Shading loss factors for fourteen scenarios
What a chimney, a neighbour’s roof or a line of trees actually costs you, modelled with the same shade factor method MCS sanctions. The annual figures are milder than people expect; the winter ones are far worse.
Fourteen shading scenarios, modelled the way MCS says to model them.
Annual, summer and winter loss against an identical unshaded array — and the reason to publish all three is that the annual figure alone makes shade look survivable when the winter figure often does not.
Download
UK solar shading loss factors
14 scenarios — shade factor, annual loss, the range across four UK locations, summer and winter loss, and the resulting shaded yield per kWp.
Download the CSV · 3 kB · CC BY 4.0
Credit as: solar.org.uk, modelled from PVGIS 5.3 (European Commission, Joint Research Centre). Contains modified Joint Research Centre data.
We can license this because we generated it. It is our own output following the method published in MIS 3002 — not a copy of MCS’s own lookup tables, which are published under terms that do not permit republication.
What it shows
The number the annual figure hides
Take the worst scenario in the file. It costs about a third of annual generation — bad, and the sort of figure someone might weigh against a discount.
Now look at the winter column: over 75% of winter generation gone. Winter is when electricity is dearest, when you are home in the dark, and when a battery has least to work with. An annual average spreads that across the summer months where the same obstruction does almost nothing, and produces a number that reads as tolerable.
That is why the dataset carries three columns rather than one.
Columns
| Column | What it is |
|---|---|
scenario_key | Short identifier, stable across releases |
scenario_label | What the obstruction is, its height in degrees and its width |
shade_factor | Shaded annual generation ÷ unshaded, per the MIS 3002 method. 1.00 is unshaded |
annual_loss_percent | Annual generation lost, at the headline location |
annual_loss_min_percent, annual_loss_max_percent | The range across the four locations we model — how much the scenario varies with latitude, not model uncertainty |
summer_loss_percent | April to September |
winter_loss_percent | November to February |
shaded_yield_kwh_kwp_y | What is left, per kWp per year |
How it was made
scripts/data/fetch-shading.py runs PVGIS 5.3 twice for each scenario at each location —
once with the horizon profile describing the obstruction, once clear — at 35 degrees, due
south, 20% system losses, building-mounted. The shade factor is the ratio, which is the
definition MIS 3002 gives.
The 20% loss figure matches the 0.8 factor MCS applies in MIS 3002, so these figures sit on the same footing as the 625-value dataset our calculators run on.
Sources
- PVGIS 5.3 Our own runs. Contains modified Joint Research Centre data.
- MIS 3002: The Solar PV Standard, issue 6.0 The shade factor method. MCS restricts reproduction of its own lookup tables, so we model our own figures using the method rather than republishing theirs.
Sorry to hear that. What was the problem?