Zwei Masterarbeiten des Bits-to-Energy-Labs mit einem F?rderpreis der Stiftung Energieinformatik ausgezeichnet

Die Stiftung Energieinformatik zeichnet j?hrlich exzellente Bachelor- und Masterarbeiten aus, die einen Beitrag zur Gestaltung des Energiesystems von morgen leisten. Die Stiftung hat dabei gleich zwei Abschlussarbeiten des Bits-to-Energy-Labs in Folge (jeweils in den Jahren 2021 und 2022) mit einem F?rderpreis in der Kategorie "Beste Masterarbeit" ausgezeichnet.

Die Masterarbeit von Elena Giacomazzi zum Thema "Forecast of electricity demand and load peaks in the distribution network with LSTM and transformer architectures" gewann einen F?rderpreis im Jahr 2022.

Kurzzusammenfassung der Masterarbeit:

Short-term load forecasting is an essential problem for the electric power industry. It contributes to operating power systems efficiently and making them more adaptable for using renewable energy. This thesis evaluates the performances of a long-short term memory (LSTM) neural network, a basic Transformer model, and the Temporal Fusion Transformer (TFT) on an hourly day-ahead and week-ahead forecasting task. Additionally, the models are evaluated for their accuracy in predicting load peaks. This thesis contributes to the literature by applying Transformer-based architectures to short-term load forecasting, which are relatively new to the field. Experiments are conducted on a data set of German household loads which is aggregated to the distribution network level. This work finds that the LSTM architecture performs best for the day-ahead horizon with MAPE = 6.09%, while the TFT performs best for the week-ahead forecast with MAPE = 6.46%. Although the results demonstrate the value of modern deep learning methods for load forecasting, further work is necessary to validate the methods on other data sets.

Felix Haag gewann einen F?rderpreis im Jahr 2021 für seine Masterarbeit zum Thema "Explainable AI for in-depth benchmarking of domestic electricity consumption".

Kurzzusammenfassung der Masterarbeit:

Machine learning models have made strong advances in improving modeling capabilities. Such models can detect patterns in data and provide precise predictions, but are usually—compared to more traditional statistical methods such as linear regression—non-interpretable to humans. Motivated by this tension, research has put forth Explainable Artificial Intelligence (XAI) techniques that represent patterns discovered by machine learning in a human-readable way. One application area that can benefit from high predictive performance and the explanation of patterns in data is energy benchmarking. In the field, feedback of energy benchmarks has so far been limited to a single performance score. Thus, it remains unclear to feedback recipients which building and household characteristics affect their specific consumption. Taking a linear regression model as baseline, this thesis tests the applicability of XAI methods for energy benchmarking in the context of domestic electricity use in order to determine influencing factors. The results of this work show that XAI is able to identify building and household characteristics that are, according to a machine learning model, relevant for electricity consumption. Notably, XAI in combination with machine learning offers a higher predictive performance compared to a statistical approach while maintaining explainability. Energy benchmarking providers should therefore use XAI to offer additional information on performance ratings to improve the feedback given.