infrastructure
The Solar Forecast Keeps the Tree and Selects Its Helpers
A nonnegative combiner lets complementary neural predictors reduce residual error while assigning zero weight to models that would make the forecast worse.
Summary
A nonnegative combiner lets complementary neural predictors reduce residual error while assigning zero weight to models that would make the forecast worse.
The study starts from a strong LightGBM photovoltaic forecaster and asks whether seven neural alternatives add complementary signal across 15- to 240-minute horizons. A constrained combiner keeps nonnegative weights summing to one, so unhelpful candidates can receive zero and the tree remains a fallback. On 56,078 windows from two co-located public PVDAQ systems, shared weights reduced scaled mean absolute error by 3.21 percent relative to the tree alone; a newer bounded correction did not improve further. The evaluation reuses an inspected period and does not establish independent replication or universal gains.
Why it matters
A nonnegative combiner lets complementary neural predictors reduce residual error while assigning zero weight to models that would make the forecast worse.
Limits and context
- On 56,078 windows from two co-located public PVDAQ systems, shared weights reduced scaled mean absolute error by 3.21 percent relative to the tree alone; a newer bounded correction did not improve further.
- The evaluation reuses an inspected period and does not establish independent replication or universal gains.
Key claims
A nonnegative combiner lets complementary neural predictors reduce residual error while assigning zero weight to models that would make the forecast worse.
Qualification: On 56,078 windows from two co-located public PVDAQ systems, shared weights reduced scaled mean absolute error by 3.21 percent relative to the tree alone; a newer bounded correction did not improve further.
Evidence: source-2026-10-24-008
Sources
- arXiv preprint 2610.31248, original v1arXiv · primary research
Corrections
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