  Recently, tremendous interest has been devoted to develop data fusion
strategies for energy efficiency in buildings, where various kinds of
information can be processed. However, applying the appropriate data fusion
strategy to design an efficient energy efficiency system is not
straightforward; it requires a priori knowledge of existing fusion strategies,
their applications and their properties. To this regard, seeking to provide the
energy research community with a better understanding of data fusion strategies
in building energy saving systems, their principles, advantages, and potential
applications, this paper proposes an extensive survey of existing data fusion
mechanisms deployed to reduce excessive consumption and promote sustainability.
We investigate their conceptualizations, advantages, challenges and drawbacks,
as well as performing a taxonomy of existing data fusion strategies and other
contributing factors. Following, a comprehensive comparison of the
state-of-the-art data fusion based energy efficiency frameworks is conducted
using various parameters, including data fusion level, data fusion techniques,
behavioral change influencer, behavioral change incentive, recorded data,
platform architecture, IoT technology and application scenario. Moreover, a
novel method for electrical appliance identification is proposed based on the
fusion of 2D local texture descriptors, where 1D power signals are transformed
into 2D space and treated as images. The empirical evaluation, conducted on
three real datasets, shows promising performance, in which up to 99.68%
accuracy and 99.52% F1 score have been attained. In addition, various open
research challenges and future orientations to improve data fusion based energy
efficiency ecosystems are explored.
