Analytics

Detailed specification of analytics functions, their benefits, and application.

Introduction

Analytics pursues only one goal: Guide technicians and building users to improve the operational performance of buildings and energy systems, while the benefits of improved operational performance are multilateral:

  • Higher comfort, well being and therefore the performance of people in buildings.

  • Higher energy efficiency providing comfort and energy services.

  • Lower effort maintaining and servicing complex technical facilities.

aedifion.io and any add-ons do not include energy consulting. All data calculated, analysed and/or displayed by aedifion.io and any add-ons, especially setting values (e.g. room temperature) or the mappings of data points, are non-binding recommendations that have to be carefully checked by the user's qualified personnel in each individual case before implementation ("human-in-the-loop principle").

How to read the docs?

Each analysis function documentation starts with a short description. Specifications for the application of the analysis are ordered in tabs

Summary

This tab summarizes the value offered by the analysis function, the component types the analysis is recommended for, and the conditions checked by the analysis.

Example

In general, you can expect a short case study on how the analysis function was applied during development or a test bench.

Results

Results of analytics functions are structured to deliver simple to navigate insights and fast to apply measures on how to improve operational performance.

Therefore, each result regardless of the analytics function includes

  • one qualitative warning level, aka. traffic light color,

  • one interpretation,

  • zero to n recommendations,

  • zero to n KPIs, and

  • zero to n timeseries.

These categories are explained below. While the warning level, interpretation, and recommendation are specified for all analysis functions equally, KPIs and timeseries differ between each analysis function.

Warning level

The warning level represents the urgency of looking into the analyzed condition and taking action to improve it. It can have one of these traffic light states, but not every analysis makes use of the full spectrum:

Red: Suboptimal performance identified. It can be expected that either improving the identified condition will have a strong effect on the performance or the effort to realize the optimization is moderate compared to its benefit.

Yellow: Suboptimal performance identified. The effort to optimize might consume its benefit. To reduce the effort, implement the measure with the maintenance work that is required anyway. Observation of the analyzed condition is recommended.

Green: Performance is satisfactory. No action recommended.

Interpretation

The interpretation delivers a summary over the observed performance and state of the condition analyzed. In general it describes either a symptom of a suboptimal operation or condition that could be found or not.

In the engineering vocabulary of Fault Detection and Diagnosis (FDD), the interpretation represents Fault Detection.

Recommendation

Recommendations is a list of 0 (for sufficient operational performance) to n measurements on how to correct the reason for the detected symptom for suboptimal operation. Either by providing recommendations on how to correct the source of the symptom itself or on how to narrow down its cause.

KPIs and timeseries

KPIs and timeseries offer insights and transparency. They enable reporting and manual investigation of the operational behavior the component or system analyzed. KPIs and timeseries are highly individual for each function and are explained in the respective specification of each analysis function in Results.

Components

The Components tab contains the API identifier and information of

  • the components the analysis function is available for,

  • the pins of the components which need to be mapped, and

  • the attributes of the component required.

Application

The Application tab provides information on the application of the analysis function.

  • Recommended time span: Most of the analysis functions have a sweet spot for the amount of historical data required to derive accurate results.

  • Recommended repetition: Components of building energy systems are subject to seasonal effects and wear out. Follow the recommended repetition to limit the amount of analysis to the required ones without risking blind spots in continuous monitoring.

Alarm State Analysis

The Alarm State Analysis assesses the occurrences and duration of alarm messages of a component. It is particularly useful for notifying the user when alarm messages have been overseen, as it summarizes the alarm messages over a given time period. Additionally, the Alarm State Analysis considers the most recent alarm message to determine whether the error has been resolved. While this analysis can be used for all alarm messages, it is most suited to critical alarm messages.

Summary
Example
Results
Components
Application
Summary

Value

  • Avoids alarm messages being overlooked

  • Identifies faulty components

  • Can reduce component wear-and-tear

  • Can increase energy efficiency

Recommended for components

Any component or subsystem which could have an alarm or error message such as:

  • Fans

  • Heat pumps

  • Thermal control loops

Checked conditions

  • Last state of alarm message

  • Relative duration of alarm message

  • Total duration of alarm messages

  • Total occurrences of alarm message

Example

The Alarm State Analysis was performed on a component for February 2020. The error message is active at the beginning of the time period and then about twice a week after that.

Figure 1: Component error message for the month of February 2020

The analysis returns a red warning message to indicate that the error message over the time period is suboptimal. This is because the error message is active for a significant percentage of the total time.

