Wache, Holger

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Wache, Holger

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2015 - 20204
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  • Vorschaubild
    Publikation
    Impact of prosumers on the accuracy of load forecast with neural networks
    (2020) Muff, Roswitha; Wache, Holger [in: Abstracts from the 9th DACH+ Conference on Energy Informatics]
    More and more prosumers will penetrate the power grid. But how do prosumers affect the accuracy of the day-ahead load forecast? In contrast to related research on prosumers and load forecast, this paper addresses the impact of different shares of prosumers on the load forecast for areas with several households. In order to answer this research question, the load forecast accuracies for a dataset without prosumers is compared to the ones of datasets with different shares of prosumers in an experimental setup using neural networks. A sliding window approach with lagged values up to seven days is applied. Apart from electricity consumption data weather and date data are considered. The conducted tests show, that the mean absolute percentage error increases from about 8% for a dataset without prosumers up to about 39% for a dataset with a share of prosumers of 80%. It can therefore be concluded that prosumers decrease the accuracy of the day-ahead load forecast with neural networks.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    Load management for idle capacity of power grids
    (Springer, 2019) Layec, Vincent; Wache, Holger [in: Energy Informatics]
    A major issue hampering a rapid substitution of fossil fuels by electricity from sustainable sources is the fear of congestion of the power grid and of associated costs of their reinforcement. The conventional approach prevents any rapid raise of electricity demand by encouraging other energy carriers and sector coupling. However, no approach investigates the utilization of the full capacity of the power grid alone, which are kept idle to provide sufficient reserve for the case of a failure. Therefore, we test a load management approach designed to utilize this reserve capacity. We verify in this paper the correct functionality of the system made of a device manager for cost optimization of schedules and of a grid manager to enforce the respect of power limits of the grid. This novel approach contributes to reduce emission of greenhouse gases without grid reinforcement.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Classification of Economic Approaches for Smart Grid
    (IEEE, 2015) Keller, Corinne; Manser, Daniel; Vogler, Sandro; Wache, Holger [in: 12th International Conference on the European Energy Market, IEEE Computer Society Press]
    Abstract—Several European countries are increasingly focusing on renewable energy in order to satisfy their demand. A core problem of these sources is their reliability, which means less continuously available energy is accessible. Smart grids are trying to cope with this problem by adding intelligence to the net, which tries to adjust the load according to the current produced amount of electrical energy. Many approaches try to tackle down this problem by technical means. This paper analyses existing economical approaches for smart grid environments and highlights the unique features and important properties of a broad selection of papers. Classification criteria are derived from existing literature. Afterwards, the most prominent papers are used to demonstrate how the classification scheme can be applied.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    Energy saving in smart homes based on consumer behavior: A case study
    (IEEE, 2015) Zehnder, Michael; Wache, Holger; Witschel, Hans Friedrich; Zanatta, Danilo; Rodriguez, Miguel [in: First IEEE International Smart Cities Conference (ISC2-2015)]
    This paper presents a case study of a recommender system that can be used to save energy in smart homes without lowering the comfort of the inhabitants. We present an algorithm that mines consumer behavior data only and applies machine learning to suggest actions for inhabitants to reduce the energy consumption of their homes. The system looks for frequent and periodic patterns in the event data provided by the digitalSTROM home automation system. These patterns are converted into association rules, prioritized and compared with the current behavior of the inhabitants. If the system detects opportunities to save energy without decreasing the comfort level, it sends a recommendation to the inhabitants.
    04B - Beitrag Konferenzschrift