Solar disaggregation: experiments
This page lists every experiment tried in the theme, newest first. An experiment that belongs mainly to another theme but bears on this one is marked "also relevant".
| Experiment | Hypothesis | Verdict | Production status | Take-home message | Relevance |
|---|---|---|---|---|---|
| 00370 mva-fold-pv-capacity-and-eight-primaries | When the registered rule of experiment 366 chooses between the fit through the meter (L0) and the naive fit (N), the solar capacity of the chosen fit (the naive fit N or the fit through the meter L0) stays close to the capacity of the fit to the signed series at the six Primaries with signed MW readings, and at the eight MVA-only Primaries the fit through the meter finds reverse flow at the substations NGED marks "Yes" or "Maybe", not only at Stickney (D12). | partly supported | not-for-production | With the meter's reactive power at zero, the rule of experiment 366 recovers each signed-MW Primary's solar capacity to within 3.1% of the signed fit, and the fit through the meter fits Stickney's sunny-half-hour readings with a reading error 9.3 points of capacity lower than the naive fit's. The fit through the meter does not find reverse flow at Marsh Lane (D8) or Langrick (D22), so either those substations have little reverse flow or the fit cannot see it. The pinned solar capacity is 45% and 58% above the signed fit's at D9 and D16, which is a reason to treat the pin as a reference and not as the truth. | also relevant |
| 00366 mva-fold-irradiance-correlation-reverse-flow-rule | Reverse flow can be told apart from invented reverse flow using the meter readings and the irradiance forecast alone: where a substation has reverse flow, its sunny-midday meter readings rise with irradiance (the magnitude of a negative flow grows), and where it has none, they fall. | partly supported | not-for-production | A test on how the midday meter readings move with sunshine, together with a test for readings near zero, picks the right fit at the substation with no reverse flow and at every substation where solar output was added, and it picks reverse flow at Stickney (D12) and at no other MVA-only Primary. All registered pass criteria are met with reactive power zero. The verdict is "partly supported" only because an unregistered check, at power factor 0.97, fails. The rule only works when the meter reading has no reactive power in it. At power factor 0.97 the readings no longer show the reverse flow, so the rule stays with the naive fit and loses badly at moderate injected solar. The 1.0 threshold on the low readings was chosen after seeing experiment 365's numbers, and it carries the moderate-injection cases. | also relevant |
| 00365 mva-fold-readings-only-reverse-flow-rule | Reverse flow can be told apart from invented reverse flow using the meter readings alone: if the training readings on sunny midday half-hours never come close to zero, the substation has no reverse flow, and the naive fit (reading the magnitude as signed power) is right. If they do come close to zero, the fit through the meter is used. | partly supported | not-for-production | Looking for midday meter readings near zero does not reliably tell whether a substation has reverse flow. The rule still picks the wrong fit at the substation with none (D5), because that substation's readings also dip towards zero. The rule also misses heavy reverse flow, because heavy reverse flow gives large readings, not small ones: the rule picks the naive fit at Stickney (D12), the MVA-only Primary expected to have it. The registered pass test is met only because it allows one of five substations to be wrong. A rule that uses how the readings move with sunshine is an idea to test next. | also relevant |
| 00362 mva-fold-gated-fit-known-answer | A gate that accepts reverse flow only where an independent solar forecast exceeds the fitted demand stops the fit through the meter from inventing reverse flow at a substation with none, without losing the reverse flow that real solar output produces. | refuted | not-for-production | A fit to meter readings that give only the size of the power flow sometimes invents reverse flow (power flowing back to the grid) at a substation that has none. Rejecting such fits with an independent solar forecast does not work. The registered gate (ratio 0.75) rejected no fit, and the stricter gate (ratio 1.0) rejected the correct fits where reverse flow is heavy. The next test is a rule that uses the meter readings alone: accept reverse flow only if the training readings come close to zero at midday. | also relevant |
| 00358 mva-fold-known-answer-at-folded-mw-primaries | When the signed power of a Primary substation is folded to apparent power (its magnitude), the convex model fitted through the meter (experiment 110's L0) recovers the sign of the flow and the solar capacity, and a model that reads the folded reading as signed power does not. | partly supported | not-for-production | A substation meter that reports only the size of the power flow, not its direction, still lets the model fitted through the meter recover the direction and the solar capacity about as well as a model that sees the signed power, as long as reactive power is zero. The model also invented reverse flow at the 1 of 5 substations that has none, so reverse flow it reports at a real substation needs an independent check. The pre-registered test could not be passed as written, because with the larger injection almost every sunny half-hour has reverse flow, so only one flow direction is present and balanced accuracy cannot be computed. | also relevant |
| 00352 primary-demand-default-3-capacity-response-surface | The score of the first (convex) stage of Primary demand research default 3 on sunny daylight half-hours is nearly flat in the solar capacity over 0.8 to 1.2 times the pinned capacity when the Kalman filter is on, because the filter absorbs the difference. Experiments 349 and 350 found no reliable gain from a different capacity, and a stored-data analysis suggested a flat response. Without the filter the response is steeper. | partly supported | not-for-production | The demand forecast's score on sunny half-hours moved by only 0.010 points (-0.104 to +0.088) when the assumed solar capacity was 20% too low, but rose by 0.124 points when it was 20% too high and by 0.358 when it was 40% too high, so a capacity estimate above the pin is the risk and a lower capacity estimate is not. The Kalman filter halves the rise from too much capacity, which supports the idea that the filter absorbs part of the capacity error, but leaving out D5 cuts the rise at 20% too high from +0.124 to +0.074. Chasing a more exact capacity cannot be justified by this result: no scale from 0.6 to 1.4 beat the pin by more than 0.03 points with the filter on. | also relevant |
