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Chapter


Title

Quality Versus Speed in Energy Demand Prediction

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2022

Chapter type

chapter in monograph / paper

Publication language

english

Keywords
EN
  • time-series analysis
  • energy demand forecasting
  • artificial neural networks
  • time-quality trade-off
Abstract

EN Effective heat energy demand prediction is essential in combined heat power systems. The algorithms considered so far do not sufficiently take into account the computational costs and ease of implementation in industrial systems. However, computational cost is of key importance in edge and IoT systems, where prediction algorithms are constantly updated with new arriving data. In this paper, we propose two types of algorithms for heat demands prediction: (1) novel extensions to the algorithm originally proposed by E. Dotzauer and (2) based on a kind of autoregressive predictor. They were developed within an R &D project for a company operating a cogeneration system and for their real dataset. We evaluate the algorithms experimentally focusing on prediction quality and computational cost. The algorithms are compared against two state-of-the art artificial neural networks.

Date of online publication

29.07.2022

Pages (from - to)

447 - 452

DOI

10.1007/978-3-031-12423-5_34

URL

https://link.springer.com/chapter/10.1007/978-3-031-12423-5_34

Book

Database and Expert Systems Applications : 33rd International Conference, DEXA 2022, Vienna, Austria, August 22–24, 2022, Proceedings, Part I

Presented on

International Conference on Database and Expert Systems Applications (DEXA 2022), 22-24.08.2022, Vienna, Austria

Ministry points / chapter

20.0

Ministry points / conference (CORE)

70.0

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