publications

2023

1. Energies

   A Systematic Literature Review on Data-Driven Residential and Industrial Energy Management Systems

   J. Sievers, and T. Blank

   Energies 2023, Feb 2023

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   The energy transition and the resulting expansion of renewable energy resources increasingly pose a challenge to the energy system due to their volatile and intermittent nature. In this context, energy management systems are central as they coordinate energy flows and optimize them toward economic, technical, ecological, and social objectives. While numerous scientific publications study the infrastructure, optimization, and implementation of residential energy management systems, only little research exists on industrial energy management systems. However, results are not easily transferable due to differences in complexity, dependency, and load curves. Therefore, we present a systematic literature review on state-of-the-art research for residential and industrial energy management systems to identify trends, challenges, and future research directions. More specifically, we analyze the energy system infrastructure, discuss data-driven monitoring and analysis, and review the decision-making process considering different objectives, scheduling algorithms, and implementations. Thus, based on our insights, we provide numerous recommendations for future research in residential and industrial energy management systems.

   @article{Energies.2023,
     title = { A Systematic Literature Review on Data-Driven Residential and Industrial Energy Management Systems},
     author = {Sievers, J. and Blank, T.},
     journal = {Energies 2023},
     volume = {16},
     issue = {4},
     pages = { 1688},
     numpages = {0},
     year = {2023},
     month = feb,
     publisher = ieeexplore,
     doi = {https://doi.org/10.3390/en16041688},
     dimensions = {true},
   }

2. ICCEP

   Secure short-term load forecasting for smart grids with transformer-based federated learning

   J. Sievers, and T. Blank

   International Conference on Clean Electrical Power (ICCEP), Sep 2023

   Abs Bib PDF Code

   Electricity load forecasting is an essential task within smart grids to assist demand and supply balance. While advanced deep learning models require large amounts of high-resolution data for accurate short-term load predictions, fine-grained load profiles can expose users’ electricity consumption behaviors, which raises privacy and security concerns. One solution to improve data privacy is federated learning, where models are trained locally on private data, and only the trained model parameters are merged and updated on a global server. Therefore, this paper presents a novel transformer-based deep learning approach with federated learning for short-term electricity load prediction. To evaluate our results, we benchmark our federated learning architecture against central and local learning and compare the performance of our model to long short-term memory models and convolutional neural networks. Our simulations are based on a dataset from a German university campus and show that transformer-based forecasting is a promising alternative to state-of-the-art models within federated learning.

   @article{ICCEP.2023,
     title = {Secure short-term load forecasting for smart grids with transformer-based federated learning},
     author = {Sievers, J. and Blank, T.},
     journal = {International Conference on Clean Electrical Power (ICCEP)},
     volume = {},
     issue = {},
     pages = { 229-236},
     numpages = {0},
     year = {2023},
     month = sep,
     publisher = ieeexplore,
     doi = {https://doi.org/10.1109/ICCEP57914.2023.10247363},
     dimensions = {true},
   }