Algorithms¶

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.

Expect a short case-study on how the analysis function was applied during development or a test bench.

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. signal color,
• One interpretation,
• Zero to one summary KPI,
• Zero to n recommendations,
• Zero to n KPIs,
• Zero to n plots, and
• Zero to n timeseries.

These categories are explained below. While the warning level, interpretation, and recommendation are specified for all analysis functions equally, summary KPIs, KPIs, plots, and time series 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:

Interpretation

The interpretation delivers a summary of the observed performance and state of the condition analyzed. It describes either a symptom of a suboptimal operation or a condition that could be identified.

Recommendation

Recommendations are summarized in a list of zero (for sufficient operational performance) to n measurements on how to improve the operational performance. The recommendations either help by providing information on how to correct the source of the symptom itself or on how to narrow down to its root cause.

Plots

It is easy to understand complex data insights with the right visualization. Thus, many .analytics algorithms offer plotting recommendations including the type of plot, data, and labeling. This structured and well defined plotting data makes it easy to transport data insights.

Available plot types

Included data structures

Summary KPIs, KPIs, and time series

KPIs and time series offer insights and transparency, while the most important KPI is offered as summary KPI including its current value and KPI development compared to the previous analysis. They enable reporting and manual investigation of the operational behavior of the component or system analyzed. KPIs and time series are highly individual for each function and are explained in the respective specification of each analysis function in Results.

The Components tab has one tab per component the analysis function supports and shows what you need to map for it.

• Sub-components appear in analyses of the building that use several component types, such as meters. The Building tab then has one tab per type, and the text above these tabs says which ones the analysis needs.
• Cases list, for each check, which inputs it needs (Required) and which ones refine it. Each case runs on its own, so the analysis runs as soon as one case has all its required inputs. A few components return a result without any case; a note in their tab says so. The If missing column shows what happens without an input, for example a default value.
• Savings list the inputs the analysis needs on top of a case to calculate savings.
• Additional results list the inputs for optional results that are not savings, such as energy costs.
• Inputs of the type External do not come from a mapping, for example the project location or Weatherbit data.
• Pin Rankings are inputs the analysis can take from several pins or attributes, called candidates. The analysis uses the first mapped candidate in the ranking. If none is mapped, it uses the default in the last row, if the ranking has one. Select a pin ranking in a case table to jump to its candidates.

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 ensure the analysis is performed as often as necessary, without risking blind spots during the continuous monitoring of the system.

The Further information tab provides supplementary information about possible analysis results. Based on the respective and concise interpretation message, a technical explanation is given.

"Interpretation message"

Explanation

A detailed description of the analysis result.

Suggestion

A detailed description of the given recommendations.

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

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.

Signal colors

Interpretations

Recommendations

KPIs

Summary of alarm messages

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

Value

• ESG-Reporting and monitoring of CO₂ emissions
• Insights in CO₂ performance of the building
• CO₂ emission benchmarking
• Trace back CO₂ emissions to energy sources
• Additional weather normalization of the CO₂ emissions
• Prediction of yearly CO₂ emissions based on weather-normalized extrapolation

Recommended for components

Complete buildings or energy systems

Checked conditions

• CO₂ emissions of the building or system
• Benchmarking
• Emission by energy source

This example shows the results of an CO₂ Emission analysis displayed in the Asset Overview.

Figure 1: CO₂ emissions Asset Overview result overview

Above all cards the interpretation text of the result is highlighted with the signal color of the result. In the top left the current KPI and the monthly trend is shown. On the right side of that you will find the recommendations of this result as well as further information about the function.

The three plots below are:

1. The building benchmark

Figure 2: CO₂ emissions Asset Overview plot bullet graph

The current KPI is plotted inside a bullet graph between the limits of the signal color.

2. Compared to last months

Figure 3: CO₂ emissions Asset Overview plot month comparison

The plot shows the current KPI of the last three months, including the month of the analysis.

3. By energy source

Figure 4: CO₂ emissions Asset Overview plot energy source

The plot shows the distribution of CO₂ emissions by energy source.

Signal colors

Interpretations

Recommendations

KPIs

Summary KPI

Total CO2 emissions KPIs

The .weather normalized KPI-variants below correct for the weather actually experienced during the period. See Weather normalization for more information.

Yearly predicted CO2 emissions KPIs

The prediction is made by calculating and aggregating the yearly consumption of each relevant meter. For weather-dependent consumption, the yearly consumption is weather-normalized with heating/cooling degree hours (see Weather normalization). If a meter has less than one year of consumption data, the consumption is extrapolated to a full year.

