New Method Improves Predictions of Distributed Energy Resource Adoption Across Power Grid Levels
Researchers have proposed a probabilistic forecasting framework that predicts distributed energy resource (DER) adoption—such as rooftop solar installations—while maintaining statistical guarantees at multiple levels of the electric grid hierarchy. The method combines a multivariate Hawkes process to model adoption dynamics with a tailored split conformal prediction algorithm and a novel nonconformity score. Accurate, uncertainty-aware DER forecasting is increasingly important as utilities need to plan infrastructure proactively for a rapidly growing and spatially uneven wave of customer-level renewable installations.
As distributed energy resources like rooftop solar panels proliferate, electric utilities face growing difficulty predicting where and when new installations will occur, complicating grid infrastructure planning. Traditional forecasting methods struggle with the inherent uncertainty and spatial heterogeneity of DER growth, and they typically lack formal statistical guarantees. The proposed framework addresses this by modeling adoption dynamics with a multivariate Hawkes process—a tool suited to capturing self-exciting, clustered event patterns—and wrapping predictions in a split conformal prediction procedure. A key technical contribution is a new nonconformity score designed so that statistical validity is preserved when predictions are aggregated from the circuit level up to the substation level, matching the hierarchical structure of real distribution grids. The authors establish theoretical validity under mild assumptions and validate the approach empirically on customer-level solar panel installation data from Indianapolis, Indiana, where it outperforms existing baselines on both predictive accuracy and uncertainty calibration. The work is a preprint on arXiv and has undergone multiple revisions since its initial submission in November 2024, with the most recent version posted in June 2026.
What's missing
The paper is a preprint and has not yet undergone formal peer review, so its claims of outperforming baselines have not been independently validated. Key open questions include how well the framework generalizes beyond the Indianapolis dataset to grids with different urban densities, regulatory environments, or DER incentive structures, and whether computational scalability holds for very large distribution networks. The paper does not appear to address non-solar DERs (e.g., battery storage, EVs) in its empirical evaluation, leaving the scope of generalization to other DER types unclear.
What different sources said
- arXiv cs.LGCenter
Hierarchical Probabilistic Conformal Prediction for Distributed Energy Resources Adoption
Related
Gut Bacteria Enzyme Found to Break Down Heat-Processed Food Compounds, Producing Novel Biogenic Amines
Researchers have discovered that an enzyme in common gut bacteria can degrade N-epsilon-carboxymethyllysine (CML), a compound formed during thermal food processing, producing previously unknown biogenic amines. The enzyme, ornithine decarboxylase SpeC from enterobacteria, acts on CML and related modified lysine derivatives through a low-level 'underground' catalytic activity. This finding suggests a previously unrecognized communication axis between thermally processed dietary compounds and gut microbial physiology, with potential implications for host health.
Full-Length Gene Sequencing Reveals Two Distinct Bacterial Communities in Black-Legged Ticks Expanding Into Canada
Researchers used Oxford Nanopore full-length 16S rRNA gene sequencing to characterize the microbiome of Ixodes scapularis black-legged ticks collected in Nova Scotia, Canada, distinguishing between tick-adapted bacteria and environmentally acquired bacteria. The study comes as I. scapularis — the primary vector of Lyme disease — is rapidly expanding northward into Canada due to climate change. The findings suggest that environmentally derived bacteria in tick microbiomes are not mere contamination, which has implications for how tick microbiome data is collected and interpreted across surveillance studies.
Study Identifies Metabolic Link Between Cell Envelope Stress and Biofilm Formation in Bacteria
Researchers have discovered that the metabolite acetyl-CoA directly inhibits enzymes that degrade the bacterial signaling molecule c-di-GMP, connecting cell envelope biosynthesis stress to biofilm formation in Pseudomonas aeruginosa. The study found that sub-inhibitory concentrations of antibiotics targeting early peptidoglycan biosynthesis — but not other antibiotic classes — elevate c-di-GMP levels by reducing phosphodiesterase activity, with acetyl-CoA competing for the enzyme active site. Because the relevant enzyme domain is broadly conserved across bacterial species, this checkpoint mechanism may be widespread and could have implications for understanding antibiotic-induced biofilm responses.