The energy sector has differences with other sectors, mainly due to a strong regulation and a limited capacity to improve its revenues, because they are subject to current prices and consumption.
Also, another particularity of this sector is the cost of the physical infrastructures which support the operational side of the business.
That is why most of the activities are focused on operating improvements, in aspects such as:
- Optimization of energy networks.
- The improvement of quality of service and a reduction of failures.
- Fraud detection.
- Adequacy of the generation/ purchase of energy
The digital transformation of this sector has focused on the digitization of networks, the exploitation of data, and the improvement of customer’s experience, for example by adjusting the supply to an individual actual consumption, or by establishing actions that allows to be an active element of the chain.
The main technical breakthrough of this sector has been the digitization of networks, from generation / distribution plants to hubs and smart meters.
This progress generates a large amount of data, not only in relation to telemetering (from 12 measures a year to almost 10,000, multiplied by millions of customers), but also in the network monitoring..
In this scenario, the use of Big Data technologies, with data analysis capabilities (Data Analytics), offers incountless benefits to energy companies, both distributors and suppliers, from a simple operational optimization of work to provide predictive models of the customer’s activity or even the detection of fraud.
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For years Indizen has collaborated with the Nuclear Safety Council of Spain on various projects related to the analysis of safety in nuclear power plants.
"Investigation of an XML-based System for Simulation Procedures of Nuclear Plants"
This integrated simulation system is been developed by our company, together with researchers of Energy Systems Department of the School of Mines (DSE-EM). It covers as closely as possible the operator-plant dynamics and it can be applied to analysis in detail with a powerful, widely-used simulation codes (ie, MAAP, RELAP5 or TRACE in nuclear industry) integrated into the simulation of operations. It is currently under development co-financed by the PROFIT Program for Informatic Technologies
By using an advanced techniques in data analysis, such as Machine Learning, deep learning or graph analysis, we have helped to detect electricity frauds.
Interpretation of data obtained from different elements of the network with others environmental data has enabled us to generate models that reduce the occurrence of faults in network, to predict incidents and to applicate interventions programs before their occurrence.
CASES OF SUCCESS
Discover our projects and cases of success in health sector
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