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Applying machine learning techniques for forecasting flexibility of virtual power plants

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Author: Macdougall, P. · Kosek, A.M. · Bindner, H. · Deconinck, G.
Type:article
Date:2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source:2016 IEEE Electrical Power and Energy Conference, EPEC 2016. 12 October 2016 through 14 October 2016
Identifier: 745598
ISBN: 9781509019199
Article number: 7771738
Keywords: Aggregation · Demand response · Energy flexibility · Heating Systems · Neural Networks · Prediction · Smart Grids · Agglomeration · Artificial intelligence · Commerce · E-learning · Electronic trading · Forecasting · Heating · Heating equipment · Learning algorithms · Learning systems · Linear regression · Network layers · Neural networks · Random number generation · Regression analysis · Supervised learning · Demand response · Energy flexibility · Heating system · Linear regression algorithms · Machine learning techniques · Multivariate linear regressions · Smart grid · Supervised machine learning · Smart power grids · 2016 ICT · MCS - Monitoring & Control Services · TS - Technical Sciences