Enhanced spatio-temporal electric load forecasts using less data with active deep learning

TL;DR

This paper demonstrates that active learning can significantly reduce data requirements for accurate spatio-temporal electric load forecasting, aiding renewable energy integration and grid decarbonization.

Contribution

It introduces an active learning approach for electric load prediction, reducing data needs by about half compared to passive methods.

Findings

Active learning improves prediction accuracy with less data.

Utilities can optimize smart meter deployment using active learning.

Reduced data collection costs without sacrificing forecast quality.

Abstract

An effective way to oppose global warming and mitigate climate change is to electrify our energy sectors and supply their electric power from renewable wind and solar. Spatio-temporal predictions of electric load become increasingly important for planning this transition, while deep learning prediction models provide increasingly accurate predictions for it. The data used for training deep learning models, however, is usually collected at random using a passive learning approach. This naturally results in a large demand for data and associated costs for sensors like smart meters, posing a large barrier for electric utilities in decarbonizing their grids. Here, we test active learning where we leverage additional computation for collecting a more informative subset of data. We show how electric utilities can apply active learning to better distribute smart meters and collect their data for more accurate predictions of load with about half the data compared to when applying passive learning.