Bayesian Deep Learning-Based Probabilistic Load Forecasting in Smart Grids

Yandong Yang

doi:10.1109/TII.2019.2942353

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Bayesian Deep Learning-Based Probabilistic Load Forecasting in Smart Grids

2020-07-01

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DOI：10.1109/TII.2019.2942353

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Abstract

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The extensive deployment of smart meters in millions of households provides a huge amount of individual electricity consumption data for demand side analysis at a fine granularity. Different from traditional aggregated system-level data, smart meter data is more irregular and unpredictable. As a result, probabilistic load forecasting (PLF), which can provide a better understanding of the uncertainty and volatility in future demand, is critical to constructing energy-efficient and reliable smart grids. In this article, a recently developed technique called Bayesian deep learning is employed to solve this challenging problem. In particular, a novel multitask PLF framework based on Bayesian deep learning is proposed to quantify the shared uncertainties across distinct customer groups while accounting for their differences. Further, a clustering-based pooling method is designed to increase the data diversity and volume for the framework. This not only addresses the problem of overfitting but also improves the predictive performance. Numerical results are presented which demonstrate that the proposed framework provides superior probabilistic forecasting accuracy over conventional methods.

Keywords:

Deep learning

Bayes methods

Probabilistic logic

Load forecasting

Smart meters

Uncertainty

Forecasting

Bayesian deep learning

clustering-based pooling

multitask learning (MTL)

probabilistic load forecasting (PLF)

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