March 29, 2016

Role of Big Data in harvesting Utility Intelligence


Technology shifts periodically occur that change the rules of the game. Machine 2 Machine ( M2M) & Big Data Analytics are two fundamental forces which are profoundly disrupting business models globally. M2M + Big data analytics offer fantastic opportunities by harvesting  behavioural patterns which were previously not seen or answering powerful unanswered questions.

The Utility sector is ripe for unlocking energy efficiencies byreducing technical and commercial losses along the complete grid value chain. It also offers to understand energy consumption patterns at a level of granularity which was previously not possible

Business Challenge-1 : Neighborhood Outages


When a neighborhood experiences outages there are multiple dimensions to the pain experienced. If the outage is experienced in a neighborhood with a lot of industrial/corporate customers then the frequency and duration of outages has a direct economic impact as it affects industrial productivity and costs. These outages are again classified into various types - black outs, brownouts and transient outages.  If the outage is experiences in a neighborhood with heavy concentration of residential customers, it impacts the customer satisfaction index. Also today the time taken to respond to an outage is long since the latency between outage and the utility knowing about it is long. So the utility really wanted to dig deep into outages, minimize turn around time ( TAT) for outages and minimize its occurrence and duration

Business Challenge-2 : Last mile energy blind spots


The last mile in the power transmission value chain is a blind spot for most utility companies. In many neighborhoods there have been instances of various kinds of tamper on the distribution side leading to loss of revenue for the utility company. The utility company wanted to identify revenue leakage hot-spots and minimize last mile leakages.

What kind of data is typically captured in Utility industry ?


There are typically 3 classes of Utility data which are captured across the Power grid
Meter data streams
-          Current
-          Voltage
-          Power  (across various phases at 15,30,60 minute intervals )
Grid events data pool ( Both Status data +  Exception events + Derived events )
-          Outage events
-          Voltage surge events
-          Tamper events ( reverse energy flow )
-          Voltage sag events
-          Weak emission signal events
-          "Last gasp" events
-          Power restore events
-          Volatility events
-          Low Battery alarm events
Grid Master data
-          Consumer data
-          Smart meter location data
-          Feeder station data
-          Substation data
-          Field force data
-          Organisational hierarchy data

Powerful advanced visualisation and machine learning techniques can help surface patterns in the 3 classes of data outlined above

1 comment:

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