Industrial Energy Waste: How to identify inefficiencies that consumption data doesn’t reveal

In the manufacturing sector, a significant portion of the energy used does not contribute to productive output: a study conducted by the International Energy Agency on the U.S. manufacturing sector found that nearly 50% of the energy used on-site is wasted. Identifying where and why this waste occurs, however, is much more complex when energy consumption is analyzed without considering the operational context.

In fact, an increase in energy consumption does not necessarily indicate inefficiency. It may be linked to an increase in production, but it can also be caused by an asset operating outside expected conditions, energy consumption during periods of inactivity, an incorrect setpoint, or a gradual decline in performance. When these issues are not detected promptly, they can recur over time and become persistent energy costs.

For Energy Managers and Operations Managers, the key is therefore not just knowing how much energy is being consumed, but understanding why that energy is being consumed and whether the consumption is consistent with the plant’s operating conditions.

Implementing an Energy Management System (EMS) allows for more advanced monitoring by linking energy data to the actual operating conditions of the systems. This makes it possible to interpret deviations in their proper context, better understand their causes, and identify with greater precision where action is needed.

Why Energy Consumption Alone Isn't Enough

In an industrial plant, energy consumption fluctuates constantly in response to changes in production volumes, shifts, product mix, cycle times, and operating conditions. For this reason, even when a company already has data from meters, PLCs, SCADA systems, BMS, or production systems, energy data alone is not sufficient to determine whether a change in consumption indicates an inefficiency.

To interpret it correctly, it must be considered in relation to consumption and operating conditions so that a normal variation can be distinguished from an actual decline in performance.

Without connecting these data points, certain anomalous behaviors can gradually become part of the normal consumption profile and thus go unnoticed. For example, an oven that consistently preheats ahead of schedule or an exhaust system that remains active during downtime can become structural costs that impact energy expenses and the plant's overall efficiency.

This becomes even more valuable when it involves similar assets installed at different sites. Two equivalent lines or two compressors of the same model, under identical operating conditions, should exhibit comparable consumption profiles. If this is not the case, the discrepancy can be one of the most immediate signs of an inefficiency that an analysis of a single plant might not reveal.

The main causes of inefficiency in industrial assets

Not all inefficiencies manifest themselves as consumption spikes. In many cases, the problem arises from recurring deviations, variations from expected behavior, or anomalous relationships between energy usage, load, and production output. The key, therefore, is not only to identify what consumes energy, but also to recognize when an asset or process is behaving differently than it should. Some of the most significant patterns are:

• Energy consumption that does not correspond to production output. When energy demand increases or remains high without a proportional change in output, there may be a mismatch between energy used and production. Analyzing consumption in relation to production volumes, cycle times, or load levels makes it possible to distinguish between normal growth and a loss of efficiency.
• Operational sequences that result in unnecessary energy consumption. A single asset may function properly, but the logic governing how multiple assets are started, stopped, or modulated can lead to inefficiencies. Overlapping cycles, the simultaneous activation of equipment, or suboptimal priority management can increase overall energy consumption without necessarily triggering an alarm.
• Progressive deviation from the baseline. An asset may continue to operate normally while its energy profile gradually deviates from historical or expected behavior. The variation may be minimal on a daily basis but become significant over the medium to long term, indicating a decline in performance before a failure or obvious anomaly occurs.
• Inefficiencies that emerge only under certain load conditions. Some assets do not exhibit abnormal behavior during nominal operation, but become less efficient at partial loads, during startup phases, or under specific demand patterns. Without a segmented analysis by operating conditions, these phenomena may remain hidden in the average consumption.
• Differences Between Equivalent Assets. As we have already seen, comparing machinery, production lines, or facilities with comparable characteristics and operating conditions makes it possible to identify anomalous energy performance that would not be apparent from an analysis of a single asset. Significantly higher energy consumption for the same output or under the same operating conditions can serve as an indicator of inefficiency that warrants further investigation.
• Consumption figures may appear normal, but they are out of context. A consumption value may fall within historical ranges yet still be inefficient if operating conditions have changed. For example, maintaining the same energy consumption when the production load is lower may indicate that some of the energy used is no longer necessary for the process.

An energy anomaly rarely stems from a single value that exceeds a threshold; rather, it manifests itself through changes over time in the relationships between consumption, loads, and production output. Consequently, energy KPIs are most valuable when used to interpret operational deviations rather than simply reporting on volumes.

Monitoring specific energy consumption makes it possible to verify the proportionality between energy used and production output, identifying performance deviations even when absolute consumption levels remain stable. Similarly, analyzing baseload and idle consumption during periods of downtime or low activity allows for distinguishing between necessary technical loads and avoidable energy waste.

Using EnPIs (Energy Performance Indicators) and a robust energy baseline provides greater visibility and control by correlating consumption data with process variables (temperature, pressure, operating hours) to compare actual performance with expected benchmarks. To complete the picture, a comparison of equivalent assets highlights structural inefficiencies that monitoring a single plant in isolation might overlook.

In summary, the effectiveness of monitoring does not depend on the quantity of metrics collected, but rather on the quality of the correlation between energy data and operating conditions, with a focus on identifying economically significant deviations.

Advanced EMS: from anomaly detection to measurable savings

Identifying an inefficiency is only the first step; extracting value from it over time is the real challenge. The effectiveness of an Energy Management System is measured by its ability to transform consumption deviations into actionable insights. By integrating energy data and process variables, the EMS dynamically maintains baselines and EnPI, instantly identifying whether a deviation is an isolated event or a structural cost.

E-BOOST, the EMS developed by Veil Energy, applies this approach throughout the entire performance cycle. The platform integrates data acquisition, condition monitoring, and the calculation of specific EnPIs, triggering predictive alerts to guide high-impact maintenance and operational interventions.

The real value comes from verifying the results after an intervention:

• Has the reduction in idle power lowered the baseload?
• Has a new setpoint improved specific energy consumption?
• Did the maintenance work restore the asset to its expected performance levels?

Measuring absolute kWh is misleading unless consumption is correlated with actual production volumes. Compared to traditional systems, E-BOOST normalizes the data based on process variables, isolating the actual effect of the intervention to provide a clear view of verified and measurable savings.

Energy efficiency does not always require high capital expenditures or hardware replacements. Significant savings can often be achieved by optimizing existing settings, sequences, and operating logic.

Do you want to understand where energy waste occurs in your plant? Turn plant data into measurable savings and better operational decisions with E-BOOST.