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 is not automatically meaningful. Only through plausibility checks and expert interpretation do numbers become a reliable basis for decision-making.

Data evaluation as a tool for the potential study

Feasibility studies aim to identify opportunities for technical and economic improvements in energy facilities. The goal is not only to recognize obvious weaknesses but also to identify areas of potential that often remain hidden during normal operation.
Data analysis is a key tool for this purpose. It enables a better understanding of the plant’s actual historical operation, helps trace operating patterns, and reveals trends over extended periods. Especially with more complex plants, a single snapshot is not sufficient to draw reliable conclusions. Only through the analytical examination of historical data, technical design parameters, and economic conditions can a comprehensive picture emerge.
The benefits of data analysis are most evident when operational data is combined with technical expertise, plant knowledge, and practical experience. In this way, individual measurement values, key performance indicators, and economic metrics form the basis for strategic decisions.

Which data is included

As part ofIn a potential study, different data sources are considered depending on the specific question at hand. These typically include operational data, design data, technical parameters, economic information, as well as empirical and target values from ongoing operations.
Operational data show how the plant is actually being operated, while design data reflect the original planning. Economic data makes it possible to quantify identified potential in terms of costs and revenues.
The interplay of these different data sets is particularly crucial, as a technical anomaly is not necessarily economically relevant. Conversely, a seemingly minor technical effect can have significant economic implications over extended periods of operation.

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 key component of data analysis is plausibility checking. Operational data does not automatically correspond to reality; measured values can vary, sensors can exhibit deviations, operating states can change, and time periods can be influenced by specific events.
This does not mean that data is inherently unreliable; rather, it must be considered within its technical context.
Plausibility checking helps prevent hasty or incorrect conclusions. When unusual values are detected, the first question is whether these values are technically plausible. Do they align with the rest of the system’s behavior? Is there a plausible explanation based on operational conditions? Is this a recurring effect or a one-time anomaly?
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 technical analysis, communication with the operator is an important part of the evaluation. Not every anomaly in the data automatically indicates a technical problem.
Some patterns can be explained by seasonal operating conditions, maintenance, fuel changes, partial-load operation, revised schedules, economic constraints, or special events during the period under review. Such information is often not fully apparent in the raw data alone.
That is why communication with the operator is an essential part of the plausibility check. It helps ensure that data is not viewed in isolation, but rather cross-referenced with actual plant operations and used to understand the strategy of the plant-operating company.

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 analyzed, validated, and interpreted from a technical perspective, a more reliable picture of the facility emerges. This picture serves as the basis for evaluating technical and economic potential.
This process can help answer a variety of questions:

– 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?

Even so-called “low hanging fruits” often become visible when operating data is not only collected but also correctly classified. Small adjustments in driving style, changed operating strategies or targeted technical measures can be evaluated much better on the basis of a well-founded analysis.

Result of the data analysis

The data analysis process does not merely result in a graphical representation of measured values. The goal is to obtain a tangible and reliable picture of plant operations:
• What operating conditions occur?
• What correlations can be identified?
• Where do anomalies or potential opportunities arise?

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.