A common problem is to forecast the aggregate of several time periods of data, using a model fitted to the disaggregated data. For example, you may have monthly data but wish to forecast the total for the next year. Or you may have weekly data, and want to forecast the total for the next four weeks. If the point forecasts are means, then adding them up will give a good estimate of the total. But prediction intervals are more tricky due to the correlations between forecast errors.
Electricity is produced by a variety of generating units, each with different lead times and costs to be readied for service, and production costs once brought online. Because electricity is a commodity that cannot be easily stored, generation should match consumption at any given time; therefore, the cost of generating electricity has a direct relationship to electricity demand, typically referred to as electricity load. An accurate load forecast enables generators to optimize the mix of generating units that can serve the expected load while minimizing the production costs. This holds true for generators in both regulated and deregulated markets. In several deregulated markets, the electricity market operator is in charge of dispatching the available generation units according to the market's expected load and individual units' offered generation costs.
Leading German economic think tanks have trimmed their 2016 growth forecast for the country's economy to 1.6 percent, pointing to slower expansion in China and elsewhere. Thursday's forecast by four economic institutes was down from the 1.8 percent they predicted in October. For 2017, they are forecasting growth of 1.5 percent. The German economy, Europe's biggest, expanded by 1.7 percent last year. It is traditionally export-heavy but lately has been fueled increasingly by domestic demand.