Time series are a big part of what we do in the energy industry. Modern energy supply systems use smart meters that record energy consumption in real time and transmit the data obtained at short intervals. Energy exchanges and trading centres use time series to keep an eye on energy prices and make sound business decisions. Load profiles, which describe energy consumption over time, are essential for utilities. This is especially true for load forecasting, network optimisation and capacity planning.

The collection and management of this extensive time series data requires advanced solutions to gain valuable insight into energy processes and make data-driven operational decisions. This in turn directly affects profitability, as optimised operational management and resource utilisation can lead to significant cost savings. In this context, efficient data management and advanced analytics are becoming increasingly important in the energy industry.

This is where SOPTIM Elements comes in with a cloud-based time series service that has been specially developed to meet the growing challenges. But what exactly are these challenges?

On the one hand, the volume of time series data will increase significantly. The introduction of dynamic electricity tariffs based on fluctuating wholesale market prices requires more complex data collection to accurately capture the variable nature of tariff structures and consumption patterns. The relational database systems used so far have reached their limits. The SOPTIM Elements time series service is therefore based on a high-performance and scalable cloud-based database system to write this data in real time.

On the other hand, the aggregation of time series data is becoming more complex and needs to be done faster. The expansion of electromobility, the switch to heat pumps and the increasing share of renewables in the electricity mix pose new challenges. The increased variability requires more frequent and faster time series analyses, which is why the SOPTIM Elements time series service offers a query and analysis system that provides scalable options for large data sets in the cloud.

Finally, the use and importance of time series data in AI is becoming increasingly relevant. Artificial intelligence is increasingly being used in the energy industry to make accurate predictions and forecasts based on historical time series data. This ranges from predicting energy consumption and generation to energy prices. The SOPTIM Elements time series service provides rapid and effective access to this data, enabling real-time training of AI algorithms and prediction-making, thus accelerating the training processes.