1. A method of forecasting an electrical load of a site, the method comprising: receiving historical load data and historical ambient condition data, the historical load data and the historical ambient condition data including time series data pertaining to a site; generating additional data from one or more of the received historical load data and the received historical ambient condition data; building a best sub-model for each of multiple forecast intervals, the building including: clustering a training portion of the historical load data, further clustering a training portion of the additional data in parallel with the clustering of the training portion of the historical load data, training possible sub-models using the clustered training portions of the historical load data and the additional data, verifying forecasted loads output from the possible sub-models against verification portions of the historical load data and the additional data, and determining a first subset of parameters for the best sub-model based upon accuracy of the forecasted loads; and forecasting an electrical load of the site for each of the forecast intervals using an ensemble of the best sub-models.