Algorithms for Fair Load Shedding in Developing Countries
Authors: Olabambo I. Oluwasuji, Obaid Malik, Jie Zhang, Sarvapali D. Ramchurn
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the heuristics against standard fairness metrics in terms of comfort delivered to homes, as well as the number of times they are disconnected from electricity supply. Thus, we establish new benchmarks for fair load shedding schemes. ... Using the data described above, we evaluate our load shedding algorithms and show how they perform in optimizing utilitarian and egalitarian social welfare, as well as in minimizing envy (deļ¬ned in Section 4). |
| Researcher Affiliation | Academia | Olabambo Ifeoluwa Oluwasuji, Obaid Malik, Jie Zhang and Sarvapali Dyanand Ramchurn School of Electronics and Computer Science, University of Southampton, Southampton, UK {oio1a14,o.malik,jie.zhang,sdr1}@soton.ac.uk |
| Pseudocode | Yes | Algorithm 1: Grouping, then selecting a group of agents for disconnection (i.e. Grouper Algorithm). ... Algorithm 2: Using consumption to select agents for disconnection while minimizing the difference in the number of times all agents are selected (i.e. Consumption-Sorter Algorithm). ... Algorithm 3: Selecting agents to shed while keeping the similarity between number of times all agents are selected (i.e. Random-Selector Algorithm). ... Algorithm 4: Using agent comfort costs to select agents to shed, while keeping the similarity between number of times all agents are selected (i.e. Cost-Sorter Algorithm). |
| Open Source Code | No | The paper does not contain an explicit statement or link providing access to the source code for the described methodology. |
| Open Datasets | Yes | By combining multiple sources of data, we create a dataset relevant to Nigeria from disaggregated electricity consumption data collected from Pecan Street s Dataport1. ... Dataport is the largest provider of disaggregated customer energy data [Parson et al., 2015] |
| Dataset Splits | No | The paper does not provide specific details regarding dataset splits for training, validation, or testing, 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 specify software dependencies with version numbers (e.g., programming languages, libraries, frameworks, or solvers). |
| Experiment Setup | No | The paper describes the input parameters for the heuristic algorithms (H, lt_i, G, L, Ii) but does not provide details on experimental setup parameters such as learning rates, batch sizes, number of epochs, or other system-level training configurations common in machine learning experiments. |