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Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression
[article]
2019
arXiv
pre-print
We propose a new forecasting method for predicting load demand and generation scheduling. Accurate week-long forecasting of load demand and optimal power generation is critical for efficient operation of power grid systems. In this work, we use a synthetic data set describing a power grid with 700 buses and 134 generators over a 365-days period with data synthetically generated at an hourly rate. The proposed approach for week-long forecasting is based on the Gaussian process regression (GPR)
arXiv:1910.03783v1
fatcat:rstn3uo42bhvrasnw4e6qsqcle