Energy crisis: how AI can help reduce and optimize consumption

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Artificial intelligence algorithms and machine learning allow you to use consumption based on cost data to redistribute demand and avoid network load peaks. You can optimize consumption and reduce energy costs for businesses using artificial intelligence solutions, learn how to continue reading this post.

For several years, electrical and technological companies have been investing in the development of artificial intelligence solutions for energy consumption objectives. The environmental issue and the transition to more sustainable energy production had already all stakeholders to reduce consumption. Companies and regulators can use artificial intelligence applications to predict future energy demand, analyze consumption, and simulate different scenarios.

Neural networks are used to analyze consumption data

A data collection strategy is necessary for any company that wants solutions based on artificial intelligence.  Consumption forecasts are based on the analysis of historical data to define recurring behaviors and patterns. To deploy an artificial intelligence solution to reduce energy costs, you need to collect complete, high-quality consumption data.

Once consumption data has been predicted and simulated, it is possible to address demand redistribution. Artificial intelligence algorithms can be used to define and, where possible, automate cost-reduction corrective actions. The demand for energy on the network can be managed through optimization models, which redirect consumption requests to the most convenient time slots based on market rates or cost data, real or simulated. You can also reduce consumption to reduce network load peaks.

As a result, artificial intelligence not only provides data but empowers frameworks’ cost-reduction strategies by automating consumption optimization processes.

The solution of Artificial Intelligence

The goal is therefore to integrate these predictive models into the company’s operational and decision-making processes, as well as to develop future strategies and corrective actions. Intelligence algorithms and machine learning in this sense can increase consumption based on cost data and redistribute demand to avoid network peaks.

An AI framework for the optimization of energy consumption acts on three different levels, whether it is a manufacturing company with various goods and production facilities, or a consumer office building for the simulation of future scenarios, the application of corrective actions, and the management of any additional energy systems.

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