| 00351 primary-demand-capacity-physical-shape-readout | Experiment 349 read the solar capacity with the empirical plant shape of six metered farms (experiment 269) but multiplied it by the stack's physical unit shape, a mismatch its page lists as a lab shortcut. Reading the capacity out in the physical unit shape the stack itself uses, with the same monthly as-of refit and its own injection divisor, removes the mismatch. If the mismatch explained experiment 349's null result, the sunny-half-hour score improves. | not supported | not-for-production | Estimating how much unmetered rooftop solar sits behind each substation with the clear-sky daily output curve that the forecast already uses did not improve the demand forecast on sunny half-hours. The quantile score was 0.065 points worse than with the fixed capacity from experiment 45 (95% interval +0.017 to +0.126). The two output curves give scores within 0.018 points of each other (-0.020 to +0.054), so mixing two curves in experiment 349 does not explain why it did not improve the forecast. Leaving out either of two substations (D16 or D10) makes the interval include zero, so the harm rests on those two. | also relevant |
| 00350 primary-demand-capacity-free-irradiance-curve | The convex model of Primary demand research default 3 subtracts a PV term, a pinned PV capacity (from the frozen weekly vintages of experiment 45) times a unit solar shape built from irradiance (35 degree tilt, south facing, temperature derated). Experiments 110 and 139 found that no fitted or as-of capacity improves on the pin, so the capacity estimate is the part of the stack most likely to be wrong. Replacing the pinned capacity and the fixed unit shape with a learned, monotone function of irradiance, scaled per Primary by a number the fit learns, keeps the forecast as good on sunny daylight half-hours and keeps it as good elsewhere, so the capacity estimate is not needed. | not supported | not-for-production | Dropping the PV capacity estimate is not yet safe: a learned curve of irradiance does no better than the pinned capacity over five of the six Primaries (+0.025 points, interval -0.252 to +0.346). Only D5's interval excludes zero (-0.969), where either the pinned capacity or the unit solar shape is probably wrong; D11 is significantly worse with the curve (+0.223 [+0.100, +0.360]). The useful next step is to check the D5 pin and unit shape, and to keep the curve as a low-effort sanity check on each Primary's pin and as a fallback for a Primary with no trustworthy capacity. | primary |
| 00349 primary-demand-default-3-exp-269-capacity-in-the-stack | Primary demand research default 3 subtracts a solar capacity pinned to experiment 45's frozen vintage (a hindsight growth prior) times a unit solar shape. Replacing that capacity with the experiment 269 estimate (the mean plant response of six metered farms, read at the Primary's satellite irradiance, with the capacity fitted from rows before each fold's origin) lowers the quantile score on sunny daylight half-hours, because the experiment 269 read-out recovers 0.89 to 0.97 of an injected farm where the physical fleet shape recovers 0.78 to 0.83. |
inconclusive | not-for-production | Using the capacity estimated from six metered solar farms in place of the capacity pinned from earlier experiments did not improve the demand forecast on sunny half-hours: the score was 0.047 points worse, within the noise (-0.029 to +0.135), and the replicate year (2024-07 to 2025-06) pointed the same way. The estimate is lower than the pin at several Primaries, most of all at D16 where the forecast got worst. This was tested on the convex stage out to 7 days only, so the full stack and the 7 to 10 day range are untested. | also relevant |
| 00289 summer-gap-weather-input-discriminator | The best-guess stack's weaker April to June score than BG2 at the decision Primaries comes from the ensemble's sunshine forecast (or its ratio calibration), and not from demand absorbing the solar term or from the error-correction layers. | partly supported | not-for-production | Giving the best-guess stack the sunshine that actually happened would close about 70% of its April to June gap to BG2, but the same change helps the stack nearly as much in July to September, when the stack already matches BG2. Most of the April to June gap beyond the July to September gap therefore stays. That points to the solar term in the demand model, or to the error-correction layers, and away from the ensemble sunshine forecast. A refit with realised sunshine as the solar driver, and a check of the solar term at D5 and D9 in April to June, are the next steps. | also relevant |
| 00282 pv-plant-curve-audit | The curve that turns sunshine into power in our solar models departs from the metered farms' measured curve by more than 5% of capacity somewhere in the clear-sky range, and the departure is the flat-top level (the inverter limit), not the panel tilt or the temperature loss. | not supported | not-for-production | The curve that turns sunshine into solar power stays within 5 percentage points of capacity at 3 of the 6 metered solar farms and departs from their measured output at the other 3 in different directions, so no single fix to the inverter limit repairs it. The best-guess demand stack assumes panels tilted 35 degrees and the farms fit best at about 20 degrees, which makes the stack over-predict output on clear half-hours in December (measured is 0.66 of the stack, median over farms) and under-predict it from April to July by 5 to 8%. Swapping in the pooled shape of the other farms does not help, so the next experiment should try the 20 degree tilt inside the stack. | also relevant |
| 00280 pv-empirical-shape-and-capacity-in-arm-d | The April to June 2026 gap between arm D (the monthly-refit generalised additive model of experiments 155 and 220, scored on experiment 178's rows) and BG2 comes from its solar term: the pinned solar capacity is too high and the south-facing fleet shape is not the shape the metered farms show. Replacing the fleet shape by the pooled empirical shape of experiment 269, at the capacity that shape's real-series fit supports, lowers arm D's April to June error at the substations where the pinned capacity looks too high (D5, D9, and D16). | not supported | not-for-production | Putting the empirical solar shape into the forecast, at the pinned capacity or at the lower capacity the real series supports, does not lower the April to June error at the substations where the pinned capacity looked too high. The change is under 0.01 MW against an error of about 0.5 MW, and the intervals are wide. A capacity cut that is correct for the whole of April to June is not the fix either: at D5 it raises the error. This experiment finds no sign that the shape or the capacity of the solar term closes the summer gap to BG2. | also relevant |
