Public Transport Bus Electrification – A Machine Learning Approach to Harmonizing Power Demand and Supply in the context of a Decentralized Energy Generation Using Renewable Energies
Stability on the distribution grid is a hard constraint in power grid operations. In order to maintain a constant electric current oscillating at 50 Hz, over- and undersupply must be avoided. Normally, this is achieved through the use of battery storage units, rotating mass storage and conventional overcapacities as backup. With the increasing importance of renewable energies for the electricity supply, distribution grid volatility increases; as does the demand for electric power, partly due to a growing electrification in the transport sector.As part of this project, we are seeking to devise an information system to predict short-term and long-term grid loads and to determine optimal load management strategies. For this, we want to accumulate heterogeneously formed data from multiple sources (e.g. historical smart meter data, powerline signal data, weather data, etc.) and blend them together. We then wish to apply data science on the resulting very high-dimensional data with the goal to predict supply and demand for a local power distribution network of arbitrary size.The first task is comprised of a series of extract-transform-load (ETL) operations within very large data-sets. The second task will be accomplished through a combined approach of machine-learning and statistical analysis methods. It involves several more or less interdependent classification as well as sequence prediction problems, for both of which there exists ample parallel solution methods and algorithms.Both, ETL and data analysis, will have to be performed in a long-term and a short-term context. This is to identify long-term patterns, on the one hand, and to properly react to current load scenarios, on the other. While a minor factor in long-term predictions, execution time in on-line decision making becomes crucial. We hope to exploit high performance computing techniques in order to be able to use complex modeling even for such decision situations, while adhering to the time constraint.