Bottom-Up Demand Response by Following Local Energy Generation Voluntarily
Authors: Tobias Linnenberg, Alexander Fay, Michael Kaisers
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present an open-source low-budget hardware and software prototype of a smart plug, and the principles behind its capability to align power demand with a reference signal, e.g. from local renewable energy generation. We envision its use on a platform that combines social-media with energy networks, where users provide bottom-up demand response voluntarily. This article has two main objectives: 1. to illustrate the concept of voluntary demand response, and 2. to give researchers a tool to test behavioral flexibility assumptions by extending the open source prototype plug with their own concepts, e.g., implementing more complex autonomous decision-making. The proposed proof-of-concept interface of the Open Energy Exchange (OEEX) shows details of nearby generators and their operators, from which users select the preferred target. |
| Researcher Affiliation | Academia | Tobias Linnenberg, Alexander Fay Helmut-Schmidt-Universität, Hamburg {Tobias.Linnenberg, Alexander.Fay}@hsu-hh.de Michael Kaisers Centrum Wiskunde & Informatica, Amsterdam Michael.Kaisers@cwi.nl |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It provides schematic diagrams and circuit diagrams but no code-like procedures. |
| Open Source Code | Yes | Both hardware plans and software of the smart plug are open source3, lowering the threshold for researchers to become part of the system they are studying, which fosters realistic assumptions. 3Available on michaelkaisers.com. |
| Open Datasets | No | The paper mentions local measurements of consumption and reference signals from generators, but it does not describe the use of any specific named, publicly available dataset for training, nor does it provide a link or citation for one. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | Yes | The hardware consists of a Spark Core micro-controller board, a RECOM RAC06-05SC AC/DC-converter, an Allegro ACS714 current sensor breakout board and a relay for switching the connected load. We implemented the Dallas One-Wire bus interface on a digital I/O of the Spark Core to connect external sensors and actuators, in this case the Maxim temperature sensor DS18B20. |
| Software Dependencies | No | The paper mentions that "The smart plug software runs on a Spark Core, and the proof-of-concept system architecture is build around the private cloud solution provided by the Spark Cloud" and uses a "TI CC3000 Simple Link wifichip." However, it does not provide specific version numbers for these software components or related libraries. |
| Experiment Setup | No | The paper describes the smart plug's operational logic, such as switching decisions based on thresholds (e.g., minimum on-time, maximum internal temperature) for connected appliances. However, these are operational rules for the device rather than specific experimental setup details like hyperparameters, training configurations, or system-level settings typically found in research papers describing machine learning or simulation experiments. |