| 00269 pv-capacity-gain-decomposition | Most of the 0.090 gain that experiment 238 found from using a nearby farm's recorded output as the solar shape comes from the farm's plant shape (tilt, orientation, flat top) and from the capacity unit in which recovery is read, and little comes from the farm's measured sunshine. | partly supported | not-for-production | For the five substations and six farms tested, the measured sunshine at a nearby farm explains almost none of the extra solar output that its recorded output helped to find. The extra output found comes from the farms' plant shape (panel tilt, panel orientation, and the flat top of the output curve). A table learned from other farms' output and driven by the satellite irradiance reproduces that plant shape. Using the table finds 0.966 of an injected solar farm's output when recovery is read as the capacity ratio of experiment 231 (0.826 with the physical fleet shape), and 0.995 when it is read over clear-sky daylight energy. In these tests the satellite irradiance was not the limit, and a metered farm beside each substation was not needed. Switching to the table lowers the fitted solar capacities of the five decision substations by 0.2 to 0.4 MW. The effect on the sunny-half-hour forecast score has not been run. | also relevant |
| 00267 pv-capacity-early-years-and-register-floor | The inferred capacity of unmetered solar at the five decision Primaries falls below the register floor in season-year 2021 (July 2021 to June 2022) because the capacity fit recovers only about 0.78 of an injected farm and the 2021 fit window is thin, not because the register is shared to the wrong district. Dividing by the median recovery of the metered farms, and using the output of a metered farm within 25 km as the sunshine shape, lifts the estimates to the floor. | not supported | not-for-production | The 2021 shortfall at D10 and D11 is not a missing recovery correction and not a thin fit window: the card already divides by the metered farms' recovery, and the 2021 season-year has 12 months of data at every Primary. Using a nearby metered farm's output as the sunshine shape makes the shortfall no smaller (the estimates fall by 7% to 22%, partly because the two shapes are divided by different recoveries), so the shortfall at those two Primaries remains unexplained. Candidate causes are the register's postcode-district share, a gap of about 20 months in the early data, and a fit that is too low in its first full year. Show 2021 at D10 and D11 as a known shortfall. | also relevant |
| 00266 summer-gap-diagnostics-solar-term | The best-guess demand stack (experiment 251's arm 1) trails BG2, the production-style XGBoost forecast, in April to June 2026 mainly because of its explicit solar term (the subtracted estimate of unmetered rooftop solar output), and not because of the neural-network head, the two-number summary of the ensemble's solar response, the Kalman filter, or the heating and lighting terms. The same defect explains the loss at D5, an anonymised Primary substation, on sunny half-hours. | partly supported | not-for-production | The best-guess stack trails BG2 in April to June because of the solar term at two of the six substations (D5 and D9 on daylight half-hours) and a night-time error at a third (D10), not because of the neural-network head, the two-number summary, the Kalman filter, or the ensemble sunshine forecast; the head and summary actually reduce the gap. The pinned solar capacity looks too high in those months, not too low, and clear mornings are forecast worse than clear afternoons. Candidate next experiments are the export clip at D9, a morning-and-afternoon solar shape, and a look at D10's night residuals; the stack's added parts need no change for this gap. | also relevant |
| 00241 pv-capacity-calibrated-interval | The recoveries that experiment 129 measured when it injected real solar farms into a Primary substation's power can be turned into a calibrated estimate of unmetered solar capacity: dividing the capacity found by the Primary's median recovery over the other farms corrects its bias, and the spread of those recoveries gives an 80% interval that covers the truth about 80% of the time. | partly supported | not-for-production | Dividing the solar capacity found by the typical recovery of other real farms removes most of the bias: the estimate lands within 10% of the truth at all five Primaries, against a typical error of 19% without the correction. The 80% interval built the same way covers the truth 59% to 79% of the time, which is too uncertain to label as an 80% interval, and it fails completely when the demand follows cloud (0% to 2%). Keep showing the range as a spread over scenarios, with the correction applied. | also relevant |
| 00239 pv-capacity-seasonal-efficiency-curve | The metered solar farms' output falls 6% to 9% from spring to summer by a pattern that no clip or heat term explains. A seasonal efficiency curve of 12 monthly factors, shared by all farms, fitted on the farms' own output, and then applied to the solar shape in a Primary substation's capacity fit, lets that fit find more of an injected real solar farm. | not supported | not-for-production | Twelve monthly efficiency factors fitted on the metered farms' output do not close the recovery shortfall. The farm-only fit rises by 0.003, below its target, and the recovery of an injected farm rises by 0.039 or 0.025 depending on whether the injected farm keeps its low-sun output. The farms fall below the satellite shape in November to January, and show no fall of 6% to 9% from spring to summer. Keeping the injected farm's low-sun output, which experiment 231 zeroed, raises the recovery by 0.033 (paired median over farms) on its own. | also relevant |
| 00238 pv-capacity-nearest-farm-sunshine-reference | Using the recorded output of a metered solar farm within 30 km of a Primary substation as the measure of sunshine, in place of the CAMS satellite irradiance, lets the capacity fit of experiment 129 find more of an injected real solar farm. | partly supported | not-for-production | A nearby farm's measured output finds more of an injected solar farm than the satellite irradiance does: 0.867 against 0.780 of its output, and at least 0.05 more at all five Primaries. The gain is 0.02 to 0.14 when the two farms are 5 to 25 km apart and 0.17 to 0.24 for the one pair of farms 2 km apart, and it falls to 0.052, reaching 0.05 at 3 of 5 Primaries, when recovery is read in the capacity unit of experiment 231. The farm may carry plant effects as well as sunshine, so the result does not yet show that the satellite irradiance is the limit. | also relevant |
