Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids

no code implementations • 9 Oct 2020 • Tong Ma, David Alonso Barajas-Solano, Ramakrishna Tipireddy, Alexandre M. Tartakovsky

For observed states, we show that PhI-GPR provides a forecast comparable to the standard data-driven GPR, with both forecasts being significantly more accurate than the autoregressive integrated moving average (ARIMA) forecast.

GPR regression

Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems

no code implementations • 8 Oct 2020 • Daniel Dylewsky, David Barajas-Solano, Tong Ma, Alexandre M. Tartakovsky, J. Nathan Kutz

Time series forecasting remains a central challenge problem in almost all scientific disciplines.

GPR Load Forecasting +2

Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression

no code implementations • 9 Oct 2019 • Tong Ma, Renke Huang, David Barajas-Solano, Ramakrishna Tipireddy, Alexandre M. Tartakovsky

We propose a new forecasting method for predicting load demand and generation scheduling.

GPR regression +1