  Buildings across the world contribute significantly to the overall energy
consumption and are thus stakeholders in grid operations. Towards the
development of a smart grid, utilities and governments across the world are
encouraging smart meter deployments. High resolution (often at every 15
minutes) data from these smart meters can be used to understand and optimize
energy consumptions in buildings. In addition to smart meters, buildings are
also increasingly managed with Building Management Systems (BMS) which control
different sub-systems such as lighting and heating, ventilation, and air
conditioning (HVAC). With the advent of these smart meters, increased usage of
BMS and easy availability and widespread installation of ambient sensors, there
is a deluge of building energy data. This data has been leveraged for a variety
of applications such as demand response, appliance fault detection and
optimizing HVAC schedules. Beyond the traditional use of such data sets, they
can be put to effective use towards making buildings smarter and hence driving
every possible bit of energy efficiency. Effective use of this data entails
several critical areas from sensing to decision making and participatory
involvement of occupants. Picking from wide literature in building energy
efficiency, we identify five crust areas (also referred to as 5 Is) for
realizing data driven energy efficiency in buildings : i) instrument optimally;
ii) interconnect sub-systems; iii) inferred decision making; iv) involve
occupants and v) intelligent operations. We classify prior work as per these 5
Is and dis-cuss challenges, opportunities and applications across them.
Building upon these 5 Is we discuss a well studied problem in building energy
efficiency -non-intrusive load monitoring (NILM) and how research in this area
spans across the 5 Is.