| 00236 pv-capacity-clear-sky-fit | Fitting the capacity of the unmetered solar behind a Primary substation only on half-hours with a clear sky and a high sun, where the irradiance error is smallest, finds more of an injected real solar farm than the fit of experiment 129 on all rows. | refuted | not-for-production | Fitting the solar capacity only on clear, high-sun half-hours did not find more of an injected solar farm: 0.818 against 0.827 for all rows, with an interval 1.8 times wider. The farm alone fits better on those half-hours (0.965), so the demand terms seem to absorb more of the solar signal when only clear half-hours are used (0.147 against 0.104). Do not restrict the rows by sky condition; reduce demand absorption instead. | also relevant |
| 00235 pv-capacity-matched-day-pairs | The capacity of unmetered solar behind a Primary substation can be read from the difference in net power between a sunny day and a cloudy day of the same day type and the same temperature, because ordinary demand cancels in the difference. That estimator should find more of an injected real solar farm than the convex fit of experiment 129 (arm 2), which finds 0.78 to 0.88. | refuted | not-for-production | Pairing sunny and cloudy days removed none of a synthetic load that follows cloud, so the pairs give no evidence that a real cloud-following demand cancels. With the load added, the load adds 0.3 of the injected capacity to the pair estimator's recovery, against 0.2 for the convex fit, and a December-and-January test finds 0.33 of the farm. Pairs of days with at least 10 hours of daylight find 0.91 of the farm, which agrees with experiment 231's finding that winter rows weaken the fit. The load is built to follow the pairing variable exactly, so 0.3 is a worst case. | also relevant |
| 00231 pv-capacity-audit-where-recovery-goes | Of the roughly 0.2 of an injected solar farm's capacity that the capacity estimator of experiment 129 (arm 2) does not find, most is lost because demand terms absorb part of the solar signal, a quarter or more is lost to the irradiance input, and neither the winter rows nor the definition of capacity (the divisor) explains much. | partly supported | not-for-production | The capacity estimator misses 0.17 of an injected solar farm mainly for two reasons: the demand terms of the fit absorb part of the solar signal (58% of the miss), and the fixed solar shape does not reach the farm's flat top (47%). Better sunshine data does not help, because fitting the farm alone on clear half-hours finds no more than fitting it on all daylight. The next experiments should target demand absorption and the shape's seasonal flat top, not the irradiance input. | also relevant |
| 00218 metered-solar-farm-plant-fit | The six metered solar farms differ in panel tilt, panel azimuth, ratio of direct-current to alternating-current capacity, heat loss, and inverter clipping, so a plant model with those parameters fitted per farm reproduces each farm's output better than the one fleet shape (tilt 35 degrees, facing south, DC:AC ratio 1.15, 0.4% loss per kelvin, cell temperature equal to air temperature plus 25 K times the shape), and explains why the flat top of farms C, D, E, and F falls 6% to 9% from spring to summer (experiment 183). | not supported | not-for-production | Giving each metered solar farm its own panel tilt, azimuth, heat loss, and inverter limit fits its output only 0.003 better than one fleet shape (a fraction of output fitted of 0.953 against 0.950), so the plant is probably not what limits the fit. About 5% of the squared metered output stays unexplained, and the sunshine data is the likelier source. Per-farm plant parameters look unpromising for forecasting; a better sunshine forecast looks more promising, because the same plants fit only 0.91 with the ensemble forecast. | also relevant |
| 00215 dp-pv-step-scan-real-series-register-match | The step path of experiment 207 (total-variation weight 3, April to August rows), run on the real 2019 to 2026 series of the Primaries, finds real changes in solar capacity, and the steps that increase capacity fall near the dates when feed-in tariff solar installations were commissioned in the Primary's area. | partly supported | not-for-production | The method that finds jumps in solar capacity finds about as many in the real Primary series as it invents when none exists, so we cannot call any of them real. The only list of dated installations we hold ends in March 2021, before the months the fit uses, so no change could be checked against it. The changes do not alter the forecast, but they should not appear on the substation cards as confirmed. A list covering 2021 to 2026, or the metered record of a known installation, would settle the question. | also relevant |
| 00207 dp-meter-regime-steps-e2-step-capacity-known-answer | A convex solar-capacity path that allows a few sudden jumps (a total-variation penalty on the month-to-month change) finds both the size and the date of a known solar-farm step added to a Primary's power, where a smooth path blurs the step and a path that steps once a year places it within the wrong month. | partly supported | not-for-production | A solar capacity that is free to change each month finds a known solar-farm step added to a Primary's power, to within 2% of its size at the pre-registered penalty, and places it within one month of the true date. At that penalty the fit also invents steps above 0.5 MW in a year without any, at 2 of 5 Primaries, and a stronger penalty that avoids them still finds 89% to 96% of the step. A capacity that changes once a year finds only 24% to 49% of the step. | also relevant |
| 00206 dp-meter-regime-steps-e1-farm-outages-in-primary | When a metered solar farm's meter reads zero for days while the sun is high, the farm's output stays in the Primary substation's supplied power, so the Primary reads more solar generation (lower net power) during the outage than in the 14 days either side. | not supported | not-for-production | When a metered solar farm's meter reads zero for days, the power of the Primary substation we could test (D10) does not change by that farm's output. Meter failures at farms A and C therefore do not explain capacity steps at D10, and the other decision Primaries could not be tested. The farms may connect to substations we could not test, so the connection list from NGED would settle it. | also relevant |
