Use Case Library

Manufacturing & Natural Resources

Classification

Predictive maintenance

Companies across a variety of industries rely on machines: pumps, engines, elevators, turbines, etc. Some are more complex than others, but they surely have one thing in common – degradation of material. With each cycle (moment) of operation, components are losing their original physical parameters. Regular checks, diagnostics, and maintenance, or even replacement is an important part of machine operations.

The ideal scenario is to avoid failure of a given machine, thus being pro-active rather then reactive is for many businesses the only option. Also, acting at the right time has real financial implications. Imagine two extreme situations:

Maintenance planned for later than needed: the device would reach the point of failure and cause operations disruption. Every minute of downtime can bring big losses.
Maintenance planned too often or too soon: expenses for time and material spent is higher than the situation requires. Also, should a component be replaced completely, then the invested capital won’t be utilized to its full potential.

TIM’s Value

TIM can build automated ML models from time series data and predict the time remaining (Remaining Useful Life, RUL) or classify whether the device is already in a window (zone) of possible failure within a certain period of time (cycles).

Data from machine sensors are often sampled in seconds, or even milliseconds. TIM can work with data sampled in any sampling rate starting from milliseconds.

Also, effort and time required to set up TIM for production use is reduced to a fraction of what would be typically required. TIM, by design, automates most of the steps required for set-up and operations, and offers a robust ML solution.

Data

Input: explanatory variables should include measurements from relevant sensors, values of key settings, information about failures, cycle numbers and/or other.

Output: TIM’s output consists of forecasted RUL value or binary classification (1 or 0), depending on given scenario.

Author:

Michal Bezak - Tangent Works

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Active power losses forecasting

For physical reasons, transport of electrical energy via electricity transmission grid leads to losses; thus, less power can be withdrawn than is fed into the grid. Losses are generated, for example, through resistance in lines or transformers, and are released as heat. Factors such as load, outside temperature and switching states in the grid have an impact on power loss. Transmission system operators (TSO) must compensate for losses, and thoroughly manage them because losses influence balance on the grid.

TIM’s Value

TIM proved to deliver highly accurate predictions for intra-day and day-ahead forecasts and is fully capable to forecast practically for any prediction horizons. Even more, effort and time required to set up such forecasting solution is reduced to fraction of what would be typically required. TIM, by design, automates most of the steps required for set up and operations, and offers robust ML solution capable to quickly adapt to structural changes.

Data

Explanatory variables can include, besides historical actual values for losses, also technical information about relevant points on the power grid, load, and weather data.

TIM’s output consists of the forecasted active power loss in the same unit of measurement (typically kW or MW) and granularity as the input data, over the desired forecasting horizon.

More information

Read the exciting success story for this Use Case.

Author:

Philip Wauters - Tangent Works

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Wind Turbine

Wind turbines have become progressively more influential as the share of energy production and the infiltration of wind energy into power systems are steadily increasing. With this, the need for reliability in the production capacity of wind turbines has increased as well. The turbines must operate as smoothly as possible, since the unscheduled stoppage of these turbines can lead to significant production losses. In this use case the importance of operations and predictive maintenance are highlighted, and especially the role of health monitoring. Continuous monitoring of wind turbine health using anomaly detection improves turbine reliability and efficiency thus reducing maintenance and wind power costs. Finally, it allows for the optimal timing of turbine maintenance and repairs so that they can reduce the impact on the overall energy production and avoid catastrophic failure of the turbines.

TIM’s Value

Due to the highly automated, exceptionally fast and reliable modeling algorithm, TIM can build multiple anomaly detection models in a limited amount of time. It is especially useful in this case, since wind turbines often operate in wind farms where multiple turbines need to be monitored simultaneously. The speed and frequency of model building that TIM is capable of also allows for real time notifications of suspicious behavior in any turbine.

Data

Building a model for the detection of anomalous behavior in wind turbines requires a set of training data with several variables. The power output of a wind turbine is dependent on the efficiency of the blades, gear assembly, alternator/dynamo, as well as wind speed, wind direction and wind consistency. Also, the taller the wind turbine, the greater the energy produced, since wind speeds are greater at higher altitudes. With these variables set up in a time series format, TIM can use its anomaly detection capabilities to determine whether or not a power output observation is abnormal.

More Information

Are you interested in a walk-through scenario of this type of use case? Then take a look at our solution template on this use case! You can find it under Wind Turbine.

