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Advanced Data Analytics in the Energy Industry

Intelligent forecasts and anomaly detection

Artificial Intelligence (AI) enables you to rethink and optimize the way data is processed and analyzed. Existing systems in almost all business areas can thus be made more intelligent.

Using AI-supported, dynamic forecasting models, behavioral predictions can be derived, for example, for process or data processes. It can also be used to answer the question of whether the currently incoming data corresponds to expectations from the past. Anomalies can thus be detected at an early stage.

This case shows how grid load data, for example, can be intelligently processed in the energy industry.

Project Overview

Background Situation

Comprehensive time series data in the form of network load data are available and must be recorded continuously and evaluated systematically and with foresight.


Implementation of an autonomous forecasting service with integrated anomaly detection.


Arvato Systems implemented a service that analyzes received data, automatically recognizes and categorizes the content. In addition, the service creates a real-time predictive model based on historical network load data. In this way, detailed forecasts can be made taking seasonal or general trends into account and anomalies can be detected at an early stage.


Better and earlier process planning
Reactions to anomalies in real time
Reduced risk of failure of associated systems
Reduced workload for employees thanks to automated warning systems
Integration into existing systems

This service was integrated into the Arvato Systems Smart Energy Platform. This allows us to offer a full cloud-based integration of AI and anomaly detection service in the Smart Energy Platform and help to "smartify" the processing and analysis of any kind of IoT sensor data. In our cloud, we offer multiple micro-services that are located in our application layer. These include basic knowledge and analysis as well as fully automated and intelligent decision systems. In a separate layer, we can store large amounts of data that can be pre-processed with our transformation engine and then transferred to the microservices and visualization processes.

Your Contact for Automated Network Load Monitoring

Niels Pothmann
Expert for Advanced Analytics & Artificial Intelligence