About the site

Electricity costs should be easier to understand.

Know Your Power Bill is an independent electricity-data and calculator project built to turn rates, watts, kilowatt-hours, utility data, and vehicle efficiency into practical dollar estimates.

Updated October 8, 2026 Primary data source U.S. EIA Methodology Editorial standards Report a correction

Useful before complicated

Every calculator is meant to answer a real household question quickly, while keeping the formula and assumptions visible enough to audit.

Public data, clearly labeled

State and utility electricity data come from public EIA datasets. We distinguish monthly state averages, annual provider averages, and customer-specific tariffs rather than presenting them as interchangeable.

Independent

Know Your Power Bill is not a utility company and is not sponsored by or affiliated with EIA or any utility listed on the site unless a page explicitly says otherwise.

Why this site exists

Most electricity questions are simple in principle but awkward in practice.

People regularly want to know whether a space heater is expensive to run, what an EV costs to charge at home, why their state has a high electricity price, or whether a utility average is comparable with the rate shown on a bill. The arithmetic is often straightforward, but the units and billing structures are easy to mix up.

Know Your Power Bill was built to bridge that gap. The calculators turn watts, hours, miles, efficiency, and cents per kilowatt-hour into understandable dollar estimates. The guides explain the concepts behind those calculations. The state and utility pages provide public-data context without pretending that an average is a personal quote.

How the data work

Different questions use different EIA datasets.

State electricity-price pages use EIA monthly residential electricity data so they can show the latest available state average, a 12-month trend, national comparison, and year-over-year change. Utility/provider pages use finalized annual Form EIA-861 detailed data because that dataset contains utility IDs, residential customers, sales, revenue, service type, ownership, and service-territory information.

We keep those periods separate on purpose. A 2024 utility annual average should not be compared as though it were the same thing as a July 2026 statewide monthly average. Provider pages therefore label the source year and explain that the figure is historical rather than a current tariff.

How calculations are built

Prefer transparent formulas over black-box scores.

For a straightforward appliance, the core relationship is watts ÷ 1,000 × hours × electricity price. EV charging adds vehicle efficiency and charging losses. EV-versus-gas adds gasoline price and miles per gallon. When real-world behavior complicates the result—such as thermostat cycling, inverter compressors, moisture sensors, or time-of-use rates—the page explains the limitation instead of hiding it behind false precision.

Whenever possible, users can replace a state average with the electricity price from their own bill. That is usually the better input for a personal estimate.

What gets published

Availability of data does not automatically make a page worth indexing.

The underlying utility dataset contains far more provider records than we expose as public search pages. We maintain a deliberately smaller reviewed utility cohort for indexing and advertising, while the broader reference layer remains accessible but noindex and ad-free. This lets us preserve useful source data without asking search engines or ad reviewers to treat every generated record as editorial content of equal value.

Our Editorial Standards page explains this publication approach, data-quality labels, corrections, and automated validation in more detail.

Corrections and contact

If something looks wrong, tell us exactly where.

Data pipelines and formulas are validated automatically, but mistakes can still happen in source interpretation, copy, accessibility, or presentation. Send the page URL and the issue to [email protected]. We review correction reports against the underlying data or calculation logic.