Author:

Carl Fransman - Tangent Works

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Transportation

Manufacturing & Natural Resources

Asset Failure Analysis

Complex and distributed assets (i.e. differently configured pumps or compressors installed across the globe) fail because of many reasons; some are purely due to the conception of the asset and represent normal wear and tear. Some failures though are due to local operating conditions and/or the specific configuration of the asset. Gathering data through IIoT platforms and performing anomaly detection not only allows for foreseeing such failures, but when this anomaly detection leads to explainable forecasts, engineers can perform root cause analysis. This leads to faster resolution of the issue and also allows R&D to analyse failures and come up with more robust and reliable equipment, which is even more important under servicisation-type contracts where the manufacturer bears (some of) the cost for maintaining the equipment and guaranteeing uptime.

TIM’s Value

TIM’s forecasting and anomaly detection capabilities not only produce accurate results, but these results are fully explainable; therefore TIM’s value extends beyond avoiding the failure and supporting predictive maintenance. TIM’s information can be analysed by technical maintenance teams in order to pinpoint the culprit rapidly and thus save precious production time by limiting downtime. TIM’s information can also be analysed by R&D teams to determine structural improvements to the equipment.

Data

Typical datasets used in this use case, consist of CMMS data combined with IIoT data and potentially external elements, such as operating conditions (weather, vibrations, speed, etc.)

Author:

Carl Fransman - Tangent Works

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Building Energy Optimisation

Back when buildings were pure power consumers and all the electricity was provided by a single supplier controlling the entire production apparatus and distribution grid, life was easy: simply match production and demand and everything is in balance. Now however, production has multiple suppliers with very different characteristics; stable (nuclear, hydro), controllable variable (coal and gas) and variable (solar, wind). Power consumption is also variable because nowadays many buildings produce electricity in addition to consuming it, and some even provide storage. Being able to forecast power requirements allows operators to ‘balance the system‘ or allows producers to optimise profitability (and even plan maintenance events at moments of otherwise lower return). Forecasting allows for decision-making between drawing power, producing and uploading energy, producing and consuming or producing and storing.

TIM’s Value

TIM’s straightforward configuration empowers users to build accurate forecasting models which can be dynamically augmented as new data streams become available. TIM will build a Model Zoo to improve accuracy in reaction to different patterns (both in consumption and in production) at different time-of-day. Industrial players can run their own TIM-powered forecasting in order to shave off peak consumption and lower their energy costs, whereas service providers can build TIM into their solution offering towards their clients in order to provide accurate and reactive forecasting capabilities.

Data

Typical data inputs are historic power consumption, smart meter readings, battery settings, weather data, etc.

Author:

Carl Fransman - Tangent Works

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Data Center HVAC

Data centers are critical infrastructure for countless operations. HVAC (Heating, Ventilation and Air Conditioning) failures can lead to a partial or full shutdown of the data center infrastructure in order to avoid critical equipment destruction. These shutdowns can cost hundreds of thousands of dollars in service and repair and in missed SLA fines. The ability to timely forecast HVAC malfunctions allows for predictive maintenance intervention, which can be planned during off-peak hours and allows for better system balancing during the intervention.

TIM’s Value

TIM’s approach to anomaly detection leads to high accuracy forecasts, because TIM deploys the optimal model for each situation; i.e. a different model may be required at 2 AM compared to 2 PM. TIM not only provides an anomaly detection, but also explains what leads to this result; feeding this information back to the technical teams empowers them to rapidly pinpoint what will cause a failure and take appropriate evasive action.

Data

Typical data for this use case relates to power consumption, sensor data (i.e. DeltaP) from the HVAC and filter age, among others. Data from external sources is also often included, such as weather data.

Author:

Elke Van Santvliet - Tangent Works

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Heat Consumption

This use case looks at heat consumption, more specifically through water heating. Typical domestic uses of hot water include cooking, bathing and space heating. This heat transfer process is associated with significant costs, thus ensuring energy efficiency is important. This illustrates the need for continuous monitoring of heating system health by closely watching whether the measured heat consumption is appropriate under given circumstances. Anomalous values might indicate underlying issues, such as a ruptured pipe, loss of system pressure, water being stolen or issues with a radiator or boiler. Accurate detection of these issues allows for well aimed, timely inspections.

TIM’s Value

TIM’s ability to generate explainable models proves its value in this use case as it enables users to understand which factors influence the target variable, heat consumption. Understanding what should be happening is a first step towards figuring out why this might sometimes not be the case. Accurate models can help to detect anomalies early on, which in turn can be crucial in preventing damage and costs. For example, the ability to timely detect and fix a leaking pipe might help in preventing a ruptured pipe. Although some anomalies might be fairly obvious to the trained eye (ex.g. a sudden fall out of (a part of) consumption might indicate a broken meter), others might be more subtle (ex.g. someone stealing a part of the supply by draining some of the water from a pipe). TIM manages to detect both observations that are anomalous in relation to historical values and observations that are anomalous in relation to current circumstances (predictors).

