Abstract

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
Pages110-115
Number of pages6
ISBN (Print)978-1-61782-761-7
StatePublished - 29 Mar 2010
EventIFAC Conference on Control Methodologies and Technology for Energy Efficiency, CMTEE 2010 - Vilamoura, Portugal

Publication series

Name
PublisherElsevier Ltd.

Conference

ConferenceIFAC Conference on Control Methodologies and Technology for Energy Efficiency, CMTEE 2010
Abbreviated titleCMTEE
CountryPortugal
CityVilamoura
Period29/03/1031/03/10

Fingerprint

Electricity
Energy efficiency
Power plants
Neural networks

Keywords

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

Cite this

Bakker, V., Bosman, M. G. C., Molderink, A., Hurink, J. L., & Smit, G. J. M. (2010). Improved Heat Demand Prediction of Individual Households. In IFAC Conference on Control Methodologies and Technology for Energy Efficiency (CMTEE 2010): Vilamoura, Portugal, 29-31 March 2010 (pp. 110-115). Oxford: Curran Associates Inc.

Bakker, Vincent; Bosman, M.G.C.; Molderink, Albert; Hurink, Johann L.; Smit, Gerardus Johannes Maria / Improved Heat Demand Prediction of Individual Households.

IFAC Conference on Control Methodologies and Technology for Energy Efficiency (CMTEE 2010): Vilamoura, Portugal, 29-31 March 2010. Oxford : Curran Associates Inc, 2010. p. 110-115.

Research output: Scientific - peer-reviewConference contribution

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abstract = "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.",
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Bakker, V, Bosman, MGC, Molderink, A, Hurink, JL & Smit, GJM 2010, Improved Heat Demand Prediction of Individual Households. in IFAC Conference on Control Methodologies and Technology for Energy Efficiency (CMTEE 2010): Vilamoura, Portugal, 29-31 March 2010. Curran Associates Inc, Oxford, pp. 110-115, IFAC Conference on Control Methodologies and Technology for Energy Efficiency, CMTEE 2010, Vilamoura, Portugal, 29-31 March.

Improved Heat Demand Prediction of Individual Households. / Bakker, Vincent; Bosman, M.G.C.; Molderink, Albert; Hurink, Johann L.; Smit, Gerardus Johannes Maria.

IFAC Conference on Control Methodologies and Technology for Energy Efficiency (CMTEE 2010): Vilamoura, Portugal, 29-31 March 2010. Oxford : Curran Associates Inc, 2010. p. 110-115.

Research output: Scientific - peer-reviewConference contribution

TY - CHAP

T1 - Improved Heat Demand Prediction of Individual Households

AU - Bakker,Vincent

AU - Bosman,M.G.C.

AU - Molderink,Albert

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AU - Smit,Gerardus Johannes Maria

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N2 - 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.

AB - 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.

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KW - METIS-270731

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KW - Prediction methods

KW - Energy management

KW - Neural network applications

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BT - IFAC Conference on Control Methodologies and Technology for Energy Efficiency (CMTEE 2010)

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Bakker V, Bosman MGC, Molderink A, Hurink JL, Smit GJM. Improved Heat Demand Prediction of Individual Households. In IFAC Conference on Control Methodologies and Technology for Energy Efficiency (CMTEE 2010): Vilamoura, Portugal, 29-31 March 2010. Oxford: Curran Associates Inc. 2010. p. 110-115.