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AI-Based Estimation of Available Flexibility at Individual House Level
This paper develops a data-driven model for assessing the availability of flexibility from individual household devices, at house level. The model predicts the potential shift, increase or decrease of the energy consumption of a particular type of device at a given time, in response to a price signal, and for each house separately. Therefore, the location of the flexibility source is known with accuracy. The model has an Auto-Encoder architecture based on Convolution Neural Network. The augmented model demonstrates good performance in terms of predicting the time-shift in load.
- Luxembourg Institute of Science and Technology Luxembourg
- Technical University Eindhoven Netherlands
- Technical University Eindhoven Netherlands
- Technical University Eindhoven TU Eindhoven Research Portal Netherlands
- Eindhoven University of Technology Netherlands
curtailable, deep learning, datadriven model, distribution level, time-shift, SDG 7 - Affordable and Clean Energy, Flexibility, SDG 7 – Betaalbare en schone energie
curtailable, deep learning, datadriven model, distribution level, time-shift, SDG 7 - Affordable and Clean Energy, Flexibility, SDG 7 – Betaalbare en schone energie
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
