Optimize Your Industrial Energy Management

Transforming data into actionable energy insights for multiple sectors.

Empowering Industrial Energy Solutions

At Rockwell Automation, we harness data from diverse sectors to optimize energy consumption and enhance operational efficiency through advanced analytics and innovative technologies.

Energy Management Solutions

Optimizing energy efficiency across manufacturing, chemical, and power generation sectors with advanced analytics.

Data Normalization Services

Standardize and clean energy consumption and operational data for accurate insights.

Report Analysis Support

Extract critical information from maintenance reports and operator logs to enhance performance.

Advanced Data Insights

Utilize AI for semantic understanding and relevant data extraction to drive efficiency.

Energy Insights

Optimizing industrial energy data across multiple sectors effectively.

An aerial view of an industrial area featuring a large wind turbine situated adjacent to a facility with rooftop solar panels. Surrounding the structures, there are patches of grass and dirt with a network of roads and pathways visible. A few scattered trees line the streets and there are several parked cars near the building.

The application of machine learning tools in industrial energy efficiency is bringing multi-dimensional energy insights to enterprises in a data-driven way, promoting the transformation from "passive response" to "active optimization".

Machine learning collects equipment operation data (such as temperature, pressure, and vibration) in real time through the Industrial Internet of Things (IIoT), combines historical energy consumption records, and builds energy consumption models to locate high-energy-consuming links. The steel plant analyzed the blast furnace operation data to identify the energy waste caused by the insufficient combustion efficiency of the hot blast furnace, and used the deep learning model to optimize the combustion parameters to reduce the energy consumption per unit product by 5%-10%. This precise diagnostic capability breaks the limitations of traditional reliance on manual experience, especially in highly complex industries such as chemicals and electricity, and can quickly locate hidden energy consumption problems such as leakage and idling, reducing energy waste.

A large industrial building with multiple tall smokestacks is situated alongside a body of water. The building has a rectangular shape with numerous small windows and is surrounded by a line of trees. A series of electrical towers and power lines extend diagonally across the left part of the image, indicating an industrial setting.

A large industrial facility is emitting thick clouds of white smoke, suggesting active production or power generation. A single wind turbine stands prominently in the foreground, juxtaposing the traditional industrial architecture with renewable energy technology. The sky is overcast with heavy, grey clouds, adding a somber tone to the scene.

A large industrial facility is situated by the water, featuring a tall smokestack and several industrial buildings. A large pile of coal is visible, with conveyor belts running through the facility. The sky is partly cloudy, creating a backdrop of blue and white hues.

Dynamic energy dispatch and deep integration of renewable energy

Machine learning plays a dual role in demand-side forecasting and supply-side optimization. Time series models (such as Transformer) can predict the energy consumption curve of a factory for the next 72 hours, and adjust production shifts based on electricity price fluctuations, reducing the peak electricity cost of a certain automobile factory by 18%. On the supply side, wind and solar power generation prediction models (such as LSTM) can predict output one hour in advance and guide the charging and discharging strategy of the energy storage system.