Improved Heat Demand Prediction of Individual Households

Vincent Bakker, M.G.C. Bosman, Albert Molderink, Johann L. Hurink, Gerardus Johannes Maria Smit

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

5 Citations (Scopus)
139 Downloads (Pure)


One of the options to increase the energy efficiency of current electricity network is the use of a Virtual Power Plant. By using multiple small (micro)generators distributed over the country, electricity can be produced more efficiently since these small generators are more efficient and located where the energy is needed. In this paper we focus on micro Combined Heat and Power generators. For such generators, the production capacity is determined and limited by the heat demand. To keep the global electricity network stable, information about the production capacity of the heat-driven generators is required in advance. In this paper we present methods to perform heat demand prediction of individual households based on neural network techniques. Using different input sets and a so called sliding window, the quality of the predictions can be improved significantly. Simulations show that these improvements have a positive impact on controlling the distributed microgenerators.
Original languageEnglish
Title of host publicationIFAC Conference on Control Methodologies and Technology for Energy Efficiency (CMTEE 2010)
Subtitle of host publicationVilamoura, Portugal, 29-31 March 2010
Place of PublicationOxford
PublisherCurran Associates Inc.
Number of pages6
ISBN (Print)978-1-61782-761-7
Publication statusPublished - 29 Mar 2010
EventIFAC Conference on Control Methodologies and Technology for Energy Efficiency, CMTEE 2010 - Vilamoura, Portugal
Duration: 29 Mar 201031 Mar 2010

Publication series

PublisherElsevier Ltd.


ConferenceIFAC Conference on Control Methodologies and Technology for Energy Efficiency, CMTEE 2010
Abbreviated titleCMTEE


  • IR-71348
  • METIS-270731
  • EWI-17435
  • Prediction methods
  • Energy management
  • Neural network applications


Dive into the research topics of 'Improved Heat Demand Prediction of Individual Households'. Together they form a unique fingerprint.

Cite this