Time Series Prediction

Predicting Large Scale Power Grid Load

This work was done for PJM while working at PointServe.  PJM is “a regional transmission organization (RTO) that coordinates the movement of wholesale electricity in all or parts of 13 states and the District of Columbia“. 

PJM contracted with us  to help them predict, near-term, the overall load on their systems; i.e. how much electricity was going to be used by people in the various states they supported.  This was meant to help with a wasteful resources and time cost problem they were trying to solve, where they would find themselves either producing too much or too little power for the needs of their customers.  Since it takes time as well as resources to alter production, being able to anticipate load made it possible for them to save millions of dollars by informing strategic raising and lowering of electricity production to better match need.  

I employed a number of techniques to try to solve the problem, taking into consideration historical and current load data alongside historical and current weather data, including:

  • Recurrent back propagation networks
  • Probabilistic estimation and averaging algorithms

The reseach was successful in that I was able to predict within 2.5% accuracty 4 hours out into the future.

The findings were pretty proprietary, so the image below from the PJM website is indicative of the sort of data we were working with.