The Cambridge Bitcoin Electricity Consumption Index (CBECI) provides an up-to-date estimate of the Bitcoin network’s daily electricity load. The underlying techno-economic model is based on a bottom-up approach initially developed by Marc Bevand in 2017 that uses the profitability threshold of different types of mining equipment as the starting point.
Given that the exact electricity consumption cannot be determined, the CBECI provides a hypothetical range consisting of a hypothetical lower bound (floor) and a hypothetical upper bound (ceiling) estimate. Within the boundaries of this range, a best-guess estimate is calculated to provide a more realistic figure that approximates Bitcoin’s real electricity consumption.
The lower bound estimate corresponds to the theoretical minimum total electricity expenditure based on the best-case assumption that all miners always use the most energy-efficient equipment available on the market. The upper bound estimate specifies the theoretical maximum total electricity expenditure based on the worst-case assumption that all miners always use the least energy-efficient hardware available on the market, as long as running the equipment is still profitable in electricity terms. The best-guess estimate is based on the more realistic assumption that miners use a basket of profitable hardware rather than a single model.

The CBECI landing page displays two numbers for each type of estimate.
The first number refers to the total  electrical power  consumed by the Bitcoin network and is expressed in gigawatts (GW). This figure is updated once a day and corresponds to the rate at which Bitcoin currently uses electricity. In other words, it describes the current electricity demand of Bitcoin miners (electricity load).
The second number refers to the total yearly electricity consumption of the Bitcoin network and is expressed in terawatt-hours (TWh). It is an annualised measure that assumes continuous power usage at the aforementioned rate over the period of one year. We apply a 7-day moving average to the resulting data point in order to make the output value less dependent of short-term hashrate fluctuations, and thus more suitable for comparisons with alternative uses of electricity.