The possible uses and potentials of data science in the energy sector are almost inexhaustible due to the gigantic amounts of data and the possibilities for linking with other data.
With the help of big data analyzes, the energy consumer can now also be given individual recommendations on how to save energy - an important basis for customer loyalty. As a further service, it is possible to compare current consumption with past values and statistical average values from comparable households or businesses.
Big data and artificial intelligence allow permanent observation with extensive analyzes and forecasts - a task that was almost impossible to manage in the past. This results in massive advantages for the energy industry:
• Improvement of processes and coordination across the organization
• Failures of technical equipment can be forecasted (predictive maintenance)
• More accurate price forecasting and real-time energy demand forecasting
• Improved exchange of information between different market players, such as network operators, consumers, balancing group managers, storage operators and the various producers
• Prediction of customer churn and fraud detection (churn prediction and fraud detection)
HData Systems assist its clients with cost-effective analysis
Information that may not have been used in the past can be used to open up new business areas or to develop services and minimize risks.
The large amount of information can be recorded cost-effectively, reliably and efficiently with big data technologies - both internal company data and external data.
For the detection of relevant patterns, the fault tolerant learning and forecasting in the energy industry, we rely on:
• Neural networks (and other processes from the field of artificial intelligence)
• statistical procedures for validation
We support you in lowering the transaction costs for analysis, forecasting and monitoring and pushing the limits of the currently possible analyzes and evaluations.
Why energy businesses should consider data analytics
• Better consumption and demand forecasts
Demand forecasts are of central importance for energy suppliers, as they buy the electricity for end customers in large tranches on the electricity exchange. With the increasing accuracy of the forecasts, supply and demand can be significantly adjusted, thus significantly reducing the risk and unnecessary additional costs for suppliers. Predictive Analytics enables forecasts of electricity demand through stochastic analyzes based on consumption data and external data (e.g. weather data, vacation times, consumer behavior).
• Smart solutions for the energy consumer
Big data analytics can be used to create new dynamic contract models for additional sales for utilities from the masses of data obtained from smart metering. The mobile communications industry has been using such innovative contracts with great success for many years. In addition, the energy supplier can now design the energy tariffs individually, taking consumer behavior into account.
• Advance maintenance of electrical systems
Predictive maintenance enables the early detection of faults in electrical systems and components and prevents early system failure. Moving or rotating components in particular (energy-generating systems, generators) are exposed to natural wear and tear, which in the event of an actual failure can lead to malfunctions or dangerous situations.
• Commercialization of data
The precision of energy consumption through smart metering enables far-reaching insights into the lifestyle or operational conditions of end users. The energy supplier can not only get a comprehensive picture of the energy behavior of a household or company, but also which devices or machines the consumer has "on the grid". These potentials suggest that new billion-dollar markets for the commercial use of data are emerging here.
• Other fields of application of big data
In addition to the outlined fields of application, the identification and commercial analysis of data anomalies is another field of application. Energy theft and loss and reactive power such as the identification of endangered or overloaded devices and systems in the distribution network can be tracked using special methods and new technologies.
Some of our best data analytics tools at HData Systems
Microsoft Power BI
The Gartner Magic Quadrant