{ "_id": "6a1ef540b401979e73416af0", "Package": "SmartMeterAnalytics", "Type": "Package", "Title": "Methods for Smart Meter Data Analysis", "Version": "1.1.1", "Date": "2025-04-18", "Authors@R": "c(\nperson(given = \"Konstantin\",\nfamily = \"Hopf\",\nrole = c(\"aut\", \"cre\"),\nemail = \"konstantin.hopf@uni-bamberg.de\",\ncomment = c(ORCID = \"0000-0002-5452-0672\")),\nperson(given = \"Andreas\",\nfamily = \"Weigert\",\nrole = \"ctb\",\nemail = \"andreas.weigert@uni-bamberg.de\"),\nperson(given = \"Ilya\",\nfamily = \"Kozlovskiy\",\nrole = \"ctb\",\nemail = \"ilya.kozlovskiy@uni-bamberg.de\"),\nperson(given = \"Thorsten\",\nfamily = \"Staake\",\nrole = \"ctb\",\nemail = \"thorsten.staake@uni-bamberg.de\",\ncomment = c(ORCID = \"0000-0003-1399-4676\")))", "Description": "Methods for analysis of energy consumption data\n(electricity, gas, water) at different data measurement\nintervals. The package provides feature extraction methods and\nalgorithms to prepare data for data mining and machine learning\napplications. Deatiled descriptions of the methods and their\napplication can be found in Hopf (2019, ISBN:978-3-86309-669-4)\n\"Predictive Analytics for Energy Efficiency and Energy\nRetailing\" and Hopf et al. (2016)\n \"Enhancing energy efficiency in\nthe residential sector with smart meter data analytics\".", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-19 06:55:31 UTC", "User": "root" }, "Author": "Konstantin Hopf [aut, cre]\n(), Andreas Weigert\n[ctb], Ilya Kozlovskiy [ctb], Thorsten Staake [ctb]\n()", "Maintainer": "Konstantin Hopf ", "Repository": "https://hopfkons.r-universe.dev", "Date/Publication": "2025-04-19 21:32:04 UTC", "RemoteUrl": "https://github.com/cran/SmartMeterAnalytics", "RemoteRef": "HEAD", "RemoteSha": "0169ab28cf3df94b8d3cf01da8b7936fd35497e6", "MD5sum": "518f0e20a06177a6b655dc417e305222", "_user": "hopfkons", "_type": "src", "_file": "SmartMeterAnalytics_1.1.1.tar.gz", "_fileid": "e70691a3a4a6e276c0173776c7656d66c204459a81232c9d4db68b217f050a81", "_filesize": 157783, "_sha256": "e70691a3a4a6e276c0173776c7656d66c204459a81232c9d4db68b217f050a81", "_created": 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