KPI

Value

Unit

alarm message.last observation

Inactive

binary

alarm message.relative

25.4

%

alarm message.duration

164

h

alarm message.count

12

count

Results

Signal colors

Signal color

Available

Info

red

Yes

The occurrences or alarm message duration is very high.

yellow

Yes

The occurrences or alarm message duration are acceptable.

green

Yes

The occurrences and alarm message duration are insignificant.

Available

Info

Yes

Either the operational rule checks of the analysis were tested positive or not

Interpretations

Available

Info

Yes

Either the operational rule checks of the analysis were tested positive or not.

Recommendations

Available

Info

Yes

Check component for physical damage and consider changing component setting.

KPIs

Summary of alarm messages

KPI Identifier

Description

Value Range

Unit

alarm message.last observation

Last available alarm message.

Active, Inactive

binary

alarm message.relative

Time of active alarm message as a percentage of total time.

0 to 100

%

alarm message.duration

Total time of active alarm message.

0 to inf

h

alarm message.count

Occurrences of alarm message.

0 to inf

count

Components

fan

Pin

Required

Mapping info

alarm message

yes

heat pump

Pin

Required

Mapping info

alarm message

yes

Pin

Required

Mapping info

alarm message

yes

Application

Recommended Time Span

1 month

Recommended Repetition

Every month

  • After changes of operational modes, e.g. transfers to heating mode

  • After changes in the control system

  • After maintenance of replacements

Control Loop Oscillation Analysis

The Control Loop Oscillation Analysis checks the process value of a control loop for oscillation. Oscillating process values are an indicator for suboptimal parameterization or structural dimensioning of the control loop.

Summary
Example
Results
Components
Application
Summary

Value

  • Increase lifetime of valve, dampers and adjacent components

  • Avoid spontaneous failures

  • Reduce energy consumption

  • Reduce noise pollution

Recommended for components

Any liquid media supply system, such as

  • thermal control loop with 2-way valve and pump

Checked conditions

  • Process value of control loop is oscillating

  • Process value of control loop is not or to a negligible degree oscillating

  • Condition checks on times of components operation

Example

For this example we analyzed the temperature control loop of a supply air volume flow, which provides fresh air and heating to a large sales room. Figure 1 shows a plot of the process value of the control loop, the outlet temperature. The plot shows a oscillation of the outlet temperature during periods of operation.

Figure 1: Oscillating processes value during operation

Figure 2 is a zoom of figure 1 to analyze the oscillation more into detail. The trajectory of the process value is common for control loops oscillating at medium frequency.

Figure 2: Oscillating process value during operation in detail

The Control Loop Analysis evaluated this oscillation as significant, signal color yellow, and derived recommendation on how to adjust controller parameters for a smoother operation.

Results

Signal colors

Signal color

Available

Info

red

No

Red as a signal for a low cost measure with high impact on the building operation will not be provided.

yellow

Yes

An oscillating control loop is a symptom for suboptimal control parameters or component design. Investing the extra effort to identify the root cause and fixing it is strongly recommended.

green

Yes

No or only slight, in respect to usual tolerances in buildings, negligible oscillation

Interpretations

Available

Info

Yes

Either the operational rule checks of the analysis were tested positive or not

Recommendations

Available

Info

Yes

Recommendations on how to smooth the control loop oscillation. No recommendation, if oscillation is negligible

Components

Pin

Required

Mapping info

operating message

no

Strongly recommended

Default: Always on

outlet temperature

yes

The outlet temperature is the process value of a thermal control loop

Application

Recommended Time Span

1 day to 1 week

Recommended Repetition

Weekly

  • After changes of operational modes, e.g. transfers to heating mode

  • After changes in the control system

  • After maintenance or replacements

Dew Point Alert Analysis

Building automation systems often have dew point alert messages which identify the possibility of unwanted condensation taking place in rooms. If the dew point alert message is active for any amount of time during the period of analysis, a recommendation is made to the user since rooms condensation in rooms can be damaging. Furthermore, if the temperature and relative humidity of the room are known, the DewPointAlertAnalysis calculates the risk of condensation and takes these into account in the evaluation. The DewPointAlertAnalysis is recommended for any room with an existing dew point alert signal or with temperature and relative humidity sensors.

Summary
Example
Results
Components
Application
Summary

Value

  • Avoids damage to rooms due to condensation

Recommended for components

  • Rooms

Checked conditions

  • Duration of dew point alert signal

  • Duration in which the room temperature is between 2 K and 4 K above the dew point temperature

  • Duration in which the room temperature is within 2 K of the dew point temperature

Example

The Dew Point Alert Analysis was performed on a room for a week in February 2020. For this particular room, a dew point alert message is available but no temperature and relative humidity data. As is shown in figure 1, the dew point alert signal is only active for a very short amount of time during the week.

