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Big data and energy prediction


That Big Data has infinite applications is not something new, nor is it the fact that it can bring great benefits to the Utilities sector. We talk specifically about the energy industry. The benefits are applicable to the different stages of production processes.

Regarding the first of them, we emphasize that one of the main conclusions that are extracted from studies of the energy market is that one of the aspects to improve in the whole industry is the use or implementation of new technologies and of course, one of them is the famous Big Data.

All the facilities, regardless of the type of energy they use, generate a large amount of data: data emito of IoT, monitors, climatological data and a long etcetera. All this indicates that Big Data can be used to achieve greater efficiency in the production of energy and its optimization in the phase of commercialization and energy exchange.
Actually, all of us try to do a Big Data exercise, when for example summer comes, the temperatures raise and we notice an increase in our energy bill because the number of devices we connect is greater. The key is to be able to know how much each of them spends and how to save on the bill.

There are techniques that clarely help us for saving on the bill like not to use devices or establish a stable temperature to be more efficient, however, measuring consumption seems a more complicated task.
This task is for us almost impossible to achieve, but not for companys because they have electric meters that record in detail the cost of each device, that is, they collect enormous amounts of data that let them know our behavior´s pattern in terms of energy consumption.

So we take another step in the production process. Nowadays, it is possible to measure and analysis the data that collet the devices and that the energy companies have , using Big Data techniques to optimize the purchase of energy.
This is achieved by the development of mathematical and technical data models to extract relevant information and predict consumption. In addition, with machine learning techniques, these processes can be automated and results can be learned more and more efficiently.

Here we come to an essential point, the buying process. The energy purchase process is vital for energy companies because they acquires energy based on predictions of consumption. The higher the precision, the more you can adjust the purchase. As a result of greater efficiency throughout the supply chain, we all hope that the use of technology will bring about a greater adjustment in the bill of final consumers.

 

SUMMARY

Big Data and the use of techniques such as Data Mining or Machine Learning are not new, nor is it something that the Utilities sector can take advantage of, as is the case of the energy industry. In this sector, companies use Big Data tools in the different stages of the production process, from energy production to prediction with advance analytics and forecasting for the purchase of energy.
The higher the precision, the more you can adjust the purchase. As a result of greater efficiency throughout the supply chain, we all hope that the use of technology will bring about a greater adjustment in the bill of final consumers.

 

BIBLIOGRAPHIC REFERENCES

Msmk, (2015), August 19, 2015, Predict the consumption of light by using big data.

R.Rodríguez Marcía, January 29, 2016, Big Data in renewable energies.

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