# Analytics

Detailed specification of available analytics functions and their application.

# Application notes

• Unit sensitivity: To this state, our algorithms are unit sensitive. Every pin and attribute is specified with an unit. Mind the specifications.

If unit conventions are disregarded, this can lead to errors and even misleading results of algorithms.

• Improving results: We continuously improve our analyses functions, interpretations and recommendations. Thus, performing exactly the same analysis at a later point in time can lead to different results.

# Example Analysis

This example leads through our description of analytics functions by providing exemplary values and descriptions. The first paragraph of an analysis function provides a short introduction to the analysis.

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Description
Results
Example
Components
Application
Quick Start

# Value

In other words: Why should I spend time to dig deeper into this analysis.

• Quick entry into analytics function specifications

• Exemplary template for analysis function specifications and documentation structure

# Recommended for component types

• All components in need for an example

Description

The Example Analysis is an example on how we structure the documentation of our analysis functions. The goal is to show you where to expect which kind of information to make your work as effective as possible.

In general the description provides information about the concepts of the analysis function, its use-case scenario, and value.

Results

# Qualitative warning level

Enum representations of traffic light colors providing a quick overview over the analysis result.

 Enum Color 0 green 1 yellow 2 red

# Interpretations

Interpretation text of the condition analysed.

# Recommendations

Recommendation texts on how to improve the operation of the analysed component.

# KPIs

Providing deeper insights to the cycle behavior. KPIs support human reasoning.

### Example KPI

Brief description of the example KPI, its determination, application, and value.

 KPI Identifier Value Range Unit example.kpi 0 to 1 kW

### Another Example KPI

...

Example

The Example tab provides a hands-on example for each analysis function.

The example building is equipped with an Example Component. This setup is used as a real test bench for the Example Analysis function...

In general you can expect a short demonstration on how we applied the analysis during our development and which results we got from our test bench.

Components

# Pins

• example pin

• ...

Most of the analysis functions require a mapping for the same pins regardless of the component data model. These pins are listed here. Component-related variations, exceptions, and deeper insights are provided component-wise further down this chapter.

# Attributes

• example attribute

• ...

Some of the analysis functions require attributes regardless of the component data model. These attributes are listed here. Component-related variations, exceptions, and deeper insights are provided component-wise further down this chapter.

# Components

## ​Example Component​

The short text snippets below a component provide information about how to utilize the analysis function for this component. In case you are new to component data models, we recommend to read the high-level introduction to components and check out the component data models for deeper insights.

## ​Another Example Component​

...

Application

The Application tab offers further information for real application of the analysis.

# Recommended Time Span

## 4 weeks

The Recommended Time Span gives the recommended time span for which the analysis function is intended. The time span is oriented to the time constant of the phenomenon investigated, e.g. in order to analyse weekly accruing patterns, a time span of 4 weeks is recommended. In shorter periods of time, the analysed phenomenon might not occur, while longer periods might lead to superposition of other effects such as seasonal influences and thus blurring of the analysis results. Of course, analysis functions can be used over flexible periods of time at your personal discretion.

# Recommended Repetition

## every 3 months

The Recommended Repetition provides a recommendation when the analysis should be repeated. The recommendation refers to a continuous operation of the analysed system. In the case of changes to the system, both physical and operational, a repetition of the analysis is generally recommended. Of course, analysis can be repeated at your personal discretion.

...

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Description
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# Value

Setpoint deviation is a strong symptom for faulty control loop operation, e.g. caused by

• Technical defects in the control loop supply,

• Control loop malfunctions, and

• Faulty control loop parameter settings.

Benefits of improving insufficient setpoint value attainment are:

• Higher occupants comfort

• Lower operating costs

• Higher energy efficiency

# Recommended for component types

Control loops, such as

• Heating systems

• Ventilation systems

• Air-conditioning systems

Description

The Setpoint Deviation Analysis aims to identify issues regarding the deviations between the desired value and the actual value within a building’s many control systems. Deviations can occur based on a whole host of reasons. Maybe the system is not supplied with a high enough temperature during certain hours to begin with, or there might be an issue with the controlling software. Maybe a blocked valve is to blame. The Setpoint Deviation Analysis’ key performance indicators (KPIs) can help pin down the root cause of the problem, or it might just confirm that everything is working as it should.

Results

# Qualitative warning level

Enum representations of traffic light colours providing a quick overview over the general setpoint compliance within the analysed period.

 Enum Color 0 green 1 yellow 2 red

# Interpretations

Interpretation of the setpoint compliance of the component in the analysed period.

