Interactive Scheduling of Appliance Usage in the Home

Authors: Ngoc Cuong Truong, Tim Baarslag, Sarvapali D. Ramchurn, Long Tran-Thanh

IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We demonstrate through extensive empirical evaluation on real world data that our approach improves savings by up to 35%, while maintaining a significantly lower bother cost, compared to state-of-the-art benchmarks.
Researcher Affiliation Academia Ngoc Cuong Truong, Tim Baarslag, Sarvapali D. Ramchurn, and Long Tran-Thanh University of Southampton, UK nct1g10@ecs.soton.ac.uk {t.baarslag, sdr1, l.tran-thanh}@ecs.soton.ac.uk
Pseudocode No The paper describes the workflow and algorithms in prose and a diagram, but does not include structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide a direct link to source code or explicitly state that the code for the described methodology is being released.
Open Datasets Yes We run our experiments on the Reference Energy Disaggregation Data Set (REDD) [Kolter and Johnson, 2011]
Dataset Splits No We use 75% of the REDD dataset as a training set, and the remaining 25% as a test set. The paper specifies train and test splits but does not mention a separate validation set.
Hardware Specification No The paper does not explicitly describe the specific hardware used for running its experiments (e.g., specific GPU/CPU models, memory details).
Software Dependencies No The paper does not provide specific software dependency details with version numbers (e.g., library or solver names with version numbers).
Experiment Setup Yes To obtain the user s decision on the system s elicited questions, we selected = 1 for the marginal comfort cost, as this is indicative of a typical user who is willing to make a trade-off between comfort and cost.