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Applying machine learning techniques for forecasting flexibility of virtual power plants

conference paper
2016
Macdougall, P.

Kosek, A.M.

Bindner, H.

Deconinck, G.
Topics
AggregationDemand responseEnergy flexibilityHeating SystemsNeural NetworksPredictionSmart GridsAgglomerationArtificial intelligenceCommerceE-learningElectronic tradingForecastingHeatingHeating equipmentLearning algorithmsLearning systemsLinear regressionNetwork layersNeural networksRandom number generationRegression analysisSupervised learningDemand responseEnergy flexibilityHeating systemLinear regression algorithmsMachine learning techniquesMultivariate linear regressionsSmart gridSupervised machine learningSmart power grids
TNO Identifier
745598
Repository link
https://resolver.tno.nl/uuid:22f7225a-90a2-4cf2-96b8-e26a70e86108
ISBN
9781509019199
Publisher
Institute of Electrical and Electronics Engineers Inc.
Article nr.
7771738
Source title
2016 IEEE Electrical Power and Energy Conference, EPEC 2016. 12 October 2016 through 14 October 2016
Files
To receive the publication files, please send an e-mail request to TNO Repository.

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