# Recommendations

Recommendation texts on what actions to take, in case of sub-optimal setpoint compliance as well as recommendations on how to further investigate the root causes for such behavior.

# KPIs

Providing deeper insights to the cycle behavior. KPIs support human reasoning and provide full transparency of the algorithms reasoning.

## Incidence of setpoint deviation

Duration of the setpoint deviations, bundled by threshold value ranges.

 KPI Identifier Description Value Range Unit setpoint deviation.largerX.relative Duration with setpoint deviation larger than X 0 to 100 % setpoint deviation.largerYsmallerX.relative Duration with setpoint deviation larger than Y and smaller than X 0 to 100 % setpoint deviation.smallerY.relative Duration with setpoint deviation smaller than Y 0 to 100 % setpoint deviation.largerX Duration with setpoint deviation larger than X 0 to inf h setpoint deviation.largerYsmallerX Duration with setpoint deviation larger than Y and smaller than X 0 to inf h setpoint deviation.smallerY Duration with setpoint deviation smaller than Y 0 to inf h

## Operating time

Operating time KPIs provide information on the total time of operation of the analysed component during the analysed time frame.

 KPI Identifier Description Value Range Unit operating time Total operating time 0 to inf h operating time.relative Relative operating time 0 to 100 %

## Statistics of setpoint deviation

General information KPIs to give further insight into the setpoint compliance over the analysed time frame.

 KPI Identifier Description Value Range Unit setpoint deviation.maximum Largest setpoint deviation 0 to inf - setpoint deviation.minimum Smallest setpoint deviation 0 to inf - setpoint deviation.mean Average setpoint deviation 0 to inf - setpoint deviation.median Median setpoint deviation 0 to inf -
Example

The setpoint deviation analysis was applied to a real test bench, a heating system at the E.ON Energy Research Center, RWTH Aachen University. Thus, a thermal control loop component model was instanced and the respective datapoints mapped to this component.

In this scenario, the figure above shows the time series recorded for an exemplary period of 36 hours on a November workday. The temperature setpoint and the actual measured value started to drift apart around 12 am on the 19th. Since then, the control loop did not comply with the setpoint temperatures although the control loop was operating.

The automated interpretation confirms our visual analysis of the time series shown in the figure, summed up by the qualitative warning level "red". The recommendations provide further instruction on how to isolate and fix the cause for the inadequate setpoint compliance. Further, the result offers an advanced set of KPIs, providing additional insights into the control loop behaviour. They support human reasoning for a case-by-case analysis.

For example, the drop in temperatures is peculiar and could point to a technical defect or malfunction, such as a blocked valve. Another cause might be a sudden drop in the temperatures supplied to the distribution system, such as an heat-pump or boiler issue. Further investigation of the root cause is possible via data visualization on the aedifion front-end.

Components

# Components

## thermal_control_loop

### Pins

• outlet temperature

• inlet temperature

• operating message

## room

### Pins

• temperature

• temperature setpoint

• operating message

Application

# Recommended Time Span

• 1 to 7 days for significant deviation

• hourly scale for in detail analysis

# Recommended Repetition

• Every few weeks, since control loops are very sensitive to operational conditions.

• After the start of the heating or cooling period.

• If an issue is suspected.

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Description
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# Value

• Occupant comfort

• Occupant health

# Recommended for component types

• Room

Description

The Room Air Quality Analysis checks and interprets the compliance of carbon dioxide concentration in the air to the recommendations of DIN EN 13776: 2007-09. In case of poor air quality, measures for improvement are recommended. In addition, the algorithms identifies calibration errors by physical plausibility checks.

Results

# Qualitative warning level

Enum representations of traffic light colors providing a quick overview over the air quality over the analysed period.

 Enum Color 0 green 1 yellow 2 red

# Interpretations

Evaluations of the general air quality over the analysed period.

# Recommendations

Recommendation texts on what actions to take, in case of mediocre or poor air quality.

# KPIs

## Air quality classification

How long was the air quality in the room (based on carbon dioxide concentrations) considered “good”, “medium”, “moderate” or “poor”? Assessments are based on EU regulation classifications of Indoor Air Quality (IDA) classes 1 (“good”) to 4 (“poor”).

 KPI Identifier Description Value Range Unit co2 duration.IDA1.relative Duration with “good“ indoor air quality 0 to 100 % co2 duration.IDA2.relative Duration with “medium “ indoor air quality 0 to 100 % co2 duration.IDA3.relative Duration with “moderate “ indoor air quality 0 to 100 % co2 duration.IDA4.relative Duration with “poor “ indoor air quality 0 to 100 % co2 duration.IDA1.absolute Duration with “good“ indoor air quality 0 to inf h co2 duration.IDA2.absolute Duration with “medium “ indoor air quality 0 to inf h co2 duration.IDA3.absolute Duration with “moderate “ indoor air quality 0 to inf h co2 duration.IDA4.absolute Duration with “poor “ indoor air quality 0 to inf h

## Statistics of CO2 concentration

Providing deeper insights to the carbon dioxide concentrations over the analysed period.