Figure 1: Dew point alert for one week in February 2020

The analysis returns a red warning message to indicate that the dew point alert was active during some of the time period. This suggests that the condensation may have formed in the room. Note that only 'dew point alert' KPIs are generated since no temperature and humidity data are available in this example.

KPI

Value

Unit

dew point alert message.relative

1.69

%

dew point alert message.duration

2.83

h

Results

Signal colors

Signal color

Available

Info

red

Yes

Dew point alert message is active for some time or the temperature and humidity show a high chance of condensation.

yellow

Yes

There is a moderate chance of condensation taking place in the room.

green

Yes

Dew point alert message is not active during analysis period. No risk of condensation.

Interpretations

Available

Info

Yes

Either the operational rule checks of the analysis were tested positive or not.

Recommendations

Available

Info

Yes

Check room for condensation and mold.

KPIs

The KPIs which are generated by this analysis depend on the information available in the analysis. The 'dew point alert message' KPI's are generated if a dew point alert message is available. The condensation risk KPIs are generated using room temperature and relative humidity.

The condensation risk is evaluated as moderate if the room temperature is between 2 K and 4 K above the dew point temperature. A high condensation risk is when the room temperature is within 2 K of the dew point temperature.

Dew point alert

KPI Identifier

Description

Value Range

Unit

dew point alert message.relative

Time of active dew point alert message as a percentage of total time.

0 to 100

%

dew point alert message.duration

Total time of active dew point alert message.

0 to inf

h

condensation risk moderate.relative

Time of moderate condensation risk as a percentage of total time.

0 to 100

%

condensation risk moderate.duration

Total time of moderate condensation risk.

0 to inf

h

condensation risk high.relative

Time of high condensation risk as a percentage of total time.

0 to 100

%

condensation risk high.duration

Total time of high condensation risk.

0 to inf

h

Components

room

Pin

Required

Mapping info

dew point alert message

no

The dew point alert message can be used as the only pin or in combination with temperature and humidity.

temperature

no

If the temperature is mapped, humidity must also be mapped. Can be used in combination with dew point alert message.

humidity

no

If humidity is mapped, the temperature must also be mapped. Can be used in combination with dew point alert message.

Application

Recommended Time Span

1 week to 1 month

Recommended Repetition

Every month

  • After changes of operational modes, e.g. transfers to heating mode

  • After changes in the control system

  • After maintenance and replacement

Fan Speed Analysis

The Fan Speed Analysis evaluates whether a fan is controlled, based on its fan speed. This helps to identify problems with the fan control and ensures that fans are implemented more energy efficiently.

Summary
Example
Results
Components
Application
Summary

Value

  • Detect AHU fans that are not controlled

  • Reduce costs through better fan speed control

Recommended for components

  • Fan

Checked conditions

  • Stationary fan speed

Example

In this example we look at a Fan Speed Analysis of the historic 7 day fan speed. While the Operating Message (grey in the plot below) show the times when the fan was operated, the fan speed (blue in the plot below) corresponds to the speed or load setting of the fan.

From the analysis results we can see that the fan was operated for 6 hours out of the 168 hours of the week or 3.57 % of the week. Additionally we get statistics of the fan speed, f.e. the fan was operated at an average of 40 % load.

This corresponds to a static fan speed setting that is currently not controlled. To improve energy efficiency and thermal comfort you can consider different control strategies outlined in the recommendations.

KPI - Statistics

KPI

Value

Unit

operating time

6

h

operating time.relative

3.57

%

speed.maximum

40

%

speed.minimum

40

%

speed.mean

40

%

speed.median

40

%

Results

Signal colors

Signal color

Available

Info

red

No

yellow

Yes

Fan speed is not controlled

green

Yes

Fan speed is controlled

Interpretations

Available

Info

Yes

Information about the fan speed

Recommendations

Available

Info

Yes

Recommendations to look into the different control options for this fan to save energy.

