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their R & D and design capacities by virtue of Crowdsourcing. In the big data
era, Spatial Crowdsourcing becomes a hot topic. The operation framework of
Spatial Crowdsourcing is shown as follows. A user may request the service and
resources related to a specified location. Then the mobile users who are willing
to participate in the task will move to the specified location to acquire related
data (such as video, audio, or pictures). Finally, the acquired data will be send to
the service requester. With the rapid growth of usage of mobile devices and the
increasingly complex functions provided by mobile devices, it can be forecasted
that Spatial Crowdsourcing will be more prevailing than traditional Crowdsourcing,
e.g., Amazon Turk and Crowdflower.
6.3.6
Smart Grid
Smart Grid is the next generation power grid constituted by traditional energy
networks integrated with computation, communications and control for optimized
generation, supply, and consumption of electric energy. Smart Grid related big data
are generated from various sources, such as (a) power utilization habits of users,
(b) phasor measurement data, which are measured by phasor measurement unit
(PMU) deployed national-wide, (c) energy consumption data measured by the smart
meters in the Advanced Metering Infrastructure (AMI), (d) energy market pricing
and bidding data, (e) management, control and maintenance data for the devices and
equipment in the power generation, transmission and distribution networks (such as
Circuit Breaker Monitors and transformers). Smart Grid brings about the following
challenges on exploiting big data.
Grid Planning
: By analyzing data in Smart Grid, the regions can be identified
that have excessive high electrical load or power outage frequencies. Even the
transmission lines with high failure possibility can be predicted. Such analytical
results may contribute to grid upgrading, transformation, and maintenance, etc.
For example, researchers from University of California, Los Angeles designed
an “electric map” according to the big data theory and made a California map
by integrating census information and real-time power utilization information
provided by electric power companies. The map takes a block as a unit to
demonstrate the power consumption of every block at the moment. It can even
compare the power consumption of the block with the average income per capita
and building types, so as to obtain more accurate power usage habits of all
kinds of groups in the community. This map provides effective and visual load
forecast for city and power grid planning. Preferential transformation on power
grid facilities in blocks with high power outage frequencies and serious overloads
may be conducted, as displayed in the map.
Interaction Between Power Generation and Power Consumption
: An ideal
power grid shall balance power generation and power consumption. However,
the traditional power grid is constructed based on one-directional approach of
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