IoT based Smart Small Scale Solar Energy Planning using Evolutionary Fuzzy Association Rule Mining

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Wedashwara:2020:ICADEIS,

• author = "Wirarama Wedashwara and I {Wayan Agus Arimbawa} and Andy {Hidayat Jatmika} and Ariyan Zubaidi and Tatang Mulyana",
• title = "{IoT} based Smart Small Scale Solar Energy Planning using Evolutionary Fuzzy Association Rule Mining",
• booktitle = "2020 International Conference on Advancement in Data Science, E-learning and Information Systems (ICADEIS)",
• year = "2020",
• month = "20-21 " # oct,
• address = "Lombok, Indonesia",
• keywords = "genetic algorithms, genetic programming, EFARM, Solar Energy",
• isbn13 = "978-1-7281-8272-8",
• DOI = "doi:10.1109/ICADEIS49811.2020.9276905",
• size = "6 pages",
• abstract = "Along-Track Stereo Sun Glitter (ATSSG) shows Indonesia especially Lombok has high solar energy potential, not only on large scale but also small scale such as for hybrid-based electricity savings. The amount of energy that can be saved through solar power is difficult to predict without measurement and planning. The paper proposed the Smart Small Scale Solar Energy Planning using Internet of Things (IoT) by collaborating Wireless Sensor Network (WSN) as data collector and Evolutionary Fuzzy Association Rule Mining (EFARM) as Decision Support System (DSS). WSN collects data generated solar energy by the solar panel and direct current (DC) energy usage by electrical devices. Then both collected data are processed by EFARM through an interpretation of tree-based fuzzy rule extractor to conclude the potential of energy efficiency. The Evaluation is carried out for two weeks using two solar panels with light intensity, temperature, and humidity sensors as a comparison for environment condition and generated energy. Through evaluation EFARM has shown the capability to Interpreted patterns of generated energy and energy consumption by achieving a high average of supports(0.247,0.236), confidence(0.393,0.219) and scores(0.335,0.127) for full-length rules; describe the rules correlation between generated energy and energy consumption to conclude the potential of energy efficiency, and make decision support for the number of panels and batteries to be added with relatively low mean square error (6.094).",
• notes = "Also known as \cite{9276905}",

}

Genetic Programming entries for Wirarama Wedashwara I Wayan Agus Arimbawa Andy Hidayat Jatmika Ariyan Zubaidi Tatang Mulyana

Citations