 KPI Identifier Description Value Range Unit co2.maximum Largest CO2 concentrations 0 to inf ppm co2.minimum Smallest CO2 concentrations 0 to inf ppm co2.mean Average CO2 concentrations 0 to inf ppm co2.median Median CO2 concentrations 0 to inf ppm
Components

• co2

Application

# Recommended Time Span

• >1 days

• Utilize on days with room occupation

# Recommended Repetition

• On changes of room occupation or usage

• Every few months in order to check sensor calibration

• If an issue is suspected

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Description
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# Value

The Virtual Heat Meter helps to

• quantify the heat delivered by various heating circuits

• identify inaccurate heat flow data

# Recommended for component types

Heating systems, such as

• Heat pumps

• Boilers

• Heat meters

Description

The Virtual Heat Meter estimates the heat that is delivered by components such as heat pumps and boilers, based on temperature and volume flow measurements

Results

# Miscellaneous KPIs

Providing deeper insights to the setpoint compliance. KPIs support human reasoning.

 KPI Identifier Description Value Range Unit Q_dot.maximum Largest heat flux 0 to inf kW Q_dot.minimum Smallest heat flux 0 to inf kW Q_dot.mean Average heat flux 0 to inf kW Q_dot.medium Median heat flux 0 to inf kW

# Timeseries KPIs

 KPI Identifier Description Value Range Unit Q_dot.timeseries The complete timeseries of transferred heat 0 to inf kW Q_dot.timeseries_cumulated The complete timeseries of cumulated transferred heat 0 to inf kWh
Components

# Components

## heat _meter

### Pins

• outlet temperature

• inlet temperature

• volume flow

## boiler

### Pins

• outlet temperature

• inlet temperature

• operating message

• volume flow

## heat_pump

### Pins

• condensator outlet temperature

• condensator inlet temperature

• evaporator outlet temperature

• evaporator inlet temperature

• operating message

• volume flow

# Attributes

### volume_flow_unit:

The unit used in this datapoint needs to be specified in order for the analysis to yield correct result. If unspecified, the default unit assumed for this measurement is cubic meters per second. Acceptable inputs for this attribute include:

• cubicMetersPerSecond

• cubicMetersPerMinute

• cubicMetersPerHour

• litersPerSecond

• litersPerMinute

• litersPerHour

Application

• 1 - 3 days

# Operating Cycle Analysis

The Operating Cycle Analysis investigates and interprets the cycle behavior of components. Besides identifying sub-optimal cycle behavior, the algorithm provides recommendations on how to improve cycle rates, and KPIs for deeper insights.

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# Value

• Lower operating costs

• Higher energy efficiency

• Longer equipment and component life times

• Smoother system integration

# Recommended for component types

• Energy conversion plants

• Components with high start-up expenses

• Heat pumps

• Combined heat and power

• etc.

Description

The Operating Cycle Analysis aims towards a more constant operation of components and thereby towards a reduction of operational costs. Frequent start and stop processes lead to energy losses and higher wear and tear of the component compared to a constant operation. Further, a frequently alternating operation of a component, e.g. a heat pump, has negative effects on adjacent components, which are enforced to alternate as well. At the same time, too low cycle rates are an indication of a possible under-supply.

The analysis function identifies excessive as well as too low cycle rates on the basis of the datapoint operating message of the analysed component. It provides interpretations of the current cycle behavior and recommendations on how to improve it. The recommended measures include adaptions of control parameter, simple structural measures, as well as instructions for diagnosis and adjustment of the cause of sub-optimal cycle behavior. Additionally to the automated generation of interpretations and recommendations, the algorithm offers a set of KPIs. They provide deeper insides in the cycle behavior of the analysed component.

Results

# Qualitative warning level

Enum representations of traffic light colors providing a quick overview over the operation quality regarding cycle rates.

 Enum Color 0 green 1 yellow 2 red

# Interpretations

Interpretation text of the cycle rates of the analysed component in regard to its overall operation time.

# Recommendations

Recommendation texts on how to improve cycle rates of the analysed component including adjustment of control parameter as well as recommendations on how to investigate the root cause for unpleasant cycle behavior.

# KPIs

Providing deeper insights to the setpoint compliance. KPIs support human reasoning.

