# 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.

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 ​

# ​thermal control loop​

 Pin Required Mapping info alarm message yes ​
Application

# 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

• 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 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.

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

# ​thermal control loop​

 Pin Required Mapping info operating message no Strongly recommendedDefault: Always on outlet temperature yes The outlet temperature is the process value of a thermal control loop
Application

# Recommended Repetition

## Weekly

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

• After changes in the control system

• After maintenance or replacements

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

• 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.

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.

 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 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

• 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

# 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

• 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.

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

# 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

# ​humidity conditioner​

 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 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.

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 $n_i$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.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 ​

# ​combined heat and power​

 Pin Required Mapping info operating message yes ​

# ​fan​

 Pin Required Mapping info operating message yes ​

## ​heat pump​

 Pin Required Mapping info operating message yes ​

## ​thermal control loop​

 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 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

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.

### 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 detectedNo = no reduced load identifiedYes = 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 reductionnegative values = reduced temperature level for heating load reductionpositive 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 mode0 = reduced load operation1 = 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 recommendedDefault: 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 recommendedDefault: UTC shutdown_flexibility no ​

# ​thermal control loop​

 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 recommendedDefault: UTC shutdown_flexibility no ​
Application

# 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 limit0 = observations in plausible range1 = observations below lowest plausible value detected Yes, No binary pin.{PIN NAME}.above high limit Time Series values of pin "PIN_NAME" above high limit0 = observations in plausible range1 = 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 = 1high limit = 100 outlet temperature no low limit = 1high limit = 100

# combine heat and power

 Pin Required Mapping info inlet temperature no low limit = 1high limit = 100 outlet temperature no low limit = 1high limit = 100

# heat meter

 Pin Required Mapping Info inlet temperature no low limit = 1high limit = 100 outlet temperature no low limit = 1high limit = 100

# heat pump

 Pin Required Mapping Info condenser inlet temperature no low limit = -50high limit = 100 condenser outlet temperature no low limit = -50high limit = 100 evaporator inlet temperature no low limit = -50high limit = 100 evaporator outlet temperature no low limit = -50high limit = 100

# room

 Pin Required Mapping Info temperature no low limit = 5high limit = 40 humdity no low limit = 0high limit = 100

# thermal control loop

 Pin Required Low Limit inlet temperature no low limit = -50high limit = 100 outlet temperature no low limit = -50high limit = 100 inlet temperature recirculation no low limit = -50high limit = 100 valve position no low limit = 0high limit = 100

# weather station

 Pin Required Low Limit temperature no low limit = -50high limit = 50
Application

# 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.

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 usedDefault: Always presence presence no Mapping of either presence (preferred) or operating message is strongly recommended. If both pins are mapped, pressence is usedDefault: 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.

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