How can you track and analyze energy usage across your IoT network?

Properly installed, calibrated, and maintained energy metering devices can provide a wealth of information for energy analysis, optimization, and troubleshooting.

The changing world of energy is making it increasingly challenging to optimize power reliability, energy costs, and operational efficiency in critical power environments such as hospitals, data centers, airports, and manufacturing facilities. Utility power grids are getting more dynamic, facility power distribution systems are becoming more complex, and cyberattacks threaten network stability. More competitive pressures and environmental regulations are pushing expectations for energy efficiency and business sustainability higher than ever. Addressing these challenges requires new digital tools designed specifically to enable faster response to opportunities and risks related to power system reliability and operations.

In order to establish a property benchmark of energy consumption in or IoT network, it is important register environmental conditions too and termical isolation especially of the data center.

Best way forward would be to integrate an IoT device over the existing powerline with a RTC to measure current flow; this installed to the consumer unit and then distributed across the distributed PSU points would give an accurate reading across multiple points. And if you take this design and spread it across a regional network then you can view real-time kW draw (down to mA accuracy) drain over specified time.

I strongly agree with you on this as these metrics are pivotal to analyse data on energy usage. Energy loggers can help to achieve this and IoT will make it easier to analyse as real time data can be analysed.

Many appliances vendors start to proactively collect electrical data from their appliances and offer insights through a mobile app. Customers can monitor and even remotely control their appliances to fit their schedules. There is a fundamental difference between collecting data for customer use and enabling control for grid operation. The latter scenario requires higher reliability, data resolution and bonding with appliances. For example, it is recommended to have a common data and communication protocols between appliances and IoT devices connected with them. It is not an easy task, as likely these individual devices are from different manufacturers. There is a need for technical standards and collaborations among manufacturers.

IoT devices continuously deliver energy, power quality, and equipment status information to operations and maintenance teams wherever they are. Smart metering device functionality ranges from a few basic energy measurements to hundreds of power and energy measurements, data logging, alarming, and advanced power quality analysis and equipment status monitoring. Embedded metering in smart equipment is cost effective and convenient for basic monitoring applications such as energy usage. For more advanced applications, such as power quality monitoring and power event analysis, advanced power quality meters are required.

Energy monitoring There exist a considerable variety in energy-monitoring based on the type of received feedback out of the power systems. This feedback is of utmost importance in learning and predicting future energy consumption and savings. In the following, we list and discuss these types in details, which can be either direct or indirect based types.Energy monitoring devices Essential component of any energy monitoring system is the sensing device. In order to monitor energy consumption using these sensing devises two critical parameters must be known; voltage and current. In order to measure these parameters, different methods have been used while applied to varying appliances types.

IoT devices include the following types: Sensor: Used to collect data on the status of the environment or equipment, such as temperature, humidity, pressure, etc. Controller: Processes sensor data and controls other devices according to preset rules. Communication module: enables the device to exchange data with other devices or cloud platforms over a wireless network. Wireless tags: Used to identify and track the location or status of items. Camera: Monitors a specific area and provides visual information.

Utilize IoT devices to elevate Smart Microgrid efficiency and sustainability Equip them with real-time energy management capabilities for instant consumption data and optimal energy distribution Integrate AI for predictive maintenance and anomaly detection, reducing downtime and extending asset life Employ edge computing for decentralized decision-making, allowing devices to autonomously manage energy based on local needs Ensure robust cybersecurity with advanced encryption and blockchain for secure communications Adhere to global standards like IEEE Std 1547-2018 for seamless device integration and interoperability across energy systems This approach not only enhances performance but also supports a resilient energy infrastructure

Harness the power of Blockchain to bring unmatched security and transparency to energy data aggregation By creating immutable records, Blockchain ensures that all transactional data remains tamper-proof and easily auditable, building trust across the network Integrate edge AI to process this data locally on devices, slashing transmission costs and accelerating response times This enables more precise, real-time adjustments in Energy Management, vital for responding to immediate Smart Microgrid conditions or unexpected shifts in energy demand This robust combination not only optimizes energy efficiency but also prepares the system for future scalability and more complex data operations, making it a cornerstone for advanced energy systems

Data on device power consumption can be collected by monitoring various parameters such as current, voltage, power, and temperature through sensors. This data can then be transferred to a data center via wired or wireless networks.

Data aggregation is a technique presently widely used to collect data from multiple resources, organize it, and summarize it as one set before analyzing it. This process usually takes through using various software programs specialized for managing data, such as Power PI, Tableau, and SAS. Other commonly used tools for data aggregation include MS Excel and Sales Force.

Data aggregation is crucial, considering both data aggressions and sudden peaks and lows. Engineers crafting aggregation logic must meticulously weigh the significance of each data point. Understanding the importance of individual data points ensures accurate representation and effective analysis. Aggressions in data, reflecting extreme values or anomalies, offer insights into system performance or irregularities. Meanwhile, sudden peaks and lows highlight critical operational shifts or potential issues. By meticulously considering these factors during logic development, engineers optimize data aggregation for informed decision-making, enabling proactive responses to fluctuations and enhancing the reliability and usefulness of insights.

