Estimating Energy Forecasting Uncertainty for Reliable AI Autonomous Smart Grid Design

Maher Selim, Ryan Zhou, Wenying Feng and Peter Quinsey
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Maher Selim: Department of Computer Science, Trent University, Peterborough, ON K9L 0G2, Canada
Ryan Zhou: Department of Computer Science, Trent University, Peterborough, ON K9L 0G2, Canada
Wenying Feng: Department of Computer Science, Trent University, Peterborough, ON K9L 0G2, Canada
Peter Quinsey: Lowfoot Inc., Toronto, ON M5A 2B7, Canada

Energies, 2021, vol. 14, issue 1, 1-15

Abstract: Building safe, reliable, fully automated energy smart grid systems requires a trustworthy electric load forecasting system. Recent work has shown the efficacy of Long Short-Term Memory neural networks in energy load forecasting. However, such predictions do not come with an estimate of uncertainty, which can be dangerous when critical decisions are being made autonomously in energy production and distribution. In this paper, we present methods for evaluating the uncertainty in short-term electrical load predictions for both deep learning and gradient tree boosting. We train Bayesian deep learning and gradient boosting models with real electric load data and show that an uncertainty estimate may be obtained alongside the prediction itself with minimal loss of accuracy. We find that the uncertainty estimates obtained are robust to changes in the input features. This result is an important step in building reliable autonomous smart grids.

Keywords: autonomous smart grid design; electric load forecasting; deep learning; gradient tree boosting; long short-term memory; Monte Carlo dropout; neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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