In this tutorial we will showcase how Energy Twin Analytics models can be used for anomaly detection using pre-defined rules, such as Relative Value Deviation rule.
Author: Jana Zazvorkova
This tutorial is focused on visualization of a model in the app ET Views once it has been identified.
This video demonstrates how to identify a model in the ET Admin app using Energy Twin Analytics.
In the fast-paced environment of fast food restaurants, maintaining the right temperature in freezers and coolers is critical. Any deviation from the optimal temperature range can lead to food spoilage, financial losses, and health risks. However, recurring issues with refrigeration equipment—such as frequent door openings, equipment shutdowns, or mechanical failures—pose significant challenges.
Understanding the Challenges: Introduction to Refrigeration Anomaly Detection
To proactively detect and address these issues, ACTuate Facility Technologies, a company specializing in facility energy and system performance optimization for small to midsize buildings optimization, approached us for our data analytics expertise. Through our collaboration, we’re leveraging their extensive dataset from various fast food restaurants to develop sophisticated methods for detecting unusual behavior in refrigeration equipment, such as walk-in and reach-in freezers and coolers.
One of the major challenges in monitoring these systems is the variability in temperature that can occur during normal operations. For example, opening a freezer’s door for a few moments can cause a brief spike in temperature, which typically doesn’t indicate a problem. Regular defrost cycles can have a similar impact to the normal variability of the temperature data. However, more sustained deviations or unusual patterns might signal an issue that requires attention.
To address this, we need a system that can differentiate between normal fluctuations and potential problems—without the need for significant hardware additions. This is where data modeling and mathematical optimization come into play, allowing us to work effectively with the existing sensors and equipment.
Showcasing Temperature Monitoring Through Data Modeling
Based on discussions with the ACTuate team, we identified occurrences of malfunctions and selected a suitable model structure to analyze the temperature data from freezers and coolers. By fitting this model to the data, we can detect unusual system behavior. The process is entirely data-driven.
Our optimization techniques involve using a fluctuating window of past data, which dynamically adjusts in length based on the fit quality. The accuracy of this fit is measured by performance metrics, including Mean Absolute Error (MAE) as can be seen in the picture below.
Transforming Data Modeling into Anomaly Detection Rule
One of the most compelling aspects of our collaboration with the ACTuate team is the development of an active detection rule based on data modeling. This approach not only allows us to analyze past temperature data but also transforms that analysis into a proactive monitoring system.
Real-Time Monitoring
With real-time data, you can also use the data modeling to predict if certain thresholds will be breached and communicate with the on-site team about the necessary actions. Sometimes it can be as simple as a reminder to “close the door.”
Conclusion
Through our collaboration with ACTuate Facility Technologies, we have developed a sophisticated approach to monitoring and detecting unusual behavior in refrigeration systems. While our efforts contribute to significant energy savings, the primary objective is to reduce the number of incidents where temperature deviations lead to costly spoilage, ultimately ensuring the safety and quality of the stored product.
Great news for Energy Twin users! the energy twin evaluation is now available just in time for christmas! 
Our new extension, Energy Twin Evaluation is now available for free for all Energy Twin licensees. It doesn’t matter if you are using ET Analytics, ET Interactive or both, ET Evaluation offers a way to easily check your model’s performance, quantify energy savings and provide insight into your data.
With various aggregation periods, you can easily visualise longer time periods as well as track your energy efficiency journey using cumulative difference between the measurement and prediction.
Ready to optimize your energy strategy? Head to Stackhub now, try out the Energy Twin Evaluation extension, and share your thoughts with us!
Exciting NEWS! One of the projects where Energy Twin is developmental partner has launched in October.
SmartCoil – The First Real Time Monitoring System for AHU Coils
SmartCoil by Sensible revolutionizes the assessment and maintenance of AHU coils by introducing a cutting-edge, data-driven solution. Unlike traditional methods such as visual inspection and pressure drop testing, which are prone to inaccuracies, SmartCoil utilizes a network of strategically placed sensors feeding data to a cloud-based machine learning algorithm. This innovative approach translates raw coil data into actionable insights displayed on the SmartCoil dashboard and compatible BMS systems.
By providing real-time information on coil fouling levels, capacity, and energy consumption, SmartCoil empowers users to optimize their coil cleaning schedules, leading to significant cost savings and minimized downtime. The Energy Twin team helps with the data analysis and utilises custom models in order to provide a data-driven solution.
In the energy industry, professionals face challenges predicting and forecasting energy consumption.
