Babel Buster Network Gateways: Big Features. Small Price.
|Energy Twin - machine learning SkySpark extension for
energy consumption analysis
|Jan Široký, PhD & Jana Zázvorková
see bios below
web site https://et.mervis.info/
Video Energy Twin at Haystack Connect virtual conference (Thu, May 6,12:25pm-1:40 pm EDT
Announcement of the joint Webinar - https://www.linkedin.com/feed/update/urn:li:activity:6800873785730981888/
There has been a
significant development in machine learning (ML) and integrating it into
numerous fields of expertise in the past few years. As Sundar Pichai, the CEO
of Google, said: "Machine learning is the future." Nowadays, ML also
serves as a buzzword promising extraordinary results while increasing systems
Since experts from the
HVAC/Energy domain usually do not use Python, R, or Matlab most of the
community does not use ML in everyday work. However, it does not mean that they
cannot take advantage of ML and use its potential, bringing their work to a
whole new level.
Meet Energy Twin (ET),
state-of-art ML algorithms accessible to the SkySpark platform.
What is the Energy Twin?
ET is a machine
learning SkySpark extension for energy consumption analysis, designed to
efficiently monitor multiple buildings using artificial intelligence to
identify problems and reveal the potential for future energy consumption
savings and optimization. ET aims at hourly, or even 15 minutes, data modeling.
This approach provides more valuable insight than aggregated daily, weekly or
monthly data models.
ET was designed for
energy optimization experts who usually do not have deep knowledge of
artificial intelligence. The main objective behind ET is to combine
state-of-the-art ML energy modeling techniques with a wide range of built-in
SkySpark functions (Sparks, KPI, tariff engine, reporting, etc.). Thanks to
smooth SkySpark integration, ET can be easily used without any coding or
studying the mathematical background of energy modeling techniques.
ET is a versatile tool
that can be used for various purposes, as described in this section.
Monitoring-based Commissioning (MBCx)
commissioning, ET is used to detect significant deviations from normal behavior
among a building portfolio and maximize the expert's time efficiency while
minimizing the human-error effect.
Based on data, a model
is identified using ET.
Fig1 - Example of ET
model prediction (blue line) and measurement (red line) comparison. Prediction
is influenced by the time of the week (see lower prediction during the weekend)
and by weather (higher predicted values during high-temperature periods).
Regardless of the
building portfolio size, ET allows experts to supervise buildings more
efficiently and identify anomalies or problematic buildings using Sparks and
KPIs. The resulting ET analysis serves as an initiative for local
investigations, which subsequently pinpoint the reason for abnormal energy
consumption leading to solving the issue and preventing future problems. As a
result, daily energy consumption can be lowered, ensuing financial savings.
There are various reasons behind detected anomalies: local heaters always on, a
non-stop operation of air doors due to manual regime override or a current
meter with an incorrect multiplication factor. Some of them could remain
undetected until a routine check-up is needed, resulting in unnecessary
building operation costs.
Fig2 - The figure
shows anomaly detection using the +20 kW threshold. There is a binary
indication of timestamps in the bottom chart when the measurement is higher
than the predicted value + 20 kW. The gray area in the top chart represents the
estimate of the amount of avoidable energy.
provides various features that are useful for M&V. Details and more
information can be found on the SkyFoundry website.
On top of the standard
SkySpark functionalities, ET adds ready-to-use modeling capabilities in
adherence to the International
Performance Measurement and Verification Protocol – IPMVP, ASHRAE Guideline 14 Measurement of Energy,
Demand and Water Savings, and other M&V guidelines. Furthermore, ET
evaluates the key statistical metrics that are defined by the guidelines. As a
result, one can readily see if the identified model can be used as an M&V
Identifying energy savings potential
Using ET analysis, we
can identify the black sheep, a building with the most significant energy
savings potential, in the building portfolio. Based on predictions and
resulting KPIs, one can quantify potential savings. For a more detailed
example, see ET Case study.
Peak demand prediction
Since ET can provide a
15-minutes prediction, it can be used in peak demand reduction scenarios. For
example, using SkySpark’s built-in weather prediction, a model predicts energy
consumption for the next days with 15-minutes sampling. This information can be
used in a particular demand reduction (peak shaving) application.
Fig2 - The blue arrows show peak demand that was avoided thanks to the
early warning based on the ET model. Local technicians were able to take
preventive actions. Low priority equipment consumption was lower during the
mid-day when full cooling power was needed.
ET is also compatible
with the SkySpark tariff engine. This allows modeling future energy costs based
on detailed pricing schemes and ET prediction.
Other possible uses of ET
ET was primarily
designed for electrical energy consumption prediction; however, it can also
provide a reliable prediction for other variables: water consumption or heat or
gas (except for the heating systems with slow dynamics). Surprisingly, ET was
also successfully used for modeling domestic hot water tank temperatures on a
The foundations of ET
consist of a mathematical model with a similar structure as the TOWT model
described in the paper: "Methods for Analyzing Electric Load Shape and its
Variability" (Lawrence Berkeley National Laboratory Report LBNL-3713E, May
2010). A similar model is used in popular energy modeling packages used in
M&V projects like RMV2.0, NMECR, or OpenEEMeter. More details about the
model can be found in a separate document.
Developers can make
use of an open API and use model prediction in custom SkySpark applications. ET
makes all calculations On the Edge – only the standard SkySpark libraries are
used. In other words, no cloud nor external libraries are needed. Additionally,
there is no data sharing while using ET; your data are not stored anywhere or
accessible for anyone else.
The possible practical
benefits of applying artificial intelligence and machine learning-based
applications in HVAC system operation are tremendous. Especially with large
building portfolios with various types of facilities. Machine learning is an
advanced technology, but one does not need a degree in Mathematics or Computer
Science to use it. With the proper tools, such as the ET extension for
SkySpark, the ML integration into everyday work is conveniently accessible.
Some of the advantages that ET brings are:
· maintaining high-standard services
regardless of portfolio size,
· maximizing efficiency while minimizing
· focusing on the most severe anomalies
by avoidable energy estimation
Using ML for analyzing
a building portfolio makes it possible to maximize the experts' efficiency and
take care of the mundane part of the job. Thanks to ET, the potential for
energy savings is exploited, resulting in successful energy consumption savings
and building operation costs.
Although ET does not
fit all the needs, it is possible to make custom ET enhancements such as custom
views or Sparks based on ET mode. There is also a possibility of creating a
customized machine learning application in SkySpark. Each ET license includes 2
hours' worth of client support – to help with installation, configuration,
modeling, or interpretation of the results.
For more information
see the ET landing page https://et.mervis.info/ or sign-up for the joint Skyfoundry and
Energy Twin webinar (June 3, 2021 at 11:30AM ET, email at firstname.lastname@example.org
for an invitation).
Jan Široký, PhD
As part of his doctoral studies, he focused on
active fault detection and control with application to HVAC. During his doctoral
studies, he completed an internship at ETH Zurich in the Automatic Control
Laboratory. He has been working in the Energocentrum Plus company since 2003.
He is the leader of a research team focusing on machine learning and automated
analysis of HVAC/energy data. He is also leader of the Energy Twin team.
Jana Zázvorková is the Communications Manager
of Energy Twin Team and currently finishing her Master's degree in Medical
Electronics and Bioinformatics at the Czech Technical University in Prague.
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