Applying machine learning techniques for forecasting flexibility of virtual power plants

Title

Applying machine learning techniques for forecasting flexibility of virtual power plants

Author

Macdougall, P.
Kosek, A.M.
Bindner, H.
Deconinck, G.

Publication year

2016

Subject

2016 ICT
MCS - Monitoring & Control Services
TS - Technical Sciences
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

To reference this document use:

http://resolver.tudelft.nl/uuid:22f7225a-90a2-4cf2-96b8-e26a70e86108

TNO identifier

745598

Publisher

Institute of Electrical and Electronics Engineers Inc.

ISBN

9781509019199

Source

2016 IEEE Electrical Power and Energy Conference, EPEC 2016. 12 October 2016 through 14 October 2016

Article number

7771738

Document type

conference paper