Cost for good class sensors at relevant places should be encouraged so that source to load loss in any palace can be identified using analysis. 1 way is source to load loss categorisation and other way is target vs actual and bucketing different losses.

According to a report by McKinsey, optimizing energy usage in buildings alone could save $170 billion annually by 2030. Additionally, the International Energy Agency (IEA) estimates that implementing IoT solutions for energy management could reduce global carbon emissions by 1.4 gigatons per year by 2030. With these figures in mind, leveraging robust analysis tools becomes not just a matter of operational efficiency, but also a strategic imperative for sustainability and cost-effectiveness. By effectively tracking and analyzing energy usage, organizations can identify areas for improvement, optimize resource allocation, and contribute to their bottom line while reducing their environmental footprint.

After accumulating enough data, you need to use data analysis technology to discover the patterns behind the data. Data analysis can help us identify where energy is wasted, predict future energy needs, and formulate corresponding optimization measures. Using machine learning algorithms such as cluster analysis, we can find energy efficiency patterns across different groups of devices or systems.

To turn raw data into actionable insights, a tradeoff needs ot be done in terms of accuracy and speed. Executing analysis tools involves resources in terms of memory and computing power, which decdes whether to perform complex algorithm AI/ML based training to perform on the cloud and use the trained model on the edge device to produce the result.

Use the insights gained from the analysis to optimize energy usage across your IoT network. This could involve adjusting device settings, implementing energy-saving measures, or upgrading to more energy-efficient equipment.

Defining Optmization objective is the key. Objectives can be to minimise the cost, energy or to maximise the renewable penetration, resiliency.

Optimization of IoT device performance is also a key aspect of managing energy consumption. Monitoring and maintenance of equipment performance can ensure that equipment is operating at its best, thereby avoiding unnecessary waste of energy. Normally, equipment efficiency decreases over time, and even small performance degradations can result in significant increases in energy consumption. Therefore, real-time monitoring of performance and timely maintenance are essential.

Optimization strategies based on energy usage analysis involve automating processes, adjusting device settings, and scheduling operations to reduce unnecessary energy consumption while maintaining IoT network functionality. This can lead to significant cost savings and environmental benefits.

The advancements in energy metering technology combined with the emergence of highly specialized power management software applications, has made it simpler than ever before to identify and manage power quality risks and opportunities. Modern energy management systems are now able to capture, analysis and present complex, high resolution energy system information in such a way that facility management teams can easily visualize and quickly reconstruct exactly what happened during a system incident. Without such tools and technology in place, companies that operate large and critical facilities are exposing themselves to unnecessary risks related to business continuity and efficiency.

24x7 monitoring is important for both machine learning & human learning Take the example of the Boiler Monitoring System, boiler efficiency can be derived through the direct efficiency method i.e. output/input. However, a better way of arriving at efficiency is the indirect method i.e. 100 - all the losses. The monitoring system provides continuous data on these losses and provides inputs on one requiring better management. One such loss "Blowdown Loss" is at times a manual activity and losses depend on blowdown activity by shift personal. Monitoring system provides inputs on optimal blowdown requirement, and also provides inputs if human resource in a particular shift needs training inputs.

By establishing a proactive ongoing monitoring, organizations can promptly detect anomalies or deviations from expected energy usage patterns, enabling timely interventions to address emerging issues. This allows for iterative refinement of optimization strategies based on real-time data insights. This iterative process fosters continuous improvement, driving efficiency gains and cost savings over time. Energy measurement and monitoring is certainly not a set it and forget it type of deployment.

Technology and energy regulations evolve over time. Regularly reviewing your energy management policies and practices ensures that you stay up to date with the latest advancements and best practices in the field. New technologies, tools, or methodologies may become available that can further optimize your energy consumption and improve overall performance. By staying informed and adaptable, you can continuously enhance your energy management approach.

You suppose to know and set the energy metrics first to calculate the consumption and these key metrics must be defined plus analysis should be easily available if your iot devices are well connected with sensors as you can easily find out data via it and you can regularly monitor the simulation.

?? Future-Forward Energy Management Strategies Explore AI-driven urban energy simulations for sustainable city planning. Harness decentralized Virtual Power Plants to empower remote communities and reduce infrastructure needs. Invest in advanced materials to enhance energy storage and solar panel efficiency. Push for universal IoT protocols for seamless global energy system integration. Ensure ethical AI use in Energy Management, promoting transparency and fairness. These initiatives lay the groundwork for a sustainable and equitable energy future.

Device Power Management: Ensure that devices are designed with efficient power management features. This includes using low-power components, optimizing sleep modes, and enabling dynamic power scaling to minimize energy consumption during idle or low activity periods.

Over and above what is stated above, I recommend the following 1. Dashboards Energy dashboards with source feeder wise reconciliation with respective loads, can identify energy concerns and leakages Like above, make a dashboard for water to optimise consumption 2. Technology Integration Log all the above data in a cloud with time stamping in real time basis, in a historian software Use AI, ML and Data analytics to find out anomalies in energy and water consumption

Using IoT devices can provide benefits but understand the risks. Cybersecurity vulnerabilities of IoT devices are potential attack vectors into your IT systems. Zero-day vulnerabilities may not be known and likely will never be patched, even if there were a way to do so.