causes, not a conclusion.

Three further limits are inherent to the method:

• The model assumes a constant weather sensitivity over the whole period (one b₁ and one b₂ per zone). If a zone's response to temperature changes over time — as heat-pump and air-conditioning adoption grows — that change is not captured by the model and instead appears in the normalised trend.
• Degree-days come from a single representative point per zone (see above), so the weather signal is an approximation of the zone's true weather.
• It is a linear model in HDD/CDD with no calendar terms (no weekday/holiday effects). It is a deliberately simple, transparent specification, not a forecasting model.

Note: the “normalise” toggle in the free explorer is a different operation — it rescales each zone's series by that zone's own mean so multiple zones can be compared on one axis. It is unrelated to the weather normalisation described here.

Records #

Records are computed over the full history (or the selected period, where the period view is active):

• Peak consumption — the highest Europe-wide load hour (or day, in period view).
• Highest / lowest wind+solar hour — the hours with the greatest and smallest Europe-wide (wind+solar)/load. The lowest is the deepest Dunkelflaute.
• Tightest hour — the highest Europe-wide residual load hour (all-time), by UTC timestamp, plus the highest hour of each year. See Residual load & system stress.

Day basis #

Every daily figure is computed on two bases — the local calendar day of the zone and the UTC calendar day — and stored separately. The map, “now” figures and records use the UTC basis for cross-zone comparability; the day picker and hourly drill-down respect this so a single selected day shows its 24 hours consistently.

Update cadence & revisions #

The pipeline runs hourly. On each incremental run it re-fetches the most recent 10 days and overwrites them, because ENTSO-E revises recent values after first publication; the rolling re-fetch lets those corrections flow through rather than freezing first-published numbers.

Deliberate exclusions #

• Germany before 1 October 2018. Until then, Germany shared a single combined bidding zone with Austria and Luxembourg (DE-AT-LU). Austria is tracked separately on this site, so including the pre-split combined data would double-count Austrian (and Luxembourg) consumption in the European totals. The German (DE) series therefore begins on 1 October 2018, when the combined zone split into a standalone DE-LU zone. This is why an automated coverage check flags Germany's early years as “missing” — the data is omitted on purpose, not lost. (A two-day junk fragment in 2015 is removed by the cutoff rule.)
• Europe as one system. Countries are not weighted or singled out; European aggregates are straightforward sums of the included zones.

Known limitations #

Coverage is complete across the 35 zones aside from the deliberate German exclusion above. The figures carry a few inherent limitations worth knowing:

• The renewable share compares a zone's local generation to local consumption. In import-dependent demand centres (for example Sweden's SE-3) the figure can read lower than the share of green electricity actually consumed there, because some of that electricity is imported.
• Net-exporting zones can exceed 100% — the northern Norwegian and Swedish zones (NO-5, SE-2, NO-2, NO-4, SE-1) generate more renewable power than they consume and export the surplus, so renewable-over-consumption rises above 100%.
• Some countries also report generation under aggregated or “not specified” categories that ENTSO-E does not break down by type; that output is not counted in the renewable sum, so the true renewable share can be somewhat higher than shown in those zones.
• The German (DE) zone begins on 1 October 2018 (see Deliberate exclusions).
• Residual load and the tight-hour/concurrency metrics (see Residual load & system stress) inherit the same absent-data handling as every other figure here, and the same caveat stated above: a high reading means demand outran wind and solar that hour, not that the system was short of spare capacity.

Reproducibility & stack #

The site is static and build-less: pre-generated JSON/parquet files served directly, read in the browser at runtime, with charts drawn by Observable Plot and the map by d3-geo. The processing pipeline is a single Python script using DuckDB. There is no server-side computation at request time — what you see is the output of the last hourly build.