| 00183 metered-farm-flat-top-cause | The flat top at farms C, D, E, and F is a fixed export cap, so its level does not change with season or with the sun's azimuth. | not supported | not-for-production | The flat top of solar farms C, D, E, and F is lower in summer than in spring (by 6% to 9% of its mean level at the 90th percentile, in even and odd years alike), so the flat top does not behave like a fixed export cap. The per-farm plant fit should therefore fit panel tilt, panel azimuth, and a loss that grows with heat or a gradual flattening near the inverter limit, not only one clip level. More cloud in summer half-hours could lower the 90th percentile, because the clear-sky filter is modelled and not observed; the 99th percentile falls by less (1% to 6%). | primary |
| 00182 switching-d8-disaggregated-farms-and-drops | D8 (Marsh Lane) is a disaggregated Primary: the metadata says the output of two solar farms was added back to its reading, and that both farms' analogues are not working. When an analogue fails, D8's reading loses that farm's output, so D8's multi-day drops (experiment 159) coincide with a stuck, zero, or missing reading at the farms, and the drop depth matches the farms' lost output. | inconclusive | not-for-production | We cannot tell from the farms' own readings whether D8's multi-day drops are the two disaggregated farms dropping out, because the readings for the farms are almost entirely absent from the power table and so look the same inside and outside the drops. D8's shortfall is larger in daylight than at night in 24 of 29 drops, which is consistent with lost solar output, but it is also large in the evening, so the daylight pattern does not settle it. The next step is to ask NGED for the farms' output and capacity, or to model the farms from weather and compare the modelled output with the drop depth. | also relevant |
| 00179 metered-solar-farm-exploration | At least 4 of the 6 metered solar farms show AC clipping, meaning output that flattens below the level that clear-sky irradiance alone would give, so that the AC limit can be separated from the DC capacity. | not supported | not-for-production | Four of the six metered solar farms have output that flattens at about 1.05 times their effective capacity once clear-sky irradiance passes about 650 W/m² (read off Figure 1), but the pre-registered test was too strict to say so, and the flat top could be an export cap (a limit on grid export set by the network operator), a panel-angle effect, or a summer-heat effect rather than a limit in the inverters. Before any DC-versus-AC follow-up, check each farm's export-cap record and get an independent DC capacity. | primary |
| 00157 differentiable-physics-joint-pv-capacity-known-answer | Letting the convex demand model (studies.dp_gam) fit the solar capacity as a smooth non-negative path across months, with a penalty on growth, finds more of a known solar farm's injected output than the two-stage capacity of experiment 129, which steps once per season-year and was recovered at 0.78 to 0.88. |
not supported | not-for-production | In this test, letting the solar capacity change month by month did not find more of an injected solar farm than a capacity that changes once a year. For the five farms tested, one scale of the fixed fleet solar shape fits a median of 93% of a farm's output, so the target of 0.95 to 1.05 was out of reach for most farms before the fit ran. No forecast was run, and the monthly capacity is not worth taking to the forecast stage. | also relevant |
| 00152 dp-mva-fold-aware-gam-k-at-mva-only-primaries | At the MVA-only Primaries, the GAM-K forecast driven by the ensemble mean (experiment 136's arm A2) forecasts the MVA reading better when it models the fold: it fits the demand model through the meter (experiment 110's L0), corrects the forecast with a Kalman filter whose state is the signed flow, and builds its quantiles by folding a signed predictive distribution. The gain is concentrated at Stickney (D12), the one trial Primary where reverse flow is the usual state on sunny middays, and the change is harmless at the Primaries without reverse flow. | inconclusive | not-for-production | At Stickney (D12), the one Primary with clear reverse flow, drawing the quantiles around a signed flow and folding them at zero lowered the score by about 0.4 points on sunny middays against the same fit with symmetric quantiles, and it did no harm at the other seven Primaries. The whole-year gain against reading the apparent power as signed power is of similar size, but its interval in the scored year includes zero. The known-answer test on five Primaries with hidden farms passed on the sign at the larger injection and failed at the smaller injection and on the upper tail's coverage, so the experiment does not support a port yet. | also relevant |
| 00139 differentiable-physics-pv-as-of-capacity-in-gam-k | Feeding the convex model with the Kalman filter (GAM-K, experiment 111) the as-of solar capacity of experiment 129 in place of experiment 45's pinned capacity lowers the quantile score on the sunniest third of half-hours, and does not raise it on the darkest third. The mechanism is that the pinned capacity is a hindsight growth prior, and the as-of capacity is read from each Primary's own power before the forecast origin. | refuted | not-for-production | Replacing the pinned solar capacity with a capacity read from each substation's own power did not improve the forecast, and made sunny half-hours worse by 0.11 points of capacity. A more accurate capacity (the hindsight arm) only matched the pin, so the accuracy of the capacity number is not what limits the solar part of the forecast. This experiment does not locate the remaining sunny-day error; it did not test the ensemble's cloud forecast, the solar shape, the Kalman filter, or the non-solar load. | also relevant |
| 00129 differentiable-physics-pv-capacity-known-answer-test | The AC capacity of the unmetered solar behind each of the five decision Primaries can be inferred offline, one value per September-to-August season-year, from CAMS irradiance, ERA5 temperature, and the Primary's net power, with an 80% range that holds its coverage when the real panels differ from the assumed south-facing shape and when a day-long cloud-driven demand is present. | partly supported | not-for-production | The capacity of the small solar panels behind five Primary substations can be read from the substations' power. For 2025/26 the arm 2 estimate is 3.3 to 4.5 MW of AC capacity per Primary, and it grew by 0.4 to 2.8 MW since 2021/22. The range around each estimate is 1.2 to 1.6 MW wide, but it is a spread over test scenarios and not a calibrated 80% interval. The method has not been shown to work on real panels that face other ways or on overcast-day demand, so a card should show the range with those caveats. | also relevant |