CO2 emissions by energy source KPIs

The .weather normalized KPI-variants below correct for the weather actually experienced during the period. See Weather normalization for more information.

An energy source is reported once at least one meter of that source is mapped: a source whose meters measured no consumption reports a value of 0, while a source without mapped meters is omitted.

Timeseries

The timeseries is saved as a virtual datapoint. Every run of the analysis will add the summary KPI (current value) as an observation at the start of the analysis period to the timeseries.

Plots

Recommend Time Span

1 month

Recommended Repetition

Monthly

• Continuously monitor CO₂ emissions
• Track success of a decarbonization measure

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 the control loop is oscillating
• Process value of the control loop is not or to a negligible degree oscillating
• Condition checks on times of components operation

For this example, we analyzed the temperature control loop of a supply air volume flow, which provides fresh air and heating to a large salesroom. Figure 1 shows a plot of the process value of the control loop, the outlet temperature. The plot shows an 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 in more 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 and assigned it the signal color yellow. Recommendations are made on how to adjust controller parameters for a smoother operation.

Signal colors

Interpretations

Recommendations

Recommend 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

Value

• Better understanding of the system's operating modes
• Detecting of faulty operation modes
• Lower the energy costs of your system through the optimization of the detected operating modes

Recommended for components

• Air handling unit

Checked faulty conditions

• Air heating without full use of the heat recovery system
• Simultaneous heating and cooling
• Contradicting operating modes in downstream air heating or cooling units

The sequence analysis was applied to an air handling unit with an analysis period of one week. We can see that the unit has been operating in the faulty air heater mode for a very long time. This suggests that the heat recovery is not fully utilized before the air heater is switched on.

Connected sub-components:

• Supply fan
• Air heater
• Heat recovery system

Figure 1: Operating state of sub-components

Signal colors

Interpretations

Recommendations

KPIs

Faulty operating states

Normal operating states

Recommend Time Span

1 day to 1 week

• Analysis is useful for all days with system operation

Recommended Repetition

Every month

• 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

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 °C and 4 °C above the dew point temperature
• Duration in which the room temperature is within 2 °C of the dew point temperature

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 a portion 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.

Signal colors

Interpretations

Recommendations

KPIs

The KPIs which are generated by this analysis depend on the information available in the analysis. The "dew point alert message" KPIs 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

Recommend 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

Value

• Detect AHU fans that are not controlled
• Reduce costs through better fan speed control

Recommended for components

• Air handling unit
• Fan

Checked conditions

• Stationary fan speed

In this example, we look at a Dynamic Control analysis of the historic 7-day fan speed. While the operating message (grey in the plot below) shows 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.

Figure 1: Speed and operating message of a 7-day fan speed analysis

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, e.g., 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

Signal colors

Interpretations

Recommendations

KPIs

Statistics

Recommended Time Span

1 week

Recommended Repetition

Every month

Value

• Insights into the operating times of elevators over weekdays
• Insight into the total availability of elevators

Recommended for components

Buildings with elevators

Checked conditions for operating time calculation

Checked conditions for availability calculation

An analysis on a building with elevators is carried out for an analysis period of 3 weeks.

KPI - Statistics

Bar chart Average Elevators Operating Time

Signal colors

Interpretations

Recommendations

KPIs

Summary KPI

Plots

Recommend Time Span

1 week

Recommended Repetition

weekly

• Continuously monitor availability of elevators
• Track operating times of your elevators

Value

• ESG-Reporting and monitoring of energy consumption
• Insights into the energy consumption of the building
• Energy consumption benchmarking
• Trace back consumption to the energy source
• Additional weather normalization of the energy consumption
• Prediction of yearly energy consumption based on weather-normalized extrapolation

Recommended for components

Complete buildings or energy systems

Checked conditions

• Energy consumption of the building or system
• Benchmarking
• Energy consumption by energy source

This example shows the results of an Energy Consumption analysis displayed in the Asset Overview.

Figure 1: Energy Consumption Asset Overview result overview

Above all cards the interpretation text of the result is highlighted with the signal color of the result. In the top left the current KPI and the monthly trend is shown. On the right side of that you will find the recommendations of this result as well as further information about the function.

The three plots below are:

1. The building benchmark

Figure 2: Energy Consumption Asset Overview plot bullet graph

The current energy consumption is plotted inside a bullet graph between the limits of the signal color.

2. Compared to last months

Figure 3: Energy Consumption Asset Overview plot month comparison

The plot shows the energy consumption of the last three months, including the month of the analysis.

3. By energy source

Figure 4: Energy Consumption Asset Overview plot energy source

The plot shows the distribution of energy consumption by energy source.

