The 9 Artificial Intelligence trends 2020 to keep an eye on – pt 2

We have already read the first 4 of the 9 trends in the world of artificial intelligence for 2020.

We continue the list with other 5 applications of this technology, hoping you will find some ideas to put artificial intelligence at the service of business goals and plant performances.

5. Interception of energy inefficiencies in consumption or production

This application is already known to the Italian market, pioneer in applying energy models for  quickly understanding if you are consuming well or not (due to the energy cost, among the highest in Europe).

Using Machine Learning algorithms, that are more complex than linear regression (which is still a great start and it learns from data in the same way as any other machine learning algorithm), it is possible to characterize with more precision the optimal energetic behaviour of an asset, a building, a plant. The aim is to be able to quickly identify deviations from normal behaviour and act accordingly.

It is estimated that by applying energy consumption models, it is possible to save between 1% and 10% on energy costs.

(Here you find our case study where applying energy models, we reduced the energy consumption of a production line by 11%).

Similarly, it is possible to control through intelligences the plant producibility, whether it is a small co-generator or a large combined cycle power plant, up to renewable wind and photovoltaic farms.

Digital maturity required: defined –
  • – Models should receive data every week at least
  • – It is necessary to have a historicized database appropriate for the training of the models
  • – A hardware and software architecture is also needed to support the communication flow and the installation of an AI platform.

6. Precise baselines for savings quantification

The necessity to demonstrate the savings achieved for activating EPC contracts or for the possibility to receive incentives, has stimulated the market to provide precise solutions by using linear regression and normalization of consumption.

With more complex algorithms, the baselines can be more precise and include several variables, also related to each other, to evaluate the status of the system. As a result, the comparison is always better representative of the savings actually achieved thanks to the operations of efficiency or extraordinary maintenance, as the factors that influence consumption variability are already included in the assessment.

Digital maturity required: managed –
  • – It is necessary to have a precise database historicized and representative of the ex-ante status.
  • – It is indispensable to collect data that are useful for the use of the model related to the ex-post status. These data do not need to be directly connected to the model, as the evaluation is done at fixed intervals and can include the manual data upload.

7. Prediction of production capacity and consumption forecast

The energy market requires energy producers to forecast the maximum plant producibility for the next day, while consumers need the forecast of the probable energy demand for the same period. Often, finding a model that represents both is difficult and the result is not precise, thus conservative coefficients are applied to avoid sanctions.

By training machine learning models on the basis of the history of consumption or production in accordance with production volume forecasts, weather conditions and other available information, it is possible to increase the accuracy of the model, guaranteeing higher margins to resellers and less expenses to buyers.

Digital maturity required: managed –
  • – It is necessary to have a precise database historicized and representative of the maximum load for the production and of the normal consumption curve for the user, combined with the forecast for the same periods.
  • – The use of the solution can be done by manual upload. However, having a solution that can make the prediction through data provided automatically and generate reports ready for sharing, simplifies the activity a lot.

8. Process optimization

As well as quality control through image recognition, it is possible to actively act on processes and plants by leveraging machine learning potentialities.

Creating a model able to represent the system behaviour allows to minimize production costs and maximize efficiency by asking the solution to autonomously act on the control parameters of the process (such as set points, temperatures, pressures, flows etc.).

The difference from a normal controller is how the result of the target function (minimum cost or maximum efficiency) is identified, as it is based on how the asset would have behaved under certain conditions. The model becomes a simplification of the digital twin of the process or of the asset, precisely representing its behavior basing on the different operational conditions.

Digital maturity required – digital oriented
  • – The system has to be integrated, connected in real-time with the process data and able to act precisely on set-points and control parameters.
  • – The amount of data needed to train a sufficiently precise intelligence is high and it has to be updated periodically in order to always represent the correct plant functioning.

9. AI on edge

It sounds like the title of a science fiction movie, but it is nothing more than an intelligent sensor capable of operating artificial intelligence algorithms directly on site. Whether it is anomaly detection, predictive maintenance, performance control or others, having the intelligence in the field saves data traffic, space in central servers and cybersecurity problems caused by data exposure.

The intelligence runs directly on the hardware installed on site and can interact with operators through interfaces and other signals.

Digital maturity required: integrated and interoperable –
  • – The intelligence has to be trained according to historical data
  • – It is necessary to have a certain control over the intelligence performance in order to avoid degradation of the system reliability.


The spread of Artificial Intelligence in the industrial world is already reality: intelligences can be implemented into industrial processes, maintenance management, systems control and much more. Actually, not all the applications require a highly integrated system. Each one of the applications presented can already  be implemented and many are already examples of success.

If you want to participate to the industry revolution thanks to AI, contact us to discuss about it together! We can also evaluate your company’s digital maturity and advise you on which projects to develop and how.

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