| 00113 differentiable-physics-n5-convex-identification-at-realistic-noise | On synthetic substations built from the real calendar and weather, with noise as large and as autocorrelated as the convex model's real residuals, the convex model's 80% month-block bootstrap intervals cover the truth 70 to 90% of the time, separately for each hour band's heating slope and for the latest season-year's PV capacity. Labelled separately, not in the verdict: the day-of-year harmonics and the drift term make those intervals wide, by absorbing the seasonal cycles of temperature and clear-sky PV. | refuted | not-for-production | At real noise levels, the convex model's 80% month-block bootstrap intervals contain the true heating slope and the true rooftop PV capacity 84% of the time each, but a heating slope is weak (the interval is about 38% of the slope wide). The pre-registered verdict is refuted, because with independent noise the capacity interval covers the truth only 52% of the time, and the cause is mainly a bias of about 0.24 points from the drift penalty at its tuned step of 3,000 (at Part 4's step of 30 the bias is 0.02 points and coverage 78%, a post hoc check). The truth here has the model's own form, so the test cannot show the bias that a wrong PV shape causes. | also relevant |
| 00110 differentiable-physics-5-embedded-generation-and-capacity | The convex differentiable-physics model can represent the generation side of a Primary substation without making its demand forecast worse: an MVA meter through an observation layer, a flat unmetered generator found where one exists and not invented where none does, and an export-limited PV clip; and its PV capacity stays the same when the routes by which demand terms can imitate solar are closed. | inconclusive | not-for-production | The demand forecast could not yet account for generators connected behind a substation. Rooftop solar capacity fitted from the data was not precise or consistent enough to trust, so the capacity from outside estimates stays in use. A generator-finding component reported a generator at every substation tested, so it stays switched off. A limit on exported solar helped only at the one substation known to export-limit. Treating the meter as apparent power improved the forecast at the 8 substations that report only apparent power by 0.13 points of capacity, but the test of the meter's flow direction failed, so that gain is provisional. | also relevant |
| 00104 differentiable-physics-2-simple-structural-model | A convex demand-minus-solar model, trained on every usable year with a slowly drifting level per hour band, forecasts Primary demand better than the same model trained on the last 12 months. | inconclusive | not-for-production | A simple model of demand minus solar, trained on 12 months, scored 0.25 points better than lagless XGBoost with its default start (post hoc; a lower score is better). Most of that lead is in the forecast spread, because the error of the mean forecast differs by only 0.08 points, and four of the five pre-registered positive controls failed, so the verdict is inconclusive. More years of history did not clearly help: the scored year and the tuning year gave opposite signs for two history-window contrasts, and a first version of this page had the wrong sign for both because its spread carried the previous year's mean bias. | also relevant |
| 00103 differentiable-physics-1-data-look | The trial area's power records hold enough clean, demand-only history for the series to go ahead as designed: 6 MW Primaries with at least 12 months before July 2025, and 8 MVA Primaries with over 3 site-years each of rows with no sunlight. | supported | not-for-production | The data lets the series go ahead, with three changes: start 13 of the 16 Primaries later than their first record (19.4 months later at D4), fix the lockdown window in advance because the data does not isolate it from drift, and count eligible long-history MW sites at each tuning year's first day (5 at 2024-07-01, so 4 of 5 must improve in Part 2's tuning contrast). The power timestamps show no 30-minute error on either side of the 2026-03-26 fix: at the 6 metered photovoltaic series the median offsets are +0.9 minutes before it and +4.7 after, and the correction is right to within 3 minutes, but offsets at the Primaries are not resolved beyond 0 to +8 minutes. | also relevant |
| 00102 simp1-simplifying-the-bg2-best-guess-before-production | A single weekly-refitted XGBoost model without the three power lags, the solar offset, the 14-day correction, or the blend scores within 0.15 points of capacity of BG2 at the 16 Primary substations, within 0.30 at the 4 bulk and grid supply points, within 0.10 at the 6 solar farms, and within 0.30 at the 3 wind farms. We expect the Primary claim to fail and the other three to be close calls (see the prediction). Separately, the solar offset helps most where it should, at midday in summer at the 5 Primary substations that have one. | partly supported | candidate | Removing the three recent-power inputs from the best-guess XGBoost forecast makes it worse at Primary substations (by 0.36 points of capacity once one substation's commissioning months are cut, and most of that comes from one substation whose demand level fell in March 2026) and at supply points (by 0.28). The wind farm forecasts lost nothing measurable and the solar farm forecasts little. The blend of 5 models can shrink to 3, and the solar offset lowers the score by 0.18 points at midday in summer at the 5 substations that use it. | also relevant |
| 00079 pvdis-7-mw-at-mva-only-primaries | At a Primary substation (a substation that steps 33 kV down to 11 kV) whose meter records only the magnitude of apparent power in megavolt-amperes (MVA), a least-squares fit of the magnitude of "calendar-and-temperature demand minus capacity times a solar shape" recovers the solar capacity and the direction of flow, as long as the power factor is near 1. The fit alternates between choosing a sign for each half-hour and fitting the signed model to the signed series. It is tested first on the 8 Primaries that are metered in MW, by hiding the sign of their readings (taking the absolute value) and recovering it. | partly supported | not-for-production | Hiding the sign of the MW series and recovering it works for the solar capacity (median error 0.09 to 0.11 of the signed fit's, against 0.29 to 1.00 for the naive fit) but not to the pre- registered level for the direction of flow (balanced accuracy 0.78 to 0.81, rule 0.80), so the verdict is partly supported. At 7 of the 8 Primaries metered only in MVA, the fit found no reverse flow. | primary |
