Data evaluation & plausibility check

Where is there unused potential in an energy system – and why does it often remain undiscovered during operation?

The answer lies in the correct interpretation of data. Only when operating data, technical correlations and practical experience are brought together can a reliable picture of plant operation be created. Data evaluation is much more than just creating diagrams. It reveals how a plant is actually operated, which patterns characterize its operation – and where technical and economic optimization potential lies hidden.
This is precisely where the challenge lies: data does not automatically reveal the truth. It is only through plausibility checks and professional classification that figures become a reliable basis for decision-making.

Data evaluation as a tool for the potential study

Potential studies aim to make technical and economic improvement opportunities in energy systems visible. The aim is not only to recognize obvious weak points, but also to identify potential that often remains hidden during operation.
Data evaluation is an important tool for this. It makes it possible to better understand the actual historical plant operation, to understand operating modes and to visualize developments over longer periods of time. Particularly with more complex systems, a single snapshot is not enough to make reliable statements. A comprehensive picture only emerges through the analytical consideration of historical data, technical design values and economic framework conditions.
The benefits of data evaluation arise above all when operating data is combined with technical understanding, plant knowledge and practical experience. This turns individual measured values, key figures and economic parameters into a basis for strategic decisions.

Which data is included

As part of a potential study, different data sources are considered depending on the issue at hand. These typically include operating data, design data, technical parameters, economic information as well as experience and target values from ongoing operation.
Operating data shows how the system is actually operated, while the design data reflects the original planning. Economic data makes it possible to quantify identified potential in terms of costs and revenues.
The interplay of these different data is crucial, as a technical anomaly does not automatically have to be economically relevant. Vice versa, a seemingly small technical effect can have a major economic impact over longer operating times.

From data to technical evaluation

Operating data can tell you a lot about a system. They show load curves, temperatures, pressures, availability, energy flows or changes in system operation. They can indicate whether a system is being operated predominantly at its design point, whether certain operating states occur particularly frequently or whether anomalies develop over time.
At the same time, this data initially provides an image of the system operation. Whether a technical or economic potential can be derived from this is only revealed in the professional assessment.
Similar to a medical health check, a blood count provides important values, but not yet a complete diagnosis. Only the interpretation by an experienced specialist can provide a reliable assessment.
It is the same with energy systems: diagrams, key figures and trends provide indications. The actual assessment is based on technical understanding, experience and the classification of the values in the overall system of the installation.
Many correlations cannot be considered in isolation, especially in energy installations. Temperatures, load conditions, fuel quality, efficiency levels, availability, operating modes and economic conditions all influence each other. A change at one point can have an impact on several other areas of the system.
Expert data evaluation therefore not only asks what the data shows. Above all, it asks what this data means in the specific plant context.

Plausibility check: A look behind the data
A central component of data evaluation is the plausibility check. Operating data cannot automatically be equated with reality, measured values can scatter, sensors can show deviations, operating states can change and time periods can be characterized by special events.
This does not mean that data is fundamentally unreliable, it is necessary that it is viewed in a technical context.
Plausibility checks help to avoid premature or incorrect conclusions. If conspicuous values are detected, the first question is whether these values are technically plausible. Do they match the rest of the system behavior? Is there a plausible explanation during operation? Is it a recurring effect or a one-off peculiarity?
This classification is not carried out in isolation from actual plant operation. Additional information that is relevant for the evaluation of the data often only emerges through comparison with operational experience.
Only when such questions have been considered can data be meaningfully used for the evaluation of potential.

Data and operational knowledge

In addition to the technical analysis, communication with the operator is an important part of the assessment. Not every anomaly in the data is automatically a technical problem.
Some patterns can be explained by seasonal operating modes, maintenance, fuel changes, partial load operation, changed schedules, economic requirements or special events in the period under consideration. Such information is often not fully visible in the pure data.
This is why communication with the operator is an essential part of plausibility checks. It helps not to view data in isolation, but to compare it with the actual plant operation and to understand the strategy of the company operating the plant.

Plausible data evaluations therefore result from the interaction of data, technical understanding and operator knowledge.

From data evaluation to potential assessment

Once data has been evaluated, checked for plausibility and technically interpreted, a more reliable picture of the system emerges. This picture forms the basis for evaluating technical and economic potential.
Various questions can be answered in the process:

– Where are there recurring anomalies?
– Which operating conditions have a particularly strong impact on system operation?
– Which deviations are technically relevant?
– What potential seems realistic?
– Which measures should be examined more closely?

Low hanging fruits” in particular often only become visible when operating data is not only collected but also correctly classified. Small adjustments in the operating mode, changed operating strategies or targeted technical measures can be evaluated much better on the basis of a well-founded analysis.
Result of the data evaluation
At the end of the data evaluation, not only a graphical representation of measured values is produced. The aim is to obtain a tangible and reliable picture of the system operation:
– Which operating states occur?
– Which correlations can be identified?
– Where do anomalies or possible potentials become apparent?

To this end, data is condensed, checked for plausibility and technically classified. This results in presentations and evaluations that are not only comprehensible but also reliable. They form the basis for evaluating potential in a comprehensible manner and deriving further steps in a targeted manner.

Conclusion: Data as the basis for reliable decisions

Data evaluation is a key tool within a potential study. It makes visible how a plant is actually operated and creates a basis for evaluating technical and economic improvement options.
However, its value does not arise solely from the presentation of measured values, key figures or curves. It is crucial that data is checked for plausibility, technically interpreted and compared with actual plant operation.
This is the only way to turn operating data into reliable findings – and these findings into concrete starting points for a well-founded assessment of potential.