Solar Power Prediction in IoT Devices using Environmental and Location Factors

Conference proceedings article

Authors/Editors

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Publication Details

Author list: Mindang, Arnan; Siripongwutikorn, Peerapon

Publisher: Hindawi

Publication year: 2020

Start page: 119

End page: 123

Number of pages: 5

ISBN: 9781450377645

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090392597&doi=10.1145%2f3409073.3409086&partnerID=40&md5=35ccf2cfc0e8ba7f624b68e4d026d3a2

Languages: English-Great Britain (EN-GB)

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Abstract

Energy-harvesting IoT nodes need to conserve their energy to remain operating without interrupting. By predicting input power supply, IoT nodes could appropriately schedule or adjust data transmission interval to match available energy for lasting operations. In this work, we explore the effectiveness of using environmental and location factors, including light intensity, temperature, humidity, facing directions of a solar panel, as well as historical input power data to help predicting the solar input power of IoT nodes. Various time series and machine learning models including EWMA, WCMA, SARIMAX, and LSTM are fitted, tuned, and compared to determine significant factors and best-performing model. Our results reveal that the facing direction has a significant impact on the input power generated and model hyperparameters. Among the models investigated, SARIMAX yields the lowest prediction errors around 11%-26%. © 2020 ACM.

Keywords

solar power prediction