AI for Energy

Energy disaggregation (NILM, Non Intrusive Load Monitoring)

Non-Intrusive Load Monitoring (NILM) is seen as a key technique for enabling innovative smart-grid services. By breaking down the energy consumption of households and industrial facilities into its components, NILM techniques provide information on present appliances and can be applied to perform diagnostics, find anomalies and save energy.

Electricity & Energy Trading

Artificial intelligence in electricity trading improves forecasting.

With AI, it is possible to systematically evaluate the large amount of data relevant for electricity trading, such as weather data or historical data. This leads to more precise forecasts, able to increase grid stability and thus security of supply. Striving to innovate  in forecasting field is fundamental to facilitate and accelerate the integration of renewable energies. Machine learning and neural networks play an important role in this remodelling process.

In recent years AI already brought results in improving forecasting quality: there is already a demand reduction for reserve control, even though share of volatile power generators in market has increased.

Virtual Power Plant

In a Virtual Power Plant, multiple decentralised units of a power network are linked and operated by a single, centralised control system. Those units can be either power producers (e.g. wind, biogas, solar, CHP, or hydro power plants), power storage units, power consumers or power-to-X plants (such as power-to-heat and power-to-gas). When integrated into a Virtual Power Plant, the power and flexibility of aggregated assets can be traded collectively. Thus, even small units get access to the lucrative markets (like market for balancing reserve) that they would not be able to enter individually. Any decentralised unit that consumes, stores, or produces electricity can become a part of a Virtual Power Plant.

Some AI algorithms are already sufficiently intelligent to trade on their own. This is the so called algorithmic trading, algo trading, or automated trading.

Moreover AI can automatically monitors and analyses trading on the electricity market. This makes it possible to detect and prevent more quickly and specifically deviations from norm, such as abuse of market power.

Power Grid, Smart Grids and Sector Coupling

The increased decentralisation and digitalisation of power grids, led to more difficult management of large grid participants. To keep the balance between all units requires evaluating and analysing a flood of data. AI makes data process quick and efficient.

Smart grids are another area of application. These networks transport not only electricity but also data. With an increasing number of volatile power generation plants, such as solar and wind, it is necessary that power generation reacts intelligently to consumption (and vice versa). AI makes possible to evaluate, analyse, and control the data of various participants (consumers, producers, storage facilities), connected to each other via the grid.

A particular focus of AI in the energy industry is on integration of electro mobility. E-cars spread offers opportunities and challenges: electric cars charge must be coordinated, but at the same time, they offer the possibility of storing electricity and stabilising the grid, for example by adjusting charge demand to price signals and availability. AI enables e-fleet smart management monitoring and coordinating.

AI can stabilise the power grid by detecting anomalies in generation, consumption, or transmission in near real time, develop effective solutions.

Further, AI facilitates maintenance work coordination and determines optimal times for interventions on networks or individual systems. This minimise costs and profit losses as well as disturbances of network operation.

Anomaly Detection

It identifies unexpected data points deviating significantly from expected behavior. Only anomalies are shown to users – reducing the cognitive load and allowing them to tell the signal from the noise.

It can be applied to data with high quality and one of the measurements within the data contains sufficient information to reveal the anomaly, such that a human operator, given sufficient time and skill, would be able to unearth it.

It can be also applied on data set whit a new key variable to the mix – time.  In many applications, they are vital for representing the normal behavior of a system.

Example Use Cases

Forecast the next Energy price

Analyse and forecast trading prices to execute your strategy.

Monitor and manage your electric vehicles fleet

Predictive maintenance is a key for cost saving and budget optimization.

Non Intrusive Load Monitoring (NILM) for energy disaggregation

Use Artificial Intelligence to disaggregate Energy consumption and give back to your customers, awareness of their behaviors.