## Operating time

Operating times KPIs provide information on the total time of operation of the analysed component during the analysed time span.

 KPI Identifier Description Value Range Unit operating time Total time of operation 0 to inf h operating time.relative Total time of operation divided by total time span 0 to 100 %

## Start

The Start KPI is the count of starts of the analysed component during the analysed time span.

 KPI Identifier Description Value Range Unit starts Count of starts 0 to inf count

## Closed operating cycle

A closed operating cycle is defined as period of time between a start $n_i$of the component an the next start $n_{i+1}$. The KPI closed operating cycles represents the number of cycles observed during the analysed time span used to determine the operating cycle KPIs (cycle times, duty times, switch-off times).

 KPI Identifier Description Value Range Unit closed operating cycles Count of closed operating cycles 0 to inf count

## Cycle times

Cycle time KPIs evaluate the cycle times of the closed cycles observed during the analysed time span. The mean, time-weighted average, minimum, and maximum cycle period are determined. In case there were no closed operating cycles observed during the analysed time span, none of the KPI variables are returned on API call.

 KPI Identifier Description Value Range Unit cycle times.median Median of cycle periods 0 to inf h cycle times.average Time-weighted average of cycle periods 0 to inf h cycle times.maximum Longest cycle period 0 to inf h cycle times.minimum Shortest cycle period 0 to inf h

## Duty times

Duty time KPIs evaluate the duty times of the closed cycles observed during the analysed time span. Duty time is defined as the time of component operation in a closed cycle. The mean, time-weighted average, minimum, and maximum duty period are determined. In case there were no closed operating cycles observed during the analysed time span, none of the KPI variables are returned on API call.

 KPI Identifier Description Value Range Unit duty times.median Median of duty periods 0 to inf h duty times.average Time-weighted average of duty periods 0 to inf h duty times.maximum Longest duty period 0 to inf h duty times.minimum Shortest duty period 0 to inf h

## Switch-off times

Switch-off time KPIs evaluate the shutdown times of the closed cycles observed during the analysed time span. Switch-off time is defined as the time of component shutdown in a closed cycle. The mean, time-weighted average, minimum, and maximum switch-off period are determined. In case there were no closed operating cycles observed during the analysed time span, none of the KPI variables are returned on API call.

 KPI Identifier Description Value Range Unit switch-off times.median Median of switch-off periods 0 to inf h switch-off times.average Time-weighted average of switch-off periods 0 to inf h switch-off times.maximum Longest switch-off period 0 to inf h switch-off times.minimum Shortest switch-off period 0 to inf h
Example

The Operating Cycle Analysis was applied to a real test bench, the heat pump of the E.ON Energy Research Center, RWTH Aachen University. Thus, a heat pump component model was instanced and the respective datapoint mapped to the pin operating message. Figure 1 shows the time series recorded for an exemplary period of 6 hours on a winter day.

The short shut-down times between periods of duty are conspicuous. This is a hint for unnecessary frequent start and stop processes of the heat pump, leading not only to energy losses but also to high wear and tear of the heat pumps scroll compressor.

In order to apply the Operating Cycle Analysis for the instanced and mapped heat pump, we defined an analysis config via the aedifion API. Executing the Operating Cycle Analysis for the same period of time as figure 1 returns following results:

Qualitative warning level: red (as in traffic light signal color)

Interpretation: Highly increased number of start procedures/operation cycles. A reduction of the starting procedures or a more continuous operation is recommended.

Recommendations:

• Check whether the system flow temperature runs into the safety limiter and causes safety shutdowns.

• Check whether the heat requirement is covered too quickly by the producer. If yes, check the modulation operation of the system and, if available, the integration of the buffer tank and the interconnected operation of several generation systems.

• Check the installation of a power choke. By reducing the electrical power consumed, the amount of heat provided can be reduced and thus the effects of an oversized plant. Pay attention to the manufacturer's specifications as to whether the installation of a choke is technically possible.

KPIs:

 KPI Value Unit operating time 4.31 h operating time.relative 71.8 % starts 16 count closed operating cycles 15 count cycle times.median 0.36 h cycle times.average 0.369 h cycle times.maximum 0.41 h cycle times.minimum 0.34 h duty times.median 0.254 h duty times.average 0.264 h duty times.maximum 0.304 h duty times.minimum 0.234 h switch-off times.median 0.105 h switch-off times.average 0.106 h switch-off times.maximum 0.106 h switch-off times.minimum 0.105 h

The automated interpretation confirms our visual analysis of the time series shown in figure 1, summed up by the qualitative warning level "red". The recommendations provide further instruction on how to isolate and fix the cause for the increased number of start and stop processes.

