Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows
arXiv:2204.13939 · doi:10.1109/TSG.2023.3254890
Abstract
The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control. However, high fluctuations and increasing electrification cause huge forecast variability, not reflected in traditional point estimates. Probabilistic load forecasts take future uncertainties into account and thus allow more informed decision-making for the planning and operation of low-carbon energy systems. We propose an approach for flexible conditional density forecasting of short-term load based on Bernstein polynomial normalizing flows, where a neural network controls the parameters of the flow. In an empirical study with 363 smart meter customers, our density predictions compare favorably against Gaussian and Gaussian mixture densities. Also, they outperform a non-parametric approach based on the pinball loss for 24h-ahead load forecasting for two different neural network architectures.
References in corpus (9)
- Adam: A Method for Stochastic Optimization
- WaveNet: A Generative Model for Raw Audio
- Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
- Parallel WaveNet: Fast High-Fidelity Speech Synthesis
- Most Likely Transformations
- Short Term Load Forecasts of Low Voltage Demand and the Effects of Weather
- Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
- Conditional Density Estimation with Bayesian Normalising Flows
- Data-Driven Load Profiles and the Dynamics of Residential Electric PowerConsumption