How would you compare the models you 
used to older ones? 
Our models were more accurate than previous 
models, and importantly, more actionable. We 
used a variety of metrics to measure model 
performance such as the mean absolute per-
centage error (MAPE) and root mean squared 
error (RMSE). Our models typically achieved 
MAPEs of less than 1.5 percent. 

How did you build up your models? 
Each neural network model was made up 
of several layers with each layer containing 

batch normalization, dropout, and activation. 
We created cross validation and out-of-
sample testing sets using time-based splits 
to prevent overfitting models. We added 
features based primarily on domain expertise.
an-ai-power-play-fueling-the-next-wave-of-innovation-in-the-energy-sector-may-2022