Solar Power Prediction in IoT Devices using Environmental and Location Factors

Conference proceedings article

ผู้เขียน/บรรณาธิการ

กลุ่มสาขาการวิจัยเชิงกลยุทธ์

รายละเอียดสำหรับงานพิมพ์

รายชื่อผู้แต่ง: Mindang, Arnan; Siripongwutikorn, Peerapon

ผู