KPIs

Statistics

statistics for "speed" will be calculated for all measured values that are not 0 %

KPI Identifier

Description

Value Range

Unit

operating time

Total time of operation

0 to inf

h

operating time.relative

Time of operation in relation to analysis period

0 to 100

%

speed.maximum

Largest observation recorded for fan speed during analysis period

0 to 100

%

speed.minimum

Smallest observation recorded for fan speed during analysis period

0 to 100

%

speed.mean

Time-weighted average of fan speed

0 to 100

%

speed.median

Time-weighted median of fan speed

0 to 100

%

Components

fan

Pin

Required

Mapping info

operating message

No

​​

speed

Yes

Use this pin to connect the datapoint that reflects fan speed settings from 0 - 100 % load

Application

Recommended Time Span

1 week

Recommended Repetition

Every month

Filter Servicing Analysis

The Filter Servicing Analysis predicts when a filter is due to be serviced or replaced, based on filter contamination or the pressure difference over the filter. This ensures that filters always function optimally and maintained or replaced as required.

Summary
Example
Results
Components
Application
Summary

Value

  • Ensures filter is serviced when required

  • Improves energy efficiency

Recommended for components

  • Filter

Checked conditions

  • Filter contamination

  • Expected time till filter service or replacement

Example

In this example, the filter contamination of an exhaust air filter in a combined heat and power plant was analyzed for a period of four months. As can be seen in figure 1, the filter contamination gradually increases over the analyzed period.

Figure 1: Filter contamination over a four month period

The signal analysis returns a green signal color since there is a significant amount of time before the filter is fully contaminated.

KPI

Value

Unit

days until filter service

35

d

expected date of filter service

2020-05-20

date

filter contamination

79.3

%

Results

Signal colors

Signal color

Available

Info

red

Yes

The filter is fully contaminated and should be serviced soon.

yellow

Yes

The filter is almost contaminated, a filter service should scheduled.

green

Yes

The filter is in a good condition and does not need to be serviced.

Interpretations

Available

Info

Yes

Information regarding the filter condition and whether the filter needs to be serviced.

Recommendations

Available

Info

Yes

Make necessary arrangements for filter to be serviced. No recommendation if the filter does not need servicing within two weeks and the filter contamination is below 95%.

KPIs

KPI Identifier

Description

Value Range

Unit

days until filter service

Number of days until filter expected filter service.

0 to inf

d

expected date of filter service

Date on which filter is expected to require a service (format: YYYY-MM-DD)

date

filter contamination

Relative extent to which filter is contaminated.

0 to 100

%

Components

filter

Pin

Required

Mapping info

filter contamination

No

Either filter contamination (preferred) or pressure difference must be mapped. If both pins are mapped, filter contamination is used.

pressure difference

No

Either filter contamination (preferred) or pressure difference must be mapped. If both pins are mapped, filter contamination is used.

Attribute

Required

Mapping info

filter_class

no

Default: F9

initial_pressure_difference

no

Default: initial pressure difference of filter class (50 Pa for filter class F9).

final_pressure_difference

no

Default: final pressure difference of filter class (300 Pa for filter class F9). Setting this attribute is highly recommended.

Application

Recommended Time Span

1 month to 6 months

Recommended Repetition

Twice a month

Humidity Conditioning Analysis

The Humidity Conditioning Analysis compares the outside air humidy with the actual supply air humidity of the a Air Handling Unit (AHU).

This analysis does not take into account air recirculation and humidity recovery modes. Make sure that the system is operated without such operational modes.

Summary
Example
Results
Components
Application
Summary

Value

  • Detect operating conditions of AHUs that are not appropriate to the outside air conditions

  • Avoids unnecessary changes in humidity, which cost a lot of energy

  • Verifies sufficient supply air humidity

Recommended for components

  • Air handling units with humidity conditioning

Checked conditions

  • Compare actual operated hours with humidification, dehumidification and no operation with the corresponding expected hours

Example

This example shows a week of analysis for a summer scenario in July. The AHU is operating throughout the week. Relative humidity conditions are displayed in red and orange, green and blue are temperature conditions and brown and purple are the water load conditions.

The analysis uses two positions, intake (outside conditions) and outlet (supply conditions) to calculate water loads. A difference of these water loads corresponds to the pink line at the bottom. The operating hours will now be divided into three categories. Hours of humidification, hours of dehumidification and hours of neither humidification nor dehumidification. These values are then compared to the expected hours in these categories derived from outside conditions. The total hours of correct operation (according to the expectation) and then evaluated for a recommendation.

KPI

Value

Unit

operating time

168

h

operating time.relative

100

%

humidification detected

135

h

dehumidification detected

18

h

humidification necessary

0

h

dehumidification necessary

49

h

humidification missing

0

h

dehumidification missing

49

h

humidification unnecessary

135

h

dehumidification unnecessary

18

h

total hours savings possible.relative

91.1

%

total hours increase air quality.relative

92.3

%

Results

Signal colors

Signal color

Available

Info

red

No

yellow

Yes

green

Yes

The AHU operates in accordance to the expected operating conditions.

