  Smart grids technologies are enablers of new business models for domestic
consumers with local flexibility (generation, loads, storage) and where access
to data is a key requirement in the value stream. However, legislation on
personal data privacy and protection imposes the need to develop local models
for flexibility modeling and forecasting and exchange models instead of
personal data. This paper describes the functional architecture of an home
energy management system (HEMS) and its optimization functions. A set of
data-driven models, embedded in the HEMS, are discussed for improving renewable
energy forecasting skill and modeling multi-period flexibility of distributed
energy resources.