Further, the result offers an advanced set of KPIs, providing additional insights into the cycle behavior of the heat pump. They support human reasoning for case-by-case analysis. For example, the similarity of switch-off times per cycle are peculiar. Since a coincidence would be surprising for such a dynamically operated plant, one could reason, that the heat pump is permanently requested by the higher level controller, but runs into a condition causing a component shutdown for a predefined period of time. Deeper human investigation of the condition is possible via data visualization of aedifion.io and the combination to insights of other analysis functions.

Components

# Pins

• operating message

# Components

## ​Fan​

Results are limited to KPIs.

Application

# Recommended Time Span

## 1 - 3 days

• Mind weekends

# Recommended Repetition

## Every month

• Cycle rates have a strong seasonal effect

• Frequent repetition allows to identify operational bad points

# Outdoor Temperature Sensor Analysis

This Analysis detects if a sensor is influenced by sun radiation or if the sensor has an offset to reference weather data from a different source.

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# Value

• Lower operating costs

• Make sure your building automation systems can work with reliable information about outside conditions

• Make sure your sensor is working correctly

# Recommended for component types

• Weather Station

Description

HVAC and heating systems of buildings are controlled with the help of sensors. The controller of these systems can't verify the correctness of the measured values and will control in accordance with the measured values.

Sensors are calibrated by the manufacturer or installation technician and will deliver a reading that is accurate to the required accuracy class. With an increasing operating time of the sensor, readings will shift and errors will increase compared to the moment of installation. Additionally to these sensor inherent errors, there can also be errors introduced that originate from the surrounding area the sensor is placed. Latter errors can be significantly higher than the sensor inherent errors because there is no accurate measure for them.

Therefore the placement of sensors should be well-considered. An important measurement for HVAC and heating systems is the outside air temperature, which helps the controller decide how much heating or cooling is required to deliver a comfortable indoor climate. Errors during the measurement of the outside air temperature directly correspond to an over or undersupply of the building and can lead to poor user comfort and a waste of energy.

To measure the outside air temperature the sensor must be placed outside of the building and often enough the placement is sub-optimal and sunlight can reach the sensor housing which results in a temperature reading that is higher than the actual outside air temperature.

This Analysis will compare the outside air temperature measurement of the sensor to measurement data from a different source (f.e. weather service) for each observation to determine if the sensor shows any discrepancies.

Results

# KPIs

 KPI Identifier Value Range Unit radiation influenced.relative 0 to 100 % radiation influenced.total 0 - inf days

radiation influenced.total is equal to the number of days that show one or more hours of radiation influence.

radiation influenced.relative is a ratio between radiation influenced.total and the observed period expressed in percent.

Example

In general you can expect a short demonstration on how we applied the analysis during our development and which results we got from our test bench.

Components

• temperature

# Attributes

• latitude

• latitude position [deg] of the examined component

• longitude

• longitude position [deg] of the examined component

Application

# Recommended Time Span

• Try to find an assessment period of several weeks or month to get a good chance of detecting influenced days

• time spans around summertime are more likely to have days with a lot of sunshine and thus increase the chance of detecting these days

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# Value

• energy-efficient operation of thermal control loops

• energy savings that lead to reduced costs

# Recommended for component types

• thermal control loop

Description

The Temperature Spread Analysis assesses the difference between two temperature pins while an operating message is active.

A thermal control loop is a typical case where outlet and return temperature are different and a temperature spread that is higher indicates a good utilization of this loop. A lower temperature spread suggests a reduced energy demand of the connected consumers and thus should lead to a reduction of volume flow caused by a reduced pump speed that saves electricity.

Results

# KPIs

 KPI Identifier Value Range Unit temperature spread.average - °C temperature spread.minimum - °C temperature spread.maximum - °C

This corresponds to the average temperature spread across the period of the analysis.

This corresponds to the smallest temperature spread across the period of the analysis.

This corresponds to the largest temperature spread across the period of the analysis.

Example

In general you can expect a short demonstration on how we applied the analysis during our development and which results we got from our test bench.

Components

# Pins

• outlet temperature

• return temperature

• operating message

Application

# Heating Curve Analysis

Documentation currently under construction 🚧

# Schedule Analysis

Documentation currently under construction 🚧

# Control Loop Oscillation Analysis

Documentation currently under construction 🚧

# Heat Flux Analysis

Documentation currently under construction 🚧

# Information

The library of analytics functions is constantly expanded. If you are missing an analytics function, wish to implement your own functions, or want us to implement it for you, feel free to contact us.