The energy industry is undergoing a transformation as renewable energy sources, electrification of transport and heating, energy storage, and smart grids are becoming more prevalent. These changes come with a series of new challenges for energy and utility companies, including optimizing energy generation and distribution, ensuring network stability under various conditions, and meeting the fluctuating energy demand. Since the electricity system is an extensive network of interconnected components that work together to generate, transmit, and distribute electricity, managing these new challenges is becoming increasingly complex.

The energy industry is data-rich, and the amount of data from various sources, such as IoT devices, smart meters, and intelligent infrastructure, is constantly increasing. Machine learning (ML) algorithms can analyze these vast amounts of data, identify patterns, and make predictions, which can help energy companies to improve their operational efficiency, reduce downtime and improve the reliability of their networks.

This blog post will look closely at three machine learning applications for the energy domain. We will explore the problems that they are solving for energy companies and see what benefits they can bring. Finally, we will discuss what these organizations need to productively adopt these solutions in their day-to-day operations.

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Predictive maintenance for critical infrastructure

Predictive maintenance is a proactive maintenance approach that uses machine learning algorithms to predict equipment failures before they occur. In the energy industry, predictive maintenance can be a valuable tool for ensuring the reliable and efficient operation of critical infrastructure, such as power plants, wind farms, transformer stations, and transmission lines. Companies in the energy industry face significant challenges in maintaining their assets, including the high cost of equipment downtime, the complexity of their infrastructure, and the difficulty of detecting and diagnosing equipment failures.

Developing a machine learning algorithm for predictive maintenance involves collecting and preprocessing data from the system, including examples of healthy and faulty system conditions. The collected data is processed to extract features that act as condition indicators and change predictably as the system degrades. Based on these condition indicators, a machine learning model is trained to detect anomalies, classify faults, and estimate the remaining useful life of the machine. The model is trained on the extracted features using supervised or unsupervised learning techniques. By identifying equipment failures before they occur, companies can proactively schedule maintenance and repairs, reducing unnecessary downtime and avoiding additional damages and costly repairs.

Predictive maintenance for equipment with multiple failure modes requires large amounts of data for each asset to be analyzed. This means, on the one hand, significant investments in hardware (sensors) and, on the other hand, dedicated data and ML teams building and maintaining the data infrastructure and ML models. After these investments, companies should expect significant benefits and a reduction of unplanned equipment downtime. Keeping in mind again that the energy system is critical infrastructure, the application of predictive maintenance makes even more sense.

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Demand Forecasting

Forecasting the electricity demand is a crucial aspect of the energy industry, as it allows companies to anticipate and plan for changes in energy consumption and adjust their production accordingly. Thus, accurate demand forecasting mitigates the risk of overproduction or underproduction and, in this way, helps reduce losses and improve efficiency. Precise demand forecasting can be challenging, as it requires companies to analyze complex data sets and consider a wide range of factors that may influence their customers’ energy consumption. Additionally, decentralized energy generation (e.g., from photovoltaic or wind) is also turning some consumers into prosumers, increasing the dynamic and complexity of the grid.

Energy companies can use machine learning algorithms to analyze large amounts of data and predict energy consumption curves. This approach involves collecting data from various sources, such as historical energy consumption data, (historical) weather forecasts, and various social and economic indicators, and using machine learning algorithms to identify trends and patterns. Additional data sources such as smart meters can allow companies to predict changes in energy consumption on a more granular grid level and adjust their production or purchases accordingly.

Different approaches can be used for demand forecasting based on the specific use case and available data. Since demand forecasting is closely linked to energy trading, different time horizons for the forecast and energy markets are to be addressed, such as day-ahead, intraday, and control energy markets.

Traditional statistical models, such as ARIMA (autoregressive integrated moving average) based ones, are well-suited for time series data, which is the type of data that is commonly used in electricity demand forecasting. They can work with a few data points and be a good starting point. Machine learning algorithms such as neural networks, on the other hand, are well suited for handling large multivariate datasets with complex relationships. They can identify patterns and trends in the data that may not be easily identified using traditional statistical models. In addition, machine learning algorithms can handle non-linear relationships between variables. They can incorporate a wide range of data sources, including weather data, economic indicators, and demographic data.

In addition to the high cost related to data infrastructure, highly skilled data, and ML teams are required to develop, maintain, and ensure the high availability of such solutions. Data quality, model robustness, prevention of overfitting, and regular model updating all play an essential role when the high performance of the solution is to be achieved. The explainability of the prediction is also a key factor when trying to improve the decision-making process.

With additional regulations, fluctuating energy prices, and a competitive market, accurate forecasts are crucial for energy companies to achieve high efficiency. Accurate predictions help companies optimize their production, distribution, and pricing strategies, which can significantly impact their profitability.

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Automated detection of energy fraud

Energy fraud and theft are significant challenges for companies in the energy industry. These activities can result in substantial losses for energy companies and, more importantly, can also impact the safety and reliability of the energy grid. Undetected fraudulent activity has the potential to damage expensive equipment and cause blackouts. Detecting energy fraud and theft can be as challenging as finding a needle in a haystack, as these activities can be subtle and difficult to identify.

One solution could be to use machine learning algorithms to analyze consumption data from various sources, such as smart meters and other sensors, to identify anomalies that may indicate energy fraud or theft. These algorithms can detect patterns in energy usage that deviate from expected customer behavior, such as sudden increases or decreases in energy consumption. Companies can employ machine learning algorithms to detect abnormal behaviors, such as tampering with meters or other equipment. One interesting scenario is the installation of electric vehicle charging stations by end customers without a permit from the distribution system operator, which could lead to overloading the grid.

In order to detect these anomalies, which are rare events and represent a very small percentage of the total distributed energy, the energy infrastructure and assets need to be monitored and analyzed. Supervised learning methods can be used to analyze historical data based on cases where fraud was manually detected or observed. Using unsupervised machine learning methods, companies can detect anomalies and outliers that are hard to interpret manually or were not observed before by detecting patterns and trends.

Detecting anomalies and bad actors in the grid is essential to maintaining the critical energy infrastructure. Having good visibility and understanding of the state of the grid at any point and any time could reduce potential losses and damages.


Machine learning approaches have proven effective in solving problems and bringing value to energy companies’ day-to-day business. While developing such solutions requires high initial investments and considerable running costs, including the acquisition of sensor equipment, data and cloud infrastructure, tooling, and dedicated expert teams to manage the complexity, the long-term benefits are well worth the investment. By implementing machine learning, energy companies can gain a competitive edge, optimize operations, reduce costs, and enhance overall performance. As such, the benefits of machine learning outweigh the costs and make it a worthwhile investment for companies looking to improve their operations.

If you want to learn more about machine learning and explore additional use cases, feel free to contact us, or check our machine learning offering.

Konstantin is a software and systems engineer with a background in Electrical Engineering and a Masters's degree from the Karlsruhe Institute of Technology. He has experience building data-driven applications for various industries, such as energy, automotive, industrial automation, and logistics.