Multiple-Profile Prediction-of-Use Games
Authors: Andrew Perrault, Craig Boutilier
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our approach with experimental results using utility models learned from real electricity use data. |
| Researcher Affiliation | Collaboration | Andrew Perrault and Craig Boutilier Department of Computer Science University of Toronto {perrault, cebly}@cs.toronto.edu Author now at Google Research, Mountain View, CA. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using TensorFlow and that TensorFlow software is available from tensorflow.org, but it does not state that the authors' own code for their methodology is open-source or provide a link to it. |
| Open Datasets | Yes | We experimentally validate our techniques, using household utility functions that we learn (via structured prediction) from publicly-available electricity use data. We find that the MPOU model provides a gain of 3-5% over a fixed-rate tariff across several test scenarios, while a POU tariff without consumer coordination can result in losses of up to 30%. These experiments represent the first study of the welfare consequences of POU tariffs. |
| Dataset Splits | No | The paper states, 'We split the data into 80% train and 20% test for each household.' However, it does not explicitly mention a validation split. |
| Hardware Specification | Yes | Each instance took around 3 minutes on a single thread of 2.6 Ghz Intel i7, 8 GB RAM. |
| Software Dependencies | No | The paper mentions using 'TensorFlow' and the 'Adam optimizer' and 'Dropout', but it does not specify version numbers for these software components, which is required for a reproducible description of ancillary software. |
| Experiment Setup | Yes | We represent zp0q i , zp1q i and zp2q i in fully-connected singlelayer neural networks, each with 10 hidden units and Re LU activations, and train the model with backpropagation. We implement the model in Tensor Flow [Abadi et al., 2015] using the squared error loss function and the Adam optimizer [Kingma and Ba, 2015]. We use Dropout [Srivastava et al., 2014] with a probability of 0.7 on each hidden unit. |