Energy Twin & Long-term Forecast
Forecasting future energy consumption for extended periods, such as until the end of the year, presents a unique challenge that traditionally requires hours spent in tools such as Excel. Energy Twin can help with that – with just one click allowing us to forecast the total kWh consumption.
While we understand the significance of cost savings, precise figures can be influenced by various uncontrollable factors. Nevertheless, our solution sets itself apart with continuous updates to the forecast, leveraging real-time measurements. Through this approach, Energy Twin ensures predictions remain relevant and reliable, providing users with up-to-date information for well-informed decisions, with the flexibility of monthly or daily granularity.
Predict energy consumption effortlessly with Energy Twin and make data-backed decisions to overcome challenges and achieve efficiency.
Using ChatGPT has become a huge trend. It is supposed to be able to make many professions more efficient – why not energy engineers? We have decided to give it a try and integrate it to our Energy Twin extension for SkySpark.
Energy Twin and ChatGPT
We have been experimenting with various ways of summarizing results provided by our SkySpark extension Energy Twin. After successfully integrating OpenAI API into SkySpark we tried various tests, let’s have a look at two of them.
We know that generative AI cannot provide complex calculations, however, one could expect that summing 12 numbers wouldn’t be such an issue. See the screenshot above, where it provides the correct equation with the (always) wrong result with its usual confidence.
On the other hand, with the same input data, it can provide you with a nice poem.
We’re still scratching our heads about how to monetise the ability to generate poems about energy conservation.
In recent years, there has been a significant increase in demand for EVs. However, there are numerous challenges associated with EV charging, such as multiple EV charging strategy optimization. Numerous factors affect optimal charging strategies, but we will focus on the following:
1) Power Demand
When installing multiple EV chargers in an office building, one must carefully consider power demand in order to avoid exceeding the maximum power demand resulting in hefty costs.
2) Variable Energy Price
Flat energy tariffs are becoming obsolete, and nowadays, time-of-use tariffs are on the rise resulting in price optimization.
The solution to multiple EV charging lies in mathematical optimization, which seeks to minimise the cost of electrical energy purchase while taking into account all constraints. We need to know the prediction of the building’s electrical energy consumption, maximum power demand, electrical energy price profile, and EV information. A flexible charging strategy for new arrivals should also be considered.
We will look at three different scenarios of multiple EVs charging under simplified conditions. At 8 a.m., eight EVs will arrive, each with the following characteristics:
30% initial battery state
scheduled departure time at 4 p.m
requested battery state at the time of expected departure 90%
battery capacity 80 kWh
maximum charging power 50 kW
Simulations
Using Energy Twin, the building’s power load predictions are calculated. In all three experiments you will see the energy profile of the office building, individual arrival of the EVs and the electrical energy price prediction for the day.
The first scenario shows the basic strategy – EVs are charged immediately after being plugged in. The possible drawbacks of this strategy lie the high cost of energy in the morning, which correlates with the EVs‘ arrival time and high power demand.
The second scenario takes variable energy prices into account, allowing the mathematical optimization to find the solution with the lowest charging price. That’s an improvement. The charging time, on the other hand, overlaps with the cooling demand of the building’s HVAC system, resulting in an even higher power demand. This disadvantage is addressed in the third scenario.
Power demand is added as a new constraint to the optimization problem in the third scenario. This means that the total of predicted own building consumption and EV charging power must be kept below the maximum power demand threshold. As a result, EVs are charged during the day at slightly higher rates in order to spread the charging and reduce the maximum power demand.
The above experiments were only hints at the difficulties associated with charging multiple EVs. The simulation demonstrates how critical it is to use machine learning and predictions to develop optimal charging strategies that allow us to better manage energy.
In our previous post, we discussed how to manage an extensive building portfolio using Energy Twin and machine learning. This time we will show you how it can be further applied to another real-world example.
Energy Twin models can be used to forecast the future energy consumption profile, from a few hours to a few days ahead. This prediction was used as part of the control system for a virtual power plant’s Combined Heat and Power (CHP).
The virtual power plant’s goal is to use CHP and sell electrical energy when it is beneficial. In this case, heat is generated by both gas boilers and CHPs; however, CHPs have a lower gas-to-heat efficiency than gas boilers. As a result, the operator must prioritise gas boilers even if it contradicts the virtual power plant’s trading plan. Exceeding the contracted daily maximum gas consumption is far more expensive than the discrepancy in electrical energy delivery.
To avoid costly penalties, the model predicts daily gas consumption and is updated every hour. If a risk of exceeding occurs, the operator receives a notification with detailed information and suggested actions so that local technicians can respond in advance, if needed.
