On the Balance of Meter Deployment Cost and NILM Accuracy
Authors: Xiaohong Hao, Bangsheng Tang, Yongcai Wang
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For evaluation, we show that our proposed scheme is efficient and effective in terms of approximation ratio and running time. On real and simulated datasets, our proposed framework achieves a higher monitoring accuracy at a much lower cost, outperforming common baseline algorithms. We carry out experiments using simulated and real data to evaluate the effectiveness of clearness function and ASMDP s performance, through comparison with three baselines: |
| Researcher Affiliation | Collaboration | Xiaohong Hao Tsinghua University Beijing, China haoxiaohong.ivy@gmail.com Bangsheng Tang Hulu LLC. Beijing, China bangsheng.tang@gmail.com Yongcai Wang Tsinghua University Beijing, China wangyc@tsinghua.edu.cn |
| Pseudocode | Yes | Algorithm 1 A PTAS for SMDP: ASMDP |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for their methodology is open-source or publicly available. |
| Open Datasets | Yes | The first experiments are conducted on real-world data from Power Net 2. Power Net provides per-device energy and usage statistics of an office building in Stanford University. More specifically, we use data collected from 126 different appliances in Sept. 2011. Sampling rate is basically 1Hz. Power Net: http://powernet.stanford.edu/ |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, and test splits (e.g., percentages, counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers required to reproduce the experiment. |
| Experiment Setup | Yes | To evaluate state monitoring accuracy, we run Viterbi-based state decoding in each meter s subtree based on [Wang et al., 2012]. State decoding experiments are conducted on randomly generated PLTs with average degrees 4 and 6. For each value of τ, we run ASMDP on 20 randomly generated trees. The error bar is the standard deviation of 20 runs. The simulation is conducted on a PLT with 200 nodes, including about 120 appliances. Power consumption of each appliance is uniformly randomly sampled from a distribution shaped resembling the histogram in Fig. 4 with mean 50 watts. |