Estimating Energy Forecasting Uncertainty for Reliable AI Autonomous Smart Grid Design

Maher Selim,Ryan Zhou,Peter Quinsey,Wenying Feng

doi:10.3390/en14010247

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.

Highlights

Confident predictions are necessary to build reliable, fully Artificial Intelligence (AI)automated smart energy grid systems [1,2,3,4,5,6]
The Mean Absolute Error (MAE) loss function and the Adam version of stochastic gradient descent were used with Keras and Tensorflow to optimize the long short-term memory (LSTM) models
The accuracy of the load forecast is measured by way of two error metrics: the Root Mean Square Error (RMSE) in MWh units and the Mean Absolute Percent Error (MAPE)

Summary

Introduction

Confident predictions are necessary to build reliable, fully Artificial Intelligence (AI)automated smart energy grid systems [1,2,3,4,5,6]. Load predictions produced without any information about their uncertainty are unsafe [7] when critical autonomous decisions are being made in energy production and distribution. Current forecasting models normally do not provide information about uncertainty in their predictions, which can lead to costly and risky decisions and affect current efforts to produce a reliable smart energy grid system [8]. This is a problem for many AI applications, such as autonomous vehicles, where the lack of consideration for uncertainty can be dangerous [9]. For safe, reliable, autonomous artificial intelligence systems and for safe prediction-based decisions, evaluating the degree of uncertainty is paramount [10].

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Results

Conclusion