Interpretations

Available

Info

Yes

Either the expected operating conditions are met by the operation of the AHU or the operating conditions do not fit.

Recommendations

Available

Info

Yes

Recommendations regarding which operating mode (humidification, dehumidification) should be looked into to change the operating modes of the AHU.

KPIs

KPI Identifier

Description

Value Range

Unit

operating time

Total time of operation

0 to inf

h

operating time.relative

Total time component was operated compared to analysis period

0 to 100

%

Operating Conditions

KPI Identifier

Description

Value Range

Unit

humidification detected

The amount of time the component operates in humidification mode according to inflow / outflow analysis

0 to inf

h

dehumidification detected

The amount of time the component operates in dehumidification mode according to inflow / outflow analysis

0 to inf

h

humidification necessary

The amount of time the component should operate in humidification mode according to outside air conditions

0 to inf

h

dehumidification necessary

The amount of time the component should operate in dehumidification mode according to outside air conditions

0 to inf

h

humidification missing

The amount of time the component did not operate in humidification mode but should

0 to inf

h

dehumidification missing

The amount of time the component did not operate in dehumidification mode but should

0 to inf

h

humidification unnecessary

The amount of time the component operated in humidification mode but should not

0 to inf

h

dehumidification unnecessary

The amount of time the component operated in dehumidification mode but should not

0 to inf

h

total hours savings possible.relative

Percentage of time de- or humidification can be switched off according to outside air conditions relative to operating time

0 to 100

%

total hours increase air quality.relative

Percentage of time de- or humidification should be switched on according to outside air conditons relative to operating time

0 to 100

%

Components

Pin

Required

Mapping info

supply air temperature

yes

conditioned air at supply side exit of AHU

supply air relative humidity

yes

conditioned air at supply side exit of AHU

outside air temperature

yes

intake air conditions

outside air relative humidity

yes

intake air conditions

operating message

no

Mapping of operating message is strongly recommended.

Default: Always operating

Application

Recommended Time Span

1 week

Recommended Repetition

Every month

  • After changes of operational modes

  • After changes in the control system

Operating Cycle Analysis

The Operating Cycle Analysis identifies excessive start and stop processes which lead to energy losses, energy consumption peaks due to higher energy consumption on plant start, and higher wear and tear of the component compared to 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. Further, the algorithm takes low cycle rates as an indication of a possible under-supply of the adjacent systems.

Summary
Example
Results
Components
Application
Summary

Value

  • Lower operating costs

  • Higher energy efficiency

  • Peak energy consumption reduction

  • Longer equipment and component lifetimes

  • Smoother system integration

Recommended for components

Energy conversion plants and components with high start-up energy consumption or wear, such as

  • Heat pump

  • Combined heat and power

  • Boiler

  • Fan

Checked conditions

  • Short cycling of component operation, evaluated component-specific

  • Long cycling of component operation, evaluated component-specific

  • Expected cycling of component operation, evaluated component-specific

  • Condition checks on times of components operation

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.

Figure 1: Operating message and cycle behavior of heat pump

Short shut-down times between periods of duty indicate excessive start and stop processes of the heat pump, leading not only to energy losses and electricity consumption peaks but also to increased wear and tear of the heat pumps compressor.

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 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.

Results

Signal colors

Signal color

Available

Info

red

No

The analysis identifies the symptom and recommends measures to investigate the root cause of short cycling respectively long cycling. Red as a signal for a low cost measure with high impact on the building operation will not be provided.

yellow

Yes

Unwanted cycling rates are a strong symptom for suboptimal control and system performance. Investing the extra effort to identify the root cause and fixing it is strongly recommended.

green

Yes

Sufficient cycle rates in respect to usual operation in buildings

Interpretations

Available

Info

Yes

Either the operational rule checks of the analysis were tested positive or not

Recommendations

Available

Info

Yes

Recommendations on how to investigate the root cause of an unwanted cycle rate. No recommendation, if cycle rate is sufficient

KPIs

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 nin_iof the component an the next start ni+1n_{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.mean

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.mean

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.mean

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

Components

boiler

Pin

Required

Mapping info

operating message

yes

Pin

Required

Mapping info

operating message

yes

fan

Pin

Required

Mapping info

operating message

yes

heat pump

Pin

Required

Mapping info

operating message

yes

Pin

Required

Mapping info

operating message

no

Mapping of either operating message (preferred) or pump operating message is mandatory. If both pins are mapped, operating message is used

pump operating message

no

Mapping of either operating message (preferred) or pump operating message is mandatory. If both pins are mapped, operating message is used

