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(Big) data analytics in smart grids
Citation for published version (APA):Mocanu, E., Nguyen, H. P., & Gibescu, M. (2016). (Big) data analytics in smart grids. Poster session presentedat European Data Forum 2016 (EDF 2016), June 29-30, 2016, Eindhoven, The Netherlands, Eindhoven,Netherlands.
Document status and date:Published: 29/06/2016
Document Version:Accepted manuscript including changes made at the peer-review stage
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Download date: 11. Mar. 2021
Smart Grids:
Data Intensive Infrastructures
Research Programme Manager: Dr. M. (Madeleine) Gibescu Departments: Electrical Engineering, Mathematics & Computer Science, Built Environment, Innovation Sciences
(No) Data
Unsupervised energy prediction
Real-Time Data
Supervised energy prediction
Factored Conditional Restricted Boltzmann
Machine (FCRBM) applied to energy
prediction
Electrical energy demand forecast
(Big) Data Analytics in Smart
Grids
Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu
Department of Electrical Engineering, Electrical Energy Systems group
Results
Methods
Results
Methods
• Reinforcement Learning Q-learning SARSA
• Transfer learning • Deep Belief Network
a) A new type of building -Commercial to residential Transfer -Residential to Residential Transfer b) A rennovated building e.g. electric heating system ↔non-electric heating system c) New tariff strategies (e.g. static tariff →time-of-use tariff)
*Mocanu, E., Nguyen, P.H., Kling, W.L. & Gibescu, M. (2016). Unsupervised energy prediction in a smart grid context using reinforcement cross-buildings transfer learning. Energy and Buildings, 116, 646-655.
**Mocanu, E., Larsen, E.M., Nguyen, H.P., Pinson, P. & Gibescu, M. (2016). Demand forecasting at low aggregation levels using factored conditional restricted Boltzmann machine. Proceedings of the 19th Power Systems Computation Conference (PSCC), 20-24 June 2016, Genoa, Italy
Demand forecast in a price-responsive context
Reinforcement Learning problem
Transfer learning problem
Continuous states estimation problem
Problem formulation using
multi-agents system
Gaussian Restricted Boltzmann Machine (GRBM) applied to quantify
the influencing factors
Research on Prediction, Optimization and Control of Smart Grids