| 00078 pvdis-5-current-capacity-vintages | Experiment 00064's arm am (the XGBoost model given a forecast solar output as a base margin) used one embedded-solar capacity per Primary substation, fitted on data up to 2025-06-30 and held for the whole validation period. That capacity is 5% to 19% below experiment 00045's 2025/26 constants at the five series. Giving am a capacity that is re-estimated before each forecast run, using only data from before the run (a real-time vintage of the season-year constant), lowers its quantile score further, and the fitted solar term still carries solar information, because the same arm with the forecast sunshine taken from another date scores worse. |
small gain | candidate | Re-estimating the solar capacity before each forecast run lowered the error of experiment 64's best forecast by only 0.018 points of capacity (98.75% interval -0.036 to -0.004), under the 0.05 threshold, and by 0.033 (-0.086 to +0.012, an interval that includes zero) at leads under 48 hours. | primary |
| 00077 pvdis-6b-fitted-solar-shape-in-arm-am | At the 5 Primary substations (33/11 kV or 66/11 kV substations in the distribution network) of experiment 00064, replacing the all-south solar shape of arm am with a fitted solar shape (a blend of east-, south-, and west-facing output, one set of shares per Primary, fitted on the training window only and held fixed across training years) lowers the quantile score of the forecast, and the gain comes from the timing of the forecast sunshine and not from the shape alone. |
refuted | not-for-production | Replacing the all-south solar shape with a fitted east-south-west shape changed the score of experiment 64's arm am by -0.001 points of capacity (98.33% interval -0.018 to +0.014), so a gain of 0.5% (0.022 points) is excluded. |
primary |
| 00076 pvdis-5-leak-check-beyond-10-days | Experiment 00064's arm am (the XGBoost model given the forecast solar output as a base margin) scored 0.082 points of capacity below d0 at leads of 10 days and beyond, where its margin is zero. That gain does not come from information that was unavailable at forecast time. If it is real, it comes from the offset in the training rows at shorter leads, which removes the solar swings from the training target so that the trees fit demand better at every lead (the cleaner-target effect), or from the fit starting at zero instead of at XGBoost's estimated base score. |
supported | not-for-production | No route for a leak was found, and a refit with no solar term at all reproduces the gain of experiment 64's arm am at 10 days and beyond (0.102 below d1l without the 14-day correction, against 0.098 for am). The gain matches XGBoost starting from zero when a base margin is supplied, and not the solar offset, with the caveat that the reference d1l came from another pipeline. |
primary |
| 00074 an1-same-time-analogues-vs-explicit-solar-forecast-demand | At the 5 Primary substations (substations that feed 11 kV circuits from the 33 kV network) with a clean solar-capacity estimate, when the XGBoost model is refitted every month on the latest 12 months of data (P-roll), an explicit forecast of the unmetered solar output, given to the model as a starting offset (experiment 64's arm am), lowers the daylight April-to-September quantile score by more than same-time-of-day analogues do. An analogue is a recent day, at the same half-hour of the day and of the same day type (working day, weekend, or bank holiday), whose satellite-based sunshine was closest to the sunshine forecast for the target. The model receives the observed power on the K nearest such days (K is 1, 3, or 5) as input columns. Once the explicit offset is in the model, the analogue columns add less than 0.05 points of capacity. |
inconclusive | not-for-production | Both ways of telling the monthly-refitted model about sunshine lowered the daylight error: the explicit forecast of the panels' output by 0.149 points of capacity (99.17% interval -0.201 to -0.104) and the 3 analogues by 0.095 (-0.185 to -0.007) against the reference. | also relevant |
| 00073 pvdis-6-orientation-mix-and-dc-ac-ratio | On the 8 Primary substations metered in MW, (a) the share of east-, south-, and west-facing solar behind each Primary can be recovered from its half-hourly power, and (b) the ratio of DC to AC capacity of that solar can be recovered from a clipping level fitted on clear days. Both are tested first on known answers: synthetic east-south-west mixes of solar output, and the output of the six metered solar farms, added to the real demand. | partly supported | not-for-production | A blend of east, south, and west shapes lowers held-out error by 1.3% against all-south panels (98.75% interval -2.4% to -0.5%, better at all 8 Primaries), although the shares themselves are not identified: with noise added to the known answer the median share error is 0.200 (limit 0.15), and the shares of the free fit change by a median 0.24 between consecutive season-years. | primary |
| 00072 p35-step-loss-monitor-for-solar-farms | For the 5 metered solar farms with a long record (labelled A, B, C, D, and F), a monitor can find a loss of generating capacity, such as a lost inverter, from the farm's own power telemetry. The monitor divides each day's power by the power a physics model expects on clear, unclipped hours, divides that ratio again by the same day's ratio at the other farms, and fits a piecewise-constant level to the result with an L0 penalty (an optimal-partitioning change-point fit). It finds a 25% loss that lasts 30 days, starting from April to September, within 28 days of its onset in at least 80% of injections, at no more than 0.5 false alarms per farm-year. It finds more injected losses of 10% than two simpler monitors that are tuned to the same false-alarm rate: a rolling median of the same signal, and the hysteresis rule of the capacity studies (called HYST here) run on the same signal. | refuted | not-for-production | A change-point monitor of a solar farm's daily output found 0.84 of 25% losses lasting 90 days and 0.45 of those lasting 30 days, below the 0.80 the plan needed, so the verdict is refuted. On 10% losses it did not beat a rolling median by an interval that excludes zero (+0.045, 97.5% interval -0.015 to +0.090). | also relevant |
| 00064 pvdis-5-net-demand-as-demand-minus-forecast-solar | At the 5 Primary substations (substations that feed 11 kV circuits from the 33 kV network) with a clean solar-capacity estimate, a demand forecast that carries an explicit solar term (the substation's embedded solar capacity times a calibrated forecast of the solar output) scores no worse than experiment 00033's best-guess recipe d0 by more than 0.10 points of capacity on daylight April-to-September rows, and the solar term carries real solar information, because the same arm with the forecast sunshine taken from another date scores worse. The solar term is built in four ways: added to the forecast after the fit with a coefficient fitted on the training window (sf), given to the XGBoost model as an input column (a), given to it as a starting offset so that the trees fit only the remainder (am), or removed from the training target and added back after the fit (b, "demand minus solar"). |