Application

Recommended Time Span

1 day to 1 week

Recommended Repetition

Every month

  • Cycle rates have a strong seasonal effect

  • Frequent repetition allows to identify operational bad points

  • After changes of operational modes, e.g. transfers to heating mode

  • After changes in the control system

  • After maintenance or replacements

Reduced Load Analysis

The Reduced Load Analysis identifies the presence of a reduced load mode based on temperature setpoints of the system under consideration. The temperature spread of the system is determined. A reduced load mode offers the possibility of operational cost and energy reductions. Additionally, a comparison with a user-defined schedule reveals times when the component could be in a reduced load operating mode.

Summary
Example
Results
Components
Application
Summary

Value

  • Lower operating costs

  • Lower energy consumption

Recommended for components

Heat and cold distribution systems, energy conversion plants and indoor areas, such as

  • Heating loops

  • Cooling loops

  • Boilers

  • Office rooms

  • Schooling rooms

Checked conditions

  • Existence of a load reduction period, e.g. night-time temperature reduction for heating

  • Condition checks on times of components operation

  • Estimation of times when the load can be reduced according to a user-defined schedule

Example

This example shows the results of a Reduced Load Analysis performed on a heating circuit. Figure 1 shows the temperature setpoint. The setpoint changes from operation at normal load to reduced load according to the schedule in the table below. The detected temperature level shift corresponds to 10 Kelvin.

Figure 1: outlet temperature setpoint [°C]

Schedule

Day

Time

Mon

05:00 - 18:00

Tue

05:00 - 18:00

Wed

05:00 - 18:00

Thu

05:00 - 18:00

Fri

05:00 - 18:00

Sat

07:00 - 14:00

Sun

07:00 - 14:00

KPI

Value

Unit

reduced load operation

Yes

binary

temperature level shift

10

°C

operating time

62.4

h

operating time.normal load.reducible

1.77

h

operating time.normal load.reducible.relative

2.84

%

operating time.normal load.scheduled

60.6

h

Results

Signal colors

Signal color

Available

Info

red

Yes

No load reduction identified (applied for thermal control loop)

yellow

Yes

No load reduction identified (applied for any other component than thermal control loop)

green

Yes

Load reduction identified

Interpretations

Available

Info

Yes

Either the operational rule checks of the analysis were tested positive or not

Recommendations

Available

Info

Yes

Implementation hints for load reduction. No recommendation, in case of sufficient measurement quality

KPIs

Identification of reduced load mode

KPI Identifier

Description

Value Range

Unit

reduced load operation

Whether a reduced load mode was detected

No = no reduced load identified

Yes = reduced load identified

Yes, No

binary

Statistics of temperature level shift

KPI Identifier

Description

Value Range

Unit

temperature level shift

Difference between setpoint temperature levels at the time of load reduction

negative values = reduced temperature level for heating load reduction

positive values = raised temperature level for cooling load reduction

-inf to inf

°C

Schedule operating times

KPIs of this category analyse if the load reduction is in accordance to a schedule and if there are further savings by adjusting/implementing a load reduction schedule.

KPI Identifier

Description

Value Range

Unit

operating time

Total time of operation

0 to inf

h

operating time.normal load.reducible

Total time component was operated under normal load outside the reviewed schedule and therefor could be saved

0 to inf

h

operating time.normal load.reducible.relative

Percentage of reducible operating time under normal load relative to the total operating time

