Conditional Approximate Normalizing Flows for Joint Multi-Step Probabilistic Electricity Demand Forecasting

• URL: http://arxiv.org/abs/2201.02753v1
• Date: Sat, 8 Jan 2022 03:42:12 GMT
• Title: Conditional Approximate Normalizing Flows for Joint Multi-Step Probabilistic Electricity Demand Forecasting
• Authors: Arec Jamgochian, Di Wu, Kunal Menda, Soyeon Jung, Mykel J. Kochenderfer
• Abstract summary: We introduce the conditional approximate normalizing flow (CANF) to make probabilistic multi-step time-series forecasts when correlations are present over long time horizons. Empirical results show that conditional approximate normalizing flows outperform other methods in terms of multi-step forecast accuracy and lead to up to 10x better scheduling decisions.
• Score: 32.907448044102864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Some real-world decision-making problems require making probabilistic forecasts over multiple steps at once. However, methods for probabilistic forecasting may fail to capture correlations in the underlying time-series that exist over long time horizons as errors accumulate. One such application is with resource scheduling under uncertainty in a grid environment, which requires forecasting electricity demand that is inherently noisy, but often cyclic. In this paper, we introduce the conditional approximate normalizing flow (CANF) to make probabilistic multi-step time-series forecasts when correlations are present over long time horizons. We first demonstrate our method's efficacy on estimating the density of a toy distribution, finding that CANF improves the KL divergence by one-third compared to that of a Gaussian mixture model while still being amenable to explicit conditioning. We then use a publicly available household electricity consumption dataset to showcase the effectiveness of CANF on joint probabilistic multi-step forecasting. Empirical results show that conditional approximate normalizing flows outperform other methods in terms of multi-step forecast accuracy and lead to up to 10x better scheduling decisions. Our implementation is available at https://github.com/sisl/JointDemandForecasting.

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