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.