MediaWiki-API-Ergebnis

{
    "batchcomplete": "",
    "continue": {
        "gapcontinue": "Refining_Domain_Knowledge_for_Domain_Knowledge_Guided_Machine_Learning",
        "continue": "gapcontinue||"
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    "warnings": {
        "main": {
            "*": "Subscribe to the mediawiki-api-announce mailing list at <https://lists.wikimedia.org/postorius/lists/mediawiki-api-announce.lists.wikimedia.org/> for notice of API deprecations and breaking changes."
        },
        "revisions": {
            "*": "Because \"rvslots\" was not specified, a legacy format has been used for the output. This format is deprecated, and in the future the new format will always be used."
        }
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    "query": {
        "pages": {
            "799": {
                "pageid": 799,
                "ns": 0,
                "title": "Reducing Measurements of Voltage Sensitivity via Uncertainty-Aware Predictions",
                "revisions": [
                    {
                        "contentformat": "text/x-wiki",
                        "contentmodel": "wikitext",
                        "*": "{{Vortrag\n|vortragender=Anton Winter\n|email=usvjo@student.kit.edu\n|vortragstyp=Bachelorarbeit\n|betreuer=Bela B\u00f6hnke\n|termin=Institutsseminar/2022-04-29 (2. Raum)\n|vortragsmodus=in Pr\u00e4senz\n|kurzfassung=Due to the energy transition towards weather-dependent electricity sources like wind and solar energy, as well as new notable loads like electric vehicle charging, the voltage quality of the electrical grid suffers. So-called Smart Transformers (ST) can use Voltage Sensitivity (VS) information to control voltage, frequency, and phase in order to enhance the voltage quality. Acquiring this VS information is currently costly, since you have to synthetically create an output variability in the grid, disturbing the grid even further. In this thesis, I propose a method based on Kalman Filters and Neural Networks to predict the VS, while giving a confidence interval of my prediction at any given time. The data for my prediction derives from a grid simulation provided by Dr. De Carne from the research center Energy Lab 2.0.\n}}"
                    }
                ]
            },
            "223": {
                "pageid": 223,
                "ns": 0,
                "title": "Reduction of Energy Time Series",
                "revisions": [
                    {
                        "contentformat": "text/x-wiki",
                        "contentmodel": "wikitext",
                        "*": "{{Vortrag\n|vortragender=Lucas Krau\u00df\n|email=uneif@student.kit.edu\n|vortragstyp=Bachelorarbeit\n|betreuer=Edouard Fouch\u00e9\n|termin=Institutsseminar/2018-04-20\n|kurzfassung=Data Reduction is known as the process of compressing large amounts of data down to its most relevant parts and is an important sub-field of Data Mining.\nEnergy time series (ETS) generally feature many components and are gathered at a high temporal resolution.\nHence, it is required to reduce the data in order to allow analysis or further processing of the time series.\nHowever, existing data reduction methods do not account for  energy-related characteristics of ETS and thus may lead to unsatisfying results.\n\nIn this work, we present a range of state-of-the art approaches for time series reduction (TSR) in the context of energy time series.\nThe aim is to identify representative time slices from the multivariate energy time series without any prior knowledge about the inherent structure of the data.\nWe rely on unsupervised approaches, i.e., clustering algorithms, to derive these representatives.\nFor validation purpose, we apply the proposed reduction methods in two distinct approaches:\n\nFirst, we use the TSR method to reduce the run time of energy system optimization models (ESM).\nESM produce predictions and recommendations for the future energy system on the basis of historical data.\nAs the model complexity and  execution time of the ESM increases dramatically with the temporal resolution of the input data, reducing the input data without impacting the quality of predictions allows analysis at scales that are out of reach otherwise.\nIn particular, we will study the Perseus-EU model.\nOur analysis show the extent to which each TSR method can reduce run times without degrading the quality of the prediction significantly.\n\nThe second application relates to the compression of ETS emerging from grid measurement data.\nMeasurements from sensors installed in the energy grid collect observations in a high temporal resolution but are often highly redundant.\nHence, while the storage requirements are high, the collected time series only contain few interesting and representative observations.\nHere, we use TSR methods to reduce the multivariate time series to a set of representative time slices.\nWe show that amount of redundant observations can be greatly reduced in that way while preserving rare and interesting observations.\n}}"
                    }
                ]
            }
        }
    }
}