- author = "Oliver Flasch and Martina Friese and Katya Vladislavleva and Thomas Bartz-Beielstein and Olaf Mersmann and Boris Naujoks and Joerg Stork and Martin Zaefferer",
- title = "Comparing Ensemble-Based Forecasting Methods for Smart-Metering Data",
- booktitle = "Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC",
- year = "2013",
- month = "3-5 " # apr,
- editor = "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and Ivanoe {De Falco} and Ernesto Tarantino and Carlos Cotta and Robert Schaefer and Konrad Diwold and Kyrre Glette and Andrea Tettamanzi and Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and Aniko Ekart and Francisco {Fernandez de Vega} and Sara Silva and Evert Haasdijk and Gusz Eiben and Anabela Simoes and Philipp Rohlfshagen",
- series = "LNCS",
- volume = "7835",
- publisher = "Springer Verlag",
- address = "Vienna",
- publisher_address = "Berlin",
- pages = "172--181",
- organisation = "EvoStar",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-3-642-37191-2",
-
DOI = "
doi:10.1007/978-3-642-37192-9_18",
- size = "10 pages",
- abstract = "This work provides a preliminary study on applying state-of-the-art time-series forecasting methods to electrical energy consumption data recorded by smart metering equipment. We compare a custom-build commercial baseline method to modern ensemble-based methods from statistical time-series analysis and to a modern commercial GP system. Our preliminary results indicate that that modern ensemble-based methods, as well as GP, are an attractive alternative to custom-built approaches for electrical energy consumption forecasting",
-
notes = "
EvoApplications2013 held in conjunction with EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013",
