  With the development of modern information technology (IT), a smart grid has
become one of the major components of smart cities. To take full advantage of
the smart grid, the capability of intelligent scheduling and planning of
electricity delivery is essential. In practice, many factors have an impact on
electricity consumption, which necessitates information fusion technologies for
a thorough understanding. For this purpose, researchers have investigated
methodologies for collecting electricity consumption related information and
variant multi-factor power consumption forecasting models. In addition,
conducting a comprehensive analysis and obtaining an accurate evaluation of
power consumption are the premise and basis for a more robust and efficient
power grid design and transformation. Therefore, it is meaningful to explore
forecasting models that are able to reflect the power consumption changes and
internal relations within fusional information effectively. Making electricity
consumption forecasting based on the neural network has been a popular research
topic in recent years, and the back-propagation neural network (BPNN) algorithm
has been recognized as a mature and effective method. In this paper, BPNN is
adopted to forecast the electricity consumption using Pecan Street, a community
with a relatively large-scale smart grid, as a case study, and takes multiple
factors into account, such as weather condition, weekend and holidays. The
influences of each factor have been evaluated for a deeper insight. We hope
this work will inspire more discussion and further study to guide the design of
future smart grids.
