Next step would probably to get a few of these, see if you can record the hum on a few of them, and then start experimenting.
The theory behind triangulation is simple. But in practice, it's probably really hard to make sure all microphones have the same sensitivity. The standard approach depends on sounds being quieter when they are far away, but if one mic is naturally quieter (deeper inside the house, or more absorbant material, or just more obstacles in the way) that no longer works.
The best approach would be using a phase difference to measure. Exploiting the delay due to the speed of sound. That means you need some way to synchronize the recordings. Presuming a 20hz signal, you'd need your synchronization to be better than 0.05 seconds. This also requires the hum waveform to be easy to match. That is, you'd need to be able to tell the phase offset by matching parts of the sound.
You could also try a 'big data' approach, just cataloging all reported cases and looking for patterns. That requires dealing with very noisy data though, as reported cases will be inconsistent. This is due to people not always being home, not everyone hearing it, and no verification of reports.
edit: Interesing idea: use the 'bit data' approach together with wind speed and direction. The sound should be easier to hear when the wind blows it towards you. With enough data and corresponding wind data, you might have something. Does require compensating for correlation between time of day and the wind. If wind is west in the night, and less people hear things when the wind is west, that might just be because they are sleeping.