supported | candidate | Giving the XGBoost model the forecast solar output as a starting offset, with a weight fitted on the training window, beat the best guess d0 by 0.152 points of capacity (98.75% interval -0.199 to -0.111; leads under 48 hours -0.272, -0.420 to -0.145), and each of the four arms beat its own placebo twin. |
primary |
| 00063 sw2-10-linear-joint-fit-v2-in-miniature | On the 8 Primary substations metered in MW, one linear fit per Primary that holds a weather baseline without radiation inputs, the embedded-solar capacity of experiment 00045 times the CAMS unit PV shape, and one share-of-the-donor's-net-power term per labelled transfer, (C1) lowers the error of the median forecast outside switching on held-out months, in hindsight, below that of the same baseline with radiation inputs by at least 0.35 points of capacity, (S1) recovers an injected solar capacity to within 0.15 of its size, and (S2) recovers an injected transfer share to within 0.05. | inconclusive | not-for-production | C1 is -0.322 points of capacity (-0.437 to -0.207), close to the 0.35-point threshold, but the S1 and S2 gates failed for reasons of test design, so the verdict is inconclusive. | also relevant |
| 00051 pvdis-3b-midday-loss-of-the-solar-term | At June-to-August midday on day+2 and day+3, the solar term of experiment 00046 scores worse than the best guess d0 (C6 +0.477 and +0.172 points of capacity) mainly because the fixed coefficient of 1 is too large for the sunniest rows, which a PV inverter clips, and not because the ENS members add noise. |
partly supported | not-for-production | At June-to-August midday the oracle shift's coefficient of 1 is too large in effect: the residual of the d1l median has a slope of -0.42 to -0.69 on it in every month and hour bin, and a fitted coefficient lowers the median's error by 0.991 points (-1.473 to -0.495). |
primary |
| 00046 embedded-solar-share-of-primary-forecast-error | At the five Primary substations metered in MW that have a clean 2023/24 capacity estimate, an explicit solar term lowers the error of the demand forecast that experiment 00033's best-guess recipe produces without its shortwave and longwave radiation inputs (arm d1l) by at least 0.35 points of capacity on daylight April-to-September rows. The solar term shifts every forecast by minus the capacity of experiment 00040 times the calibrated ENS forecast of the solar anomaly (a fitted slope on the ensemble mean, plus each member's own deviation from the mean), with the coefficient fixed at 1. The contrast is against d1l and not against the best guess. |
refuted | not-for-production | The ENS solar term lowers the quantile score of d1l by 0.056 points (97.5% interval -0.191 to +0.064), against a threshold of 0.35, so the verdict is refuted. |
primary |
| 00045 pvdis-4-weekly-capacity-asymmetric-l1 | On the 8 Primary substations (substations that feed 11 kV circuits from the 33 kV network) metered in MW, a least-squares fit of the daylight power to a calendar-and-temperature demand term minus a weekly capacity times one PV shape from the Copernicus Atmosphere Monitoring Service (CAMS), with weekly capacity increments under an asymmetric L1 penalty (a penalty on the sum of absolute increments, light on rises and heavy on falls), meets six conditions. It recovers a known injected capacity path (ramps, steps, and temporary falls, injected into a semi-synthetic baseline) with a median error of at most 0.15 of the path's mean capacity. It beats a capacity that is constant within each season-year by at least 20% on ramps and steps. It produces at most 0.5 false steps of 10% of capacity per Primary-year at the median Primary, and at most 1.0 at the worst Primary, on the placebo sky and on a constant injected path. Its path drifts upward on noise by at most 0.05 of capacity over 5 years. It recovers a 30% fall lasting 26 weeks at 0.5 or more of its depth, and an 8-week fall at under 0.5. Its 90% bootstrap interval covers 0.85 to 0.95 of non-step weeks and at least 0.60 of step weeks. Capacity is the 99th percentile of the half-hourly AC output, in MW, of the embedded solar (solar connected inside the distribution network, mostly unmetered by NGED) behind a Primary. | refuted | not-for-production | No weekly capacity fit met the limits fixed in advance for tuning: the best penalty pair recovered none of a 26-week fall of 30% and gave 0.66 false steps per Primary-year at the median Primary, against a limit of 0.5. Weekly changes in embedded solar capacity cannot yet be read from Primary-substation data at the accuracy a weekly series needs, so the next experiment should use one capacity per season-year and read it as a range. | primary |
| 00040 pv-capacity-error-bars | On the 8 directional (MW) Primary substations, comparing the PV capacity that six least-squares arms fit to the same series (they differ in how much cloud-driven demand each allows) detects a cloud-driven demand load that pushes the half-hourly fit's capacity at least 25% off, with at least 80% power and a false-positive rate of at most 10% on pure injected PV, so that a bounded error bar can be attached to the capacity at every Primary where no such load is detected. | partly supported | not-for-production | The misfit test is calibrated (median capacity over truth 0.992, 95% interval 0.951 to 1.032; false-positive rate 0.055) and detects four of the five confound shapes that no fit contains at 25% of capacity, but it detects a day-long cloud load in only 0.35 of draws, so the pre-registered power rule fails and the verdict is underpowered. |
primary |
| 00039 pv-disaggregation-cams-injection | On the 8 directional (MW) Primary substations, a calendar-plus-temperature demand term minus one PV shape driven by Copernicus Atmosphere Monitoring Service (CAMS) irradiance recovers the P99 capacity of real solar-farm output injected into the real demand series to within 25%, and the same fits on the un-injected series pass four checks that a weather-correlated demand confound would fail. | partly supported | not-for-production | A fit of calendar-plus-temperature demand minus one satellite-driven solar shape recovers 0.854 (daily fit, 95% interval 0.815 to 0.866) of the capacity of real solar-farm output added to the demand of the 8 directional Primaries. On the real series 6 of the 8 pass four checks that a demand confound would fail, and one fails the placebo-sky check, so the verdict is partly supported. | primary |