Data

Creating a model that can detect anomalous heat consumption, requires a set of training data. This training data typically consists of past values of the heat consumption, as well as other available variables that play a role in heat consumption. Such variables can be found in meteorological data (outside temperature, wind speed, wind direction…) as well as metered system data (incoming and outgoing water flow).

TIM then uses this data to determine each observation’s anomaly indicator, indicating how anomalous that observation is. This anomaly indicator in turn determines whether or not the threshold is crossed and the observation can be considered anomalous.

More Information

Are you interested in a walk-through scenario of this type of use case? Then take a look at our solution template on this use case! You can find it under Heat Consumption.

Author:

Elke Van Santvliet - Tangent Works

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Energy Consumption

Industry, companies, cities, households… all consume energy. Whether opting for electricity, gas or thermal power – or, more likely, a combination of them – the need for energy is all around us. Both consumers and producers can benefit greatly from accurate estimates of future consumption, not in the least because extreme volatility of wholesale prices force market parties to hedge against volume risk and price risk. Handling on incorrect volume estimates is often expensive, but accurate estimates tend to require the work of data scientists. This leads to the next challenge, since data scientists are hard to find and hard to keep. The ability to accurately forecast future energy consumption is a determining factor of the financial performance of market players. Therefore, the forecasts are also a key input of the decision making process.

TIM’s Value

The value of Machine Learning in this use case is clear, but has to be weighed against the costs and efforts it introduces. To achieve accurate forecasts, relevant predictors should be used. TIM automates model generation of accurate forecasting models, and tells you which input variables have the highest relevance in calculating the forecasts. Contrary to data scientists, TIM creates these models in seconds rather than days, or even weeks. The scalability of TIM’s model generation process allows for hundreds of models to be generated at the same time. This allows valuable data scientists to focus on the areas where their expertise matters most.

Let’s put this in numbers. Looking at a rough estimate of savings from a 1% reduction in the MAPE (Mean Average Percentage Error) of the load forecast, for 1 GigaWatt of peak load, can save a market player about:

€ 500 000 a year from long-term load forecasting,
€ 300 000 a year from short-term load forecasting,
€ 600 000 a year from short-term load and price forecasting.

And these numbers don’t even take into account the savings on data scientist capacity.

Data

Explanatory variables in energy consumption use cases include historical load data, in different levels of aggregation, as well as real-time measurements. These variables are supplemented by weather data, calendar information, day/night differences, In this use case, explanatory variables can include weather related data, wind speed in particular, complemented by more technical information such as the wind turbine type(s). TIM’s output consists of the forecasted wind production in the same unit of measurement (typically kWh or MWh) and granularity as the input data, over the desired forecasting horizon, production data…

TIM’s output in turn consists of the desired consumption forecast, in the same level of aggregation as the input target data, on short term, medium term and long term horizons.

More Information

Are you interested in a walk-through scenario of this type of use case? Then take a look at our solution template on this use case! You can find it under Electricity Load.

Author:

Elke Van Santvliet - Tangent Works

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Wind Production

Although ecological and quite popular, wind production is a volatile source of energy. Besides great opportunities for balancing the grid and forecasting production, this use case also involves a lot of predictive maintenance. Wind production use cases rarely centre around a single windmill or even a single wind farm, instead often involving a large portfolio of wind assets. The larger the portfolio, the more difficult to manage and obtain an optimal dispatch and exposure to the electricity market.

It is worth mentioning that mixed portfolios of solar and wind assets are common; don’t hesitate to take a look at this solar production use case.

TIM’s Value

TIM can contribute in this use case through automating and managing complex wind & solar modelling pipelines. Moreover, TIM allows for blended forecasts that unify high-quality intraday modelling and day(s) ahead modelling into a single API call. These forecasts are fully explainable and can take into account many additional variables, such as weather data, on top of historical values of the wind production. TIM accomplishes this in a scalable and accurate way, taking care to incorporate either current or expected data availability into the models it builds.

Data

In this use case, explanatory variables can include weather related data, wind speed in particular, complemented by more technical information such as the wind turbine type(s). TIM’s output consists of the forecasted wind production in the same unit of measurement (typically kWh or MWh) and granularity as the input data, over the desired forecasting horizon.

More Information

Are you interested in a walk-through scenario of this type of use case? Then take a look at our solution templates on this use case! You can find them under Single Asset Wind Production and Portfolio Wind Production.

Author:

Elke Van Santvliet - Tangent Works

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