Artificial intelligence energy consumption monitoring

The rise in energy consumption globally projects a 50% increase in its consumption by 2050 if unaddressed. To address this rising concern and to achieve the ESG goal of any industry, energy consumption monitoring using advanced technology can be the key component. Technological advancements such as artificial intelligence, machine learning, and IIOT play a big role in monitoring energy consumption and achieving ESG goals in industries and plants. In fact, by integrating AI, energy consumption across all sectors (industries, buildings, transport, and consumers) can be reduced by up to 25%, resulting in significant improvements in the GDP of any country. Real-time monitoring and predictive analytics with an accuracy of 90% enable companies to optimize energy consumption, promoting environmental sustainability and achieving ESG goals.

AI-enabled predictive maintenance is a great way to minimize equipment downtime and energy consumption. Up to 20% of energy saving in any industrial setting is possible using the right combinations of sensors and artificial intelligence energy consumption monitoring and feedback systems. NrgMonitor is an artificial intelligence energy consumption monitoring solution by Nanoprecise. It analyses the data collected by different machines in the production unit to generate meaningful insights, which can then be used to facilitate proactive energy management strategies. NrgMonitor, being a robust artificial intelligence energy consumption monitoring solution, also mitigates the challenges associated with variable energy consumption by various industrial equipment. It helps analyze and predict energy consumption and reduce waste. Industries and Companies can analyze data in real time with accuracy and efficiency by leveraging AI-based predictive capabilities. 

Nanoprecise’s artificial intelligence energy consumption monitoring solution, NrgMonitor, is cost-efficient. Although it requires an initial investment, this AI-driven solution offers a significant ROI depending on the company’s standard operating procedure. Upgrading from legacy maintenance systems or adjusting compatibilities can help one gain the most from NrgMonitor.
Here are some reports of the cost benefits to our customers using Nanoprecise PdM and Energy Monitoring Technology





NrgMonitor, Nanoprecise‘s artificial intelligence energy consumption monitoring solution, is an important part of the world’s transformative shift towards the Industry’s ESG goals. As part of this change, NrgMonitor offers its users the following benefits.

  • Diverse energy utility equipment is managed and monitored efficiently.
  • Predictive maintenance and real-time monitoring help to save energy and avoid waste.
  • The cost-efficient solution has a greater ROI than predictive or preventive maintenance.
  • It helps to promote sustainability and regulatory compliance across sectors.
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Reducing Downtime in Metal Industry

Our IoT driven Predictive maintenance solution helps to reduce downtime, monitor, collect exchange and analyze data from machines to enhance manufacturing processes of the metal industry.

Machine failure in the mines? No worries.

Our solutions can add immense value to your entire mining supply chain by harnessing the power of Industry 4.0. The asset performance will be optimized, costs and machine downtime can be reduced leading to a boost in ROI.

No more unplanned downtime in Cement Industry

Our Industry 4.0 digital solutions can help you tackle the challenges in cement production such as large energy consumption, high costs and complex processes.

Protect your assets with Zone Approved Solution

Our digitization solutions in industrial equipment maintenance can help oil and gas companies streamline maintenance. Our predictive analytics and conditional data monitoring help anticipate failures, reducing unplanned maintenance and unscheduled downtime.

No more Downtime, Keep your Machines Running in Chemical Plants

Our AI driven analytics can propel your chemical business to new heights of reliability by optimizing asset longevity and impacting top-line growth through proactive identification of upcoming machine failures. IoT driven asset maintenance solutions can provide immense flexibility and agility to production.

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Frequently Asked Questions

The artificial intelligence energy consumption monitoring solution NrgMonitor by Nanoprecise helps with real-time monitoring and predictive maintenance of industrial machines. The real-time data collected using sensor technology enables an organization to gather insight into overall energy consumption. NrgMonitor can predict potential equipment failures or malfunctions, optimizing machine performance and reducing equipment downtime. 

NrgMonitor promotes proactive energy management to ensure sustainable practice. Its ability to project real-time energy consumption data with a predictive maintenance strategy helps to reduce downtime and save energy. The AI-driven data insights optimize operations and streamline efficiency.

NrgMonitor is an artificial intelligence energy consumption monitoring solution by Nanoprecise that proactively monitors and manages energy consumption in an industrial setting or a manufacturing plant. With access to real-time data, NrgMonitor helps reduce downtime and better plan the operation of production plants.

The following are the advantages of using Nanoprecise’s NrgMonitor.

  • Real-time energy monitoring provides real-time insight into energy consumption. 
  • It guarantees proactive management and predictive maintenance.
  • Predictive analytics reduces downtime and prevents equipment failure.
  • Optimization of energy usage saves costs.
  • It improves the overall operational efficiency.

Nanoprecise’s NrgMonitor solution’s process and methods are significantly different from traditional RCM methods. The artificial intelligence NrgMonitor solution offers a comprehensive analysis of energy consumption. This helps to create a baseline in terms of both quality and cost. On the other hand, the traditional RCM method can only detect defects in motor-driven machinery. This lack of insight reduces the chance of developing an efficient strategy for a cost-saving and sustainable future.