Implementation of Learning-Based Dynamic Demand Response on a Campus Micro-Grid
Authors: Sanmukh R. Kuppannagari, Rajgopal Kannan, Charalampos Chelmis, Viktor K. Prasanna
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this work, we demonstrate a system for a real time automated Dynamic DR (D2R). Our system has already been integrated with the electrical infrastructure of the University of Southern California... We will demonstrate a delayed implementation of a live DR event that occurred earlier. ... Finally, we will evaluate the success of our DR algorithm by measuring the curtailment error. |
| Researcher Affiliation | Academia | Sanmukh R. Kuppannagari, Rajgopal Kannan, Charalampos Chelmis and Viktor K. Prasanna Ming Hsieh Department of Electrical Engineering University of Southern California, Los Angeles, California {kuppanna,rajgopalk,chelmis,prasanna}@usc.edu |
| Pseudocode | No | The paper contains control and data flow diagrams (Figure 1 and Figure 2) but does not include any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement indicating that its source code is available or will be made publicly accessible. |
| Open Datasets | No | The paper states it uses 'Historical data from the energy consumption database' from the USC campus micro-grid and 'Smart meter data'. However, it does not provide any specific information, links, or citations for a publicly available or open dataset. |
| Dataset Splits | No | The paper mentions using 'Historical data' and 'time series forecasting techniques' for learning and prediction, but it does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or predefined splits). |
| Hardware Specification | No | The paper describes the system's integration with the USC electrical infrastructure and the use of smart metering, but it does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running experiments or the D2R system's computations. |
| Software Dependencies | No | The paper mentions using 'Open ADR messages' and 'time series forecasting techniques such as ARIMA and Lasso-Granger' but does not provide specific names of software or libraries with their version numbers that are necessary dependencies for replication. |
| Experiment Setup | No | The paper describes the operational aspects of the D2R system, such as using '15 minute intervals' and a typical DR interval of '4 hours', and mentions constraints like 'fairness' and 'strategy switching overhead'. However, it does not provide concrete hyperparameter values, specific training configurations, or other system-level settings required for detailed experiment reproduction. |