> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nectarclimate.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Anomalies

> Automatically surface unusual usage, cost, unit-price, late-fee, and demand-charge patterns across your utility portfolio.

<Tip>For general data quality help, see [Data Quality FAQ](/platform/data-quality/faq).</Tip>

Nectar continuously watches your utility data for unusual patterns. When something looks off — a jump in consumption, an unexpected cost increase, a late fee, or a spike in demand charges — it surfaces as an **anomaly** for your team to review, right alongside your other data quality items.

<Info>
  Anomaly detection is off by default. Turn it on in [**Settings > Company > Data Quality > Anomaly detection**](https://dash.nectarclimate.com/settings/company/data-quality/anomaly-detection). Once enabled, Nectar checks your data automatically every day, and you can run a check on demand at any time.
</Info>

***

## What Nectar looks for

| Type                    | What it flags                                                                              |
| ----------------------- | ------------------------------------------------------------------------------------------ |
| **Usage anomaly**       | A site's consumption for a commodity is unusually high or unusually low for the conditions |
| **Cost anomaly**        | A site's monthly cost for a commodity is unusually high                                    |
| **Unit-price anomaly**  | The effective price you paid per unit (for example, \$/kWh) is unusually high              |
| **Interest charges**    | An account is paying interest, late, or penalty charges                                    |
| **High demand charges** | An account's electricity demand charges are unusually high                                 |

Each anomaly points you to the specific site or account, commodity, and time period involved, so you can investigate quickly.

***

## How detection works

Nectar compares each site or account against its own recent history and flags meaningful deviations. Detection is **weather-aware**: it accounts for how hot or cold the weather was during each period, so normal seasonal swings (more gas in winter, more electricity in summer) don't get flagged — only changes that genuinely stand out do.

<Note>
  Weather-aware detection uses historical temperature data from [Open-Meteo](https://open-meteo.com), licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Nectar processes this data into heating and cooling degree days for each site and period.
</Note>

Interest charges and demand charges work a little differently: any interest you're paying is worth surfacing, and demand-charge spikes are flagged against the account's typical pattern.

Nectar only flags a site or account once it has enough history to judge what "normal" looks like. New sites, or sites with sparse data, are left alone until there's enough to compare against — this keeps the inbox focused on real signals.

***

## Sensitivity

Each anomaly type has a **sensitivity** setting:

| Level      | Behavior                             |
| ---------- | ------------------------------------ |
| **Low**    | Flags only large, obvious deviations |
| **Medium** | Balanced — the default               |
| **High**   | Flags smaller deviations too         |

Higher sensitivity catches more, but may include some patterns that turn out to be normal. Lower sensitivity is quieter but may miss smaller changes. Adjust it per type in [**Settings > Company > Data Quality > Anomaly detection**](https://dash.nectarclimate.com/settings/company/data-quality/anomaly-detection).

***

## Where to find anomalies

Anomalies appear in your [**Data Quality inbox**](https://dash.nectarclimate.com/data-quality/issues) alongside other data quality items — there's no separate page to check. From the inbox you can:

1. **Filter** to anomalies (and by type, site, or commodity) to focus your review.
2. **Open** an anomaly to see a chart of the trend, the expected range, and the periods that were flagged.
3. **Resolve** it once you've addressed it, or **dismiss** it if it isn't a real problem.

Resolved and dismissed anomalies are kept for your records and won't be flagged again on future checks.

The [**Data Quality overview**](https://dash.nectarclimate.com/data-quality) also rolls up your open anomalies by type and shows their estimated total dollar impact, so you can see where the biggest opportunities are at a glance.

***

## Getting value out of anomalies

* **Catch overspend early.** Cost and unit-price anomalies highlight bills that cost more than expected — often a rate change, a billing error, or a usage problem worth chasing.
* **Stop paying avoidable fees.** Interest-charge anomalies surface late fees so you can fix the underlying payment or billing issue.
* **Manage demand.** High demand-charge anomalies point to facilities where reducing peak demand could lower bills.
* **Spot operational issues.** Usage anomalies (including unexpected *drops*) can reveal equipment left running, meters that stopped reporting, or changes in how a building is used.

***

## Improving accuracy

If an anomaly's chart shows gaps or unexpectedly low months, the underlying data may be incomplete — and incomplete history can make a normal period look unusual.

Records with open [data quality issues](/platform/data-quality/overview) (such as unmatched accounts) are left out of the analysis. If a site has many unresolved issues, resolve them in your [**Data Quality inbox**](https://dash.nectarclimate.com/data-quality/issues) — the next check will use the corrected data and produce more accurate results.

***

## Related pages

* [Data Quality overview](/platform/data-quality/overview) — issues, completeness, and anomalies
* [Data collection settings](/platform/settings/data-collection) — company-level data quality controls
* [Sites](/platform/sites/overview) — view per-site data

**See also:** [Glossary — Anomaly](/platform/glossary#anomaly)