Distributed energy technologies, such as residential energy storage, embedded generation, and microgrids, are likely to play an increasing role in future energy systems. Getting the most value from these distributed assets is often dependent on the ability to optimize their operation in a distributed manner. This distributed optimization, in turn, calls for effective short-term forecasts of the output of small-scale generating assets, and the demand of small-scale aggregations of users. This paper introduces the integration of data-driven forecasting and operational optimization methods into a single model, avoiding the need to explicitly produce forecasts. The method is tested against two empirical energy storage operational optimization problems, the minimization of peak energy drawn by a small aggregation of customers, and the minimization of the energy costs of a collection of households which have rooftop PV systems. The integrated forecasting and operational optimization approach performs well at the peak demand minimization problem for intermediate-sized aggregations (50 residential customers or more), while an approach with separate forecasting and optimization performed better on the energy cost minimization problem. These results suggest that the integrated approach can be effective in applications where (i) forecasting difficulty is intermediate, and (ii) the exact operational optimization formulation can be well approximated by a data-driven model trained on a small fraction of the available forecast training data.