0 to 100

%

operating time.normal load.scheduled

Total time of operation under normal load that is scheduled

0 to inf

h

Timeseries

KPI Identifier

Description

Value Range

Unit

normal load.timeseries

Timeseries of operating mode

0 = reduced load operation

1 = normal operation

0 or 1

binary

Components

boiler

Pin

Required

Mapping info

outlet temperature setpoint

yes

Attribute

Required

Mapping info

custom_day_schedules

no

custom_holiday

no

preconditioning

no

regional_key

no

schedule

no

times for operation at normal load

schedule_timezone

no

Strongly recommended

Default: UTC

shutdown_flexibility

no

room

Pin

Required

Mapping info

temperature setpoint

yes

Attribute

Required

Mapping info

custom_day_schedules

no

custom_holiday

no

preconditioning

no

regional_key

no

schedule

no

times for operation at normal load

schedule_timezone

no

Strongly recommended

Default: UTC

shutdown_flexibility

no

Pin

Required

Mapping info

outlet temperature setpoint

yes

Attribute

Required

Mapping info

custom_day_schedules

no

custom_holiday

no

preconditioning

no

regional_key

no

schedule

no

times for operation at normal load

schedule_timezone

no

Strongly recommended

Default: UTC

shutdown_flexibility

no

Application

Recommended Time Span

1 day to 1 week

Recommended Repetition

Every 3 months

  • After changes of operational modes, e.g. transfers to heating mode

  • After changes in the control system

  • After maintenance or replacements

Sensor Outage Analysis

The Sensor Outage Analysis uses the time series data of the sensor to detect irregularities on observations. This implies manual overwriting of the sensor values, constant observations for expected volatile trajectories of the data points observations and also value plausibility checks by types of sensors.

Summary
Example
Results
Components
Application
Summary

Value

  • Confirm normal operation of sensors

  • Identify faulty measurement setups inside your building automation system

  • Detection of permanently manual overwritten sensors causing permanent manipulation of control loop

Recommended for components

Any component with sensors measuring physical quantities

Checked conditions

  • Measurements of a sensor lie within a reasonable range

  • Detects constant observation for sensors which expect volatile trajectories

Example

For this example we are looking at a temperature sensor for room air temperature, that is connected to a component "room". The KPIs are generated according the mapped pins. For this setup we mapped a datapoint to pin pin "temperature", thus the result contains the three KPIs listed below.

The room temperature is measured by the sensor with values above the plausibility limit of 40 °C. The KPI "pin.temperature.above high limit = 1" indicates that the measured values lie not within a reasonable range for room temperatures.

If any of the KPIs have the boolean value of 1, a faulty sensor is detected and the signal color "red" is returned to alarm. A detected fault can be caused by various reasons ranging from manually overwritten sensors over a faulty sensor to a wrong configured measurement system.

KPI

Value

Unit

pin.temperature.below low limit

No

binary

pin.temperature.above high limit

Yes

binary

pin.temperature.faulty

No

binary

Results

Signal colors

Signal color

Available

Info

red

Yes

One or more Sensors have to be checked

yellow

No

green

Yes

no faulty sensors detected

Interpretations

Available

Info

Yes

Detection of faulty of sensors or plausible observations

Recommendations

Available

Info

Yes

Recommendations to correct the reason of the sensor fault

KPIs

PIN_NAME refers to the actual pin on the component that the KPI belongs to.

Limit

KPI Identifier

Description

Value Range

Unit

pin.{PIN NAME}.below low limit

Time Series values of pin "PIN_NAME" below low limit

0 = observations in plausible range

1 = observations below lowest plausible value detected

Yes, No

binary

pin.{PIN NAME}.above high limit

Time Series values of pin "PIN_NAME" above high limit

0 = observations in plausible range

1 = observations above highest plausible value detected

Yes, No

binary

Sensor Fault

KPI Identifier

Description

Value Range

Unit

pin.{PIN NAME}.faulty

Sensor of pin "PIN_NAME" below low limit

Yes, No

binary

Components

boiler

Pin

Required

Mapping Info

inlet temperature

no

low limit = 1

high limit = 100

outlet temperature

no

low limit = 1

high limit = 100

combine heat and power

Pin

Required

Mapping info

inlet temperature

no

low limit = 1

high limit = 100

outlet temperature

no

low limit = 1

high limit = 100

heat meter

Pin

Required

Mapping Info

inlet temperature

no

low limit = 1

high limit = 100

outlet temperature

no

low limit = 1

high limit = 100

heat pump

Pin

Required

Mapping Info

condenser inlet temperature

no

low limit = -50

high limit = 100

condenser outlet temperature

no

low limit = -50

high limit = 100

evaporator inlet temperature

no

low limit = -50

high limit = 100

evaporator outlet temperature

no

low limit = -50

high limit = 100

room

Pin

Required

Mapping Info

temperature

no

low limit = 5

high limit = 40

humdity

no

low limit = 0

high limit = 100

thermal control loop

Pin

Required

Low Limit

inlet temperature

no

low limit = -50

high limit = 100

outlet temperature

no

low limit = -50

high limit = 100

inlet temperature recirculation

no

low limit = -50

high limit = 100

valve position

no

low limit = 0

high limit = 100

weather station

Pin

Required

Low Limit

temperature

no

low limit = -50

high limit = 50

Application

Recommended Time Span

1 week - several weeks

Recommended Repetition

Every Week

  • A Sensor fault can occur at any moment

Room Air Quality Analysis

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 the case of poor air quality, measures for improvement are recommended. Human performance is significantly influenced by air quality. In addition, the algorithm identifies calibration errors by physical plausibility checks.

