Big Data Analytics
There are certain problems that can only solve through big data. Here we discuss the field big data as "Big Data Analytics". The big data came into the picture we never thought how commodity hardware is used to store and manage the data which is reliable and feasible as compared to the costly sources. Now let us discuss a few examples of how big data analytics is useful nowadays.
When you go to websites like Amazon, Youtube, Netflix, and any other websites actually they will provide some field in which recommend some product, videos, movies, and some songs for you. What do you think about how they do it? Basically what kind of data they generated on these kind websites. They make sure to analyze properly. The data generated is not small it is actually big data. Now they analysis these big data they make sure whatever you like and whatever you are the preferences accordingly they generate recommendations for you.
If you go to Youtube you have noticed it knows what kind of songs or what videos you wanna watch next. Similarly, Netflix knows what kind of movies you like it. If you visit Amazon they know what kind of product you would prefer to buy it. So, how it actually happens, it happens only due to big data analytics. There is one example which is about Walmart. So what happens when Walmart uses big data analytics to profit from it. Now you will think about how they did it. Let us discuss, they study what the purchased pattern of the different customers. Their owner makes a strike on a particular area and when they made an analysis of it. So, they found out that people tend to buy emergency stuff like a flashlight, life jacket, and a little bit other stuff and also a lot of people buy chocolate. If you read the example you see how big data analytics can help improve or grow your business and can find better insights from the data you have.
Big data Analytics
Big Data Collected by Smart MeterIn earlier, have you notices the data was collected from the meter in our home to measure the electricity consumed. It is actually sending the data from one month but nowadays IBM created the smart meter due to the use of smart meter it actually collects data after every 15 minutes. Whatever energy we have consumed after every fifteen minutes it will send data and due to this big data is generated. If we see in the below picture we have 96 million reads per day for every million meters. This amount of data generated by the smart meter is pretty huge data. " Managing the large volume and velocity of information generated by short interval read of smart meter data can overwhelm existing IT resources. "
Problem With Smart Meter Big Data
IBM realized that it is generating a huge amount of data is important for them to give something from that data. For what they need to do? They need to do to make sure to analyze this data. So they realize that big data can solve a lot of problems and they can get better business insight through that. Let's move forward what type of analysis they do on that data.
How Smart Meter Big Data Is analyzed
So before analyzing that data, they came to know that energy utilization and billing was only increasing. Now after analyzing big data, they came to know that during peak load user require more energy and during off-peak times that users require less energy. So what advantages they must get from this analysis. One thing that we can think of right now is they can tell the industries to use their machinery only during off-peak times. So that load will be pretty much balanced and we can say even that time-of-use pricing encourages cost severe e-tail like industrial heavy machines to used off-peak times. It will save money as well because of off-peak time pricing will be less than peak time prices. So this just one analysis.
IBM Smart Meter Solution
Over here we first dump all our data that we get in this data warehouse after that it is very important to make sure that our user data is secure. Then what happens we need to clean the data as we discussed earlier as well there might be many fees that we don't require. So we need to make sure we have only useful material in our dataset and then we perform certain analysis.
In order to use this suite that IBM offered us efficiently. We have to take care of a few things.
- we have to be able to manage the smart meter data now there are a lot of data coming from all these million smart meters. So we have to able to manage that large volume of data and also be able to retain it because maybe, later on, we might need it for some kind of regulatory requirements and something.
- To monitor the distribution grid so we can improve and optimize the overall grid reliability. So we can identify the abnormal condition which is causing any kind of problem.
- We can also take care of the optimizing the unit commitment. Optimizing the unit commitment companies can satisfy their customers, even more, they can reduce the power outages so that their customers cant angry. They can identify more problems and then reduce it.
- Optimizing energy trading means that we can advise the customers when they should use their appliances in order to maintain that balance in the power load.
- Forecast and schedule load companies must be able to predict when they can profitably sell the excess power and when they need to hedge the supply.
ONCOR Using IBM Smart Meter Solution
Now let's discuss how ONCOR has made use of the i-beam solution. So anchor is an electric delivery company and it is the largest electrical distribution and transmission company in Texas and it is one of the six largest in the United States. They have more than three million customers and their services area covers almost 117 thousand square miles and they begin the advanced feeder program in 2008 and they have deployed almost 3.25 million meters serving customers of North and South Texas. When they were implementing they kept three things in mind.
- The first one is "it should be instrumented". So this solution utilizes smart electricity meters so that they can accurately measure the electricity usage of household in every 15 minutes because we already discussed that smart meter sending out data every 15 minutes and it provided data inputs. which is essential for consumer insights.
- It should be "Interconnected". Now the customer will have detail information about the electricity they are consuming and it creates a very enterprise-wide view of all the meter assets. It also helps them to improve service delivery.
- To make your "customer intelligent ". Now it is getting monitored already about how each of the household or each customer is consuming the power. So now they are able to advise the customer about may be to tell them to wash their clothes at night times. Because they are using a lot of appliances during the day time and maybe they divide it up. So they can use some of the appliances at an off-peak hour so they can save more money. This is beneficial for both the customers and the company as well.