  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) method, with prior covariance matrices of the
quantities of interest (QoI) computed from ensembles formed by up to twenty
preceding weeks of QoI observations. Then, we use these covariances within the
GPR framework to forecast the QoIs for the following week. We demonstrate that
the the proposed ensemble GPR (EGPR) method is capable of accurately
forecasting weekly total load demand and power generation profiles. The EGPR
method is shown to outperform traditional forecasting methods including the
standard GPR and autoregressive integrated moving average (ARIMA) methods.