Summary
Example
Results
Components
Application
Summary

Value

  • Higher occupant comfort, health, and performance

Recommended for components

  • Rooms

  • Occupied indoor areas

Checked conditions

  • Indoor CO2 concentration evaluation based on DIN EN 13776: 2007-09

  • Identification of higher room ventilation needs

  • Sensor calibration check by the plausibility of minimal measured concentration levels

  • Condition checks on times of components operation

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 room component model was instanced and the respective datapoints mapped to this component.

Figure 1: CO2 concentration trajectory for an average office day

In this scenario, figure 1 shows the timeseries recorded for an exemplary period of 12 hours on a working day in August. The CO2 concentration in the air remained between what is considered "good" and "medium" for most of the day. However, for about 7 percent of the period, air quality was poor, with a maximum CO2 concentration of 1463 ppm, so that a complete evaluation on that day indicates poor air quality. The results provide an advanced set of KPIs that provide quantitative insights into the air quality of the rooms and support the human reasoning for analysis. A number of suggestions for possible countermeasures are given, and further investigation of the root cause of air quality problems is possible through the aedifion front-end data visualization.

Results

Signal colors

Signal color

Available

Info

red

Yes

CO2 concentrations critical for human health

yellow

Yes

CO2 concentrations reducing human comfort, decisiveness, and performance or wrongly calibrated CO2 sensors

green

Yes

CO2 concentrations sufficient for high comfort

Interpretations

Available

Info

Yes

Either the operational rule checks of the analysis were tested positive or not

Recommendations

Available

Info

Yes

Recommendations to improve fresh air supply, if necessary or re-calibrate the sensor, if physically implausible measures are observed. No recommendation, in case of sufficient 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

Duration with “good“ indoor air quality

0 to inf

h

co2 duration.IDA2

Duration with “medium “ indoor air quality

0 to inf

h

co2 duration.IDA3

Duration with “moderate “ indoor air quality

0 to inf

h

co2 duration.IDA4

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

room

Pin

Required

Mapping info

co2

yes

operating message

no

Mapping of either presence (preferred) or operating message is strongly recommended. If both pins are mapped, pressence is used

Default: Always presence

presence

no

Mapping of either presence (preferred) or operating message is strongly recommended. If both pins are mapped, pressence is used

Default: Always presence

Application

Recommended Time Span

1 days to 1 week

  • Utilize on days with room occupation

Recommended Repetition

Every month

  • After changes of room occupation or usage

  • After changes of operational modes, e.g. transfers to heating mode

  • After changes in the control system of the ventilation systems

  • After maintenance or replacements in ventilation systems

Schedule Analysis

The Schedule Analysis is used to compare the actual occurred switch on/switch off times of the component with a schedule/timetable stored inside analytics. This analysis aims at identifying the amount of hours the component is active outside of the scheduled times. In addition to a one-time check, the analysis is suitable for permanent checks, e.g. to identify manual overwriting of the operating schedule. The analysis allows to respect holidays and exceptional day schedules.

Summary
Example
Results
Components
Application
Summary

Value

  • Lower operating times of HVAC components

  • Lower energy consumption

  • Lower maintenance costs due to less component operating time

Recommended for components

Any HVAC component or room whose usage follows a recurrent schedule, such as

  • Fans

  • Thermal control loops

  • Office rooms

  • Sales rooms

Checked conditions

  • Component operation outside a user defined schedule

  • Component operation during a user defined schedule

  • Condition checks on times of components operation

Example

This example shows a schedule analysis for a component "fan" connected to a supply fan operating message of a HVAC machine. The switch on/off times of the machine are shown as a blue line in figure 1, blue regions in the background correspond to the anticipated schedule.

Figure 1: Operating times of component and reference schedule

The following KPIs show that a reduction of ~9% of the total operating time is possible. With the help of the plot we can also see, that the times were we can reduce the operating time are distributed over the workdays of the week.

KPI

Value

Unit

operating time

74

h

operating time.reducible

6.94

h

operating time.reducible.relative

9.38

%

operating time.scheduled

67.1

h

savings.daily

1.53

€/d

Results

Signal colors

Signal color

Available

Info

red

Yes

Significant operation times outside of the parameterized schedule identified

yellow

Yes

Partial operation times outside of the parameterized schedule identified

green

Yes

Sufficient operation according to the parameterized schedule

Interpretations

Available

Info

Yes

Either the operational rule checks of the analysis were tested positive or not

Recommendations