Parseval’s theorem: the total energy (or average power) of a signal computed in the time domain equals the total energy computed in the frequency domain. Energy is conserved across the Fourier representation. It’s the “rotation-preserves-length” statement applied to function-space orthogonal decompositions.

Fourier series form

For a periodic signal with Fourier coefficients :

Left side is the average power of over one period; right side is the sum of squared magnitudes of all harmonic coefficients. So power equals the sum of harmonic powers.

Fourier transform form

For a general (aperiodic) signal with Fourier transform :

Left side is the total signal energy ; right side is the energy spectral density integrated over all frequencies. Energy in time = energy in frequency.

Why both forms work

Both are instances of the same orthogonality principle: an orthonormal basis preserves the inner product. For Fourier series, the basis is ; the coefficients in this basis are , and the squared length of in time equals the sum of squared coefficient magnitudes. For the Fourier transform, the “basis” is a continuum of complex exponentials, and the sum becomes an integral.

Practical uses

Sanity checking. If you’ve computed Fourier coefficients (or a Fourier transform) and Parseval’s identity is way off, there’s an error. The two integrals must be the same number.

Estimating power. For a signal with most of its energy in a few dominant coefficients, you can estimate average power without integrating the full time-domain signal: sum over the few significant values.

Spectral occupancy. is the energy spectral density of an energy signal, the energy per unit frequency. Integrate it over a band to get the energy in that band, a basic quantity in communications and audio engineering.

A quick example

For , the Fourier coefficients (period ) are , all others zero. Parseval’s identity gives

Compare to from the direct integral (worked out in Signal power). ✓

Generalization

The general statement, sometimes called the Plancherel theorem or Rayleigh’s energy theorem, says the inner product of two signals equals the inner product of their transforms:

Parseval’s theorem is the special case . The general form shows up when correlating two signals, common in communication receiver design and statistical signal processing.