Signal colors

Interpretations

Recommendations

KPIs

Summary KPI

Total energy consumption KPIs

The .weather normalized KPI-variants below correct for the weather actually experienced during the period. See Weather normalization for more information.

Yearly predicted energy consumption KPIs

The prediction is made by calculating and aggregating the yearly consumption of each relevant meter. For weather-dependent consumption, the yearly consumption is weather-normalized with heating/cooling degree hours (see Weather normalization). If a meter has less than one year of consumption data, the consumption is extrapolated to a full year.

Energy consumption by energy source KPIs

The .weather normalized KPI-variants below correct for the weather actually experienced during the period. See Weather normalization for more information.

An energy source is reported once at least one meter of that source is mapped: a source whose meters measured no consumption reports a value of 0, while a source without mapped meters is omitted.

Timeseries

The timeseries is saved as a virtual datapoint. Every run of the analysis will add the summary KPI (current value) as an observation at the start of the analysis period to the timeseries.

Plots

Recommend Time Span

1 month

Recommended Repetition

Monthly

• Continuously monitor energy costs
• Track success of a energy savings measure

Value

• ESG-Reporting and monitoring of energy costs
• Insights into the energy costs of the building
• Energy costs benchmarking
• Trace back costs to the energy source
• Additional weather normalization of the energy costs
• Prediction of yearly energy costs based on weather-normalized extrapolation

Recommended for components

Complete buildings or energy systems

Checked conditions

• Energy costs of the building or system
• Benchmarking
• Energy costs by energy source

This example shows the results of an Energy Costs analysis displayed in the Asset Overview.

Figure 1: Energy Cost Asset Overview result overview

Above all cards the interpretation text of the result is highlighted with the signal color of the result. In the top left the current KPI and the monthly trend is shown. On the right side of that you will find the recommendations of this result as well as further information about the function.

The three plots below are:

1. The building benchmark

Figure 2: Energy Cost Asset Overview plot bullet graph

The current energy cost is plotted inside a bullet graph between the limits of the signal color.

2. Compared to last months

Figure 3: Energy Cost Asset Overview plot month comparison

The plot shows the energy costs of the last three months, including the month of the analysis.

3. By energy source

Figure 4: Energy Cost Asset Overview plot energy source

The plot shows the distribution of energy costs by energy source.

Signal colors

Interpretations

Recommendations

KPIs

Summary KPI

Total energy cost KPIs

The .weather normalized KPI-variants below correct for the weather actually experienced during the period. See Weather normalization for more information.

Yearly predicted energy cost KPIs

The prediction is made by calculating and aggregating the yearly consumption of each relevant meter. For weather-dependent consumption, the yearly consumption is weather-normalized with heating/cooling degree hours (see Weather normalization). If a meter has less than one year of consumption data, the consumption is extrapolated to a full year.

Energy cost by energy source KPIs

The .weather normalized KPI-variants below correct for the weather actually experienced during the period. See Weather normalization for more information.

An energy source is reported once at least one meter of that source is mapped: a source whose meters measured no consumption reports a value of 0, while a source without mapped meters is omitted.

Timeseries

The timeseries is saved as a virtual datapoint. Every run of the analysis will add the summary KPI (current value) as an observation at the start of the analysis period to the timeseries.

Plots

Recommend Time Span

1 month

Recommended Repetition

Monthly

• Continuously monitor energy costs
• Track success of a energy savings measure

Value

• Reduced operational costs
• Detection of broken components

Recommended for components

• Combined heat and power

Checked conditions

• Efficiency of component
• Power to heat ratio (for component combined heat and power)

The Energy Efficiency analysis was applied to a real combined heat and power plant and the pins operating message, generator power, heat flow, and fuel power were mapped. Figure 1 shows the time series recorded for an exemplary period of 7 days. The grey shaded periods correspond to the operation of the plant.

Figure 1: Period of one week for which the energy conversion is not acceptable

A very low generator power compared to the heat flow and the fuel power indicates a failure of the generator component. The automated interpretation confirms our visual analysis of the time series shown in the figure, summed up by the qualitative warning level “red”. It also provides recommendations to address the problem.

Signal colors

Interpretations

Recommendations

KPIs

Energy Performance KPIs

Recommended Time Span

1 day to 1 week

Recommended Repetition

Every month

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

In this example, the filter contamination of an exhaust air filter of an air handling unit was analyzed over a period of four months. As can be seen in figure 1, 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.

Signal colors

Interpretations

Recommendations

KPIs

Recommended Time Span

1 month to 6 months

Recommended Repetition

Twice a month

Value

• Calculation of the electricity costs if a flexible tariff had been used
• Comparison to the actual electricity costs and identification of a savings potential
• Indication if a flexible tariff would be economically beneficial based on the consumption profile in the analysis period

Recommended for components

• Building
• Electricity meter

Checked conditions

• Checks whether or not the use of a flexible tariff would have reduced costs

Figure 1 shows a scenario in which energy consumption coincides with relatively low electricity prices. In this example, the current fixed electricity price is 0.3 €/kWh. The static part of the flexible tariff is 0.2 €/kWh and the dynamic part is defined by the datapoint electricity-price-day-ahead.

Figure 1: Plot of the dynamic part of the flexible tariff (top) and the active power of the electricity meter (bottom)

In this scenario, a flexible tariff would have saved 27.37 % of the costs compared to the costs with the current fixed tariff. Hence, the analysis returns a red signal color and a recommendation to consider a flexible tariff. The following KPIs are calculated:

Signal colors

To determine the signal color the KPI savings potential.relative is evaluated.

Interpretations

Recommendations

KPIs

Recommended Time Span

1 month to 12 months

Recommended Repetition

Every month

Value

• Linear model representing the heat demand of the building
• Determination of a basic heat load e.g. for hot water energy demand
• Determination of a heating limit temperature
• Determination of a maximum heating power within one period
• Determination of zero heating power duration within one period

Recommended for components

Building with gas heating or district heating

Checked conditions

• Heat energy consumption of the building
• Comparison of heat energy consumption between different periods
• Energy saving calculation of reference period to analysis period

This example shows the monthly sampled heat energy of the main heat meter over the period of more than one year. As shown in Figure 1, a heating period between November and April and a period without required heating were selected.

Figure 1: Heat meter energy samples with monthly resolution

Figure 2: Annual duration curve of the heat power

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Figure 3: Heat demand curve with base load power

Figure 4: Heat demand result

Signal colors

Interpretations

Recommendations

KPIs

Summary KPI

Recommend Time Span

1 year, but at least one full heating period and one period without heating demand

Recommended Repetition

monthly

• Continuously monitor energy savings in comparison to the reference period

Value

• Hourly operating point simulation depending on heat demand and environmental heat
• Choice of different heat pump models and technologies from several manufacturers
• Energy saving calculation and conversion in cost and CO₂ emission savings potential
• Investment calculation depending on
  • cost savings
  • interest rate
  • depreciation rate (optional)

Recommended for components

Building with gas heating or district heating

Checked conditions

• Heat energy consumption of the building
• Efficiency potential of heat pump usage
• Payback period for heat pump investment

This example shows a simulation of an air/water heat pump. For a brief technical explanation, see Figure 1.

Figure 1: Heat pump schematic

Figure 2: COP map air/water heat pump

Figure 3: Test reference year outside air temperature

Figure 4: Heating power (TRY2015)

Figure 5: Supply temperature and sink temperature (TRY2015)

Figure 6: Simulation results at different supply temperature and nominal power

Technical heat pump KPIs

While the maximum demanded heat power is 530 kW, the optimum heat pump power is selected to 360 kW at a design supply temperature of 50 °C. Thus, the majority of all operating points in the test reference year can be covered with 1400 MWh, especially for this heating system. The remaining heat energy about 100 MWh is provided by a heating element with 170 kW. With an annual cop about 2.53, the electric energy consumption is about 570 MWh.

Economic heat pump KPIs

Assuming an electricity price and a heat energy/gas price of the existing heating system, a cost saving can be calculated. In addition to the investment cost of the heat pump installation, other investment parameters can be taken into account. This includes:

-   Price increase rate of energy costs
-   Calculation interest rate
-   Depreciation period

With an installation price of 800 €/kW the heat pump, including installation and material, the total investment is about 295700 €. Assuming an electricity price about 0.24 €/kWh and a heat energy price about 0.12 €/kWh, we get a total cash flow about 298600 € after 5 years. The cash flow considers a yearly price increase rate of energy costs about 3% and a calculation interest rate about 12%. Thus, the annual cash flow is discounted and accumulated to a total cash flow. Where the CO₂ emission factor of the current heating system is assumed with 0.2 kg/kWh, the CO₂ emission factor of the electric net supply is assumed with 0.4 kg/kWh. Nevertheless, a CO2 saving of 63 t/a is achieved, since the primary amount of energy is significantly lower with 570 MWh.
As a result, a payback period of 5 years is sufficiently small to consider a closer look.

The interpretation would be:
The conditions for a heat pump investment are suitable

The recommendation would be:
It might make sense to further review the energy system. For this you could contact a specialist planner.

Signal colors

Interpretations

Recommendations

KPIs

Summary KPI