Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Efficient Buyer Groups for Prediction-of-Use Electricity Tariffs
Authors: Valentin Robu, Meritxell Vinyals, Alex Rogers, Nicholas Jennings
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We propose a polynomial time algorithm to compute efficient buyer groups, and validate our approach experimentally, using a large-scale data set of domestic electricity consumers in the UK. |
| Researcher Affiliation | Academia | Valentin Robu Heriot-Watt University Edinburgh, Scotland, UK EMAIL Meritxell Vinyals, Alex Rogers and Nicholas R. Jennings University of Southampton Southampton, UK EMAIL |
| Pseudocode | Yes | The pseudocode of the method is given in Algorithm 1. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | No | The paper mentions using 'a large dataset of around 3000 households (i.e. customers) in the UK' but does not provide concrete access information such as a link, DOI, or formal citation for public access. |
| Dataset Splits | No | The paper does not provide specific details regarding training, validation, or test dataset splits, such as percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with versions) needed to replicate the experiment. |
| Experiment Setup | Yes | The evaluation considers three tariffs (F, P and P+) detailed as follows. Tariff F (Flat), corresponds to a flat tariff in which customers pay a fixed price ( 0.205) per k W consumed. Tariff P (Predictive) reduces the baseline price of tariff F at the cost of charging a penalty of 0.01/ 0.03 for each k W underconsumed/overconsumed respectively. Finally, tariff P+ (Highly Predictive) offers the lowest baseline price but severely penalizes any imbalance (with penalties of 0.17/ 0.26 per Kw underconsumed/overconsumed). |