Predicting energy consumption using machine learning


J. Schneider, M. Dziubany, A. Schmeink, G. Dartmann, K. Gollmer, S. Naumann,


        In the development of a sustainable smart infrastructure, the exact adaptation of energy production to the actual energy demand is of crucial importance. For this purpose, it is necessary to predict future energy requirements as accurately as possible. In this chapter, statistical and machine learning methods are presented that learn from historical consumption data to predict future consumption. In addition, the implementation of such a prognosis is described by using a real-world example, where the challenges encountered are discussed and the results are presented.

BibTEX Reference Entry 

	author = {Jens Schneider and Matthias Dziubany and Anke Schmeink and Guido Dartmann and Klaus-Uwe Gollmer and Stefan Naumann},
	title = "Predicting energy consumption using machine learning",
	pages = "167-186",
	publisher = "Elsevier",
	series = "Machine Learning for the Internet of Things",
	editor = "Guido Dartmann;Houbing Song;Anke Schmeink",
	ISBN = "9780128166376",
	edition = "1st Edition",
	month = Oct,
	year = 2019,
	hsb = RWTH-2020-04849,


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