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Future with AI and renewables

Artificial intelligence is seeing increasing application in various areas of our society, and one sector in which it could have a significantly positive impact is the energy sector. In the field, in fact, the use of this technology is being implemented to find innovative solutions to improve the performance of various infrastructures. In particular, predictive models built using Machine Learning, a branch of Artificial Intelligence, have enormous potential when applied to renewable energy plants. Let’s see in which ways.

Never stop learning

This is what a Machine Learning model does, continuously learning from the data it receives to improve its operation. This virtuous mechanism can be used in the field of renewables to make the most of plant operation. Through the acquisition of data, in fact, the Machine Learning algorithm learns heterogeneous information, encodes it and formulates a predictive model that is constantly updated over time. Applying this technology to renewables means being able to monitor the inflow of energy into the various plants and optimize production by also managing situations of energy shortage or excess.

Machine Learning can also be used to improve energy distribution performance. In fact, monitoring the consumption of households and the use of energy in business facilities can help to better manage the supply of energy according to the different consumption needs of various areas.

Ensuring a circular economy

Artificial Intelligence could support humans in solving complex systems faster and more efficiently. For renewables, this also means being able to accelerate and improve sustainability processes by establishing an efficient circular economy system. Optimizing infrastructure, monitoring consumption and better managing energy production and delivery creates a significant impact over time not only for the environment but also for the economy, from the economy of an entire country to that of a single family, for example, customizing the balance between production, consumption and cost expenditure.

Omnienergy and the Orchestra Conductor

An example of the application of Artificial Intelligence in the energy sector can be found with Omnienergy, the Omninext group company that provides a digital platform for forecasting the energy production of renewable energy plants, enabling them to optimize performance and thus achieve better results in a circular economy cycle.

Omnienergy is based on a Machine Learning model called ‘Orchestra Conductor’, which uses several underlying models. At each prediction horizon, the Orchestra Conductor assesses the quality of the predictions of the secondary models and dynamically assigns the weight of their contributions. In this way, the machine learning model acquires and processes the information, analyses and engineers the data, cleaning it of any errors to obtain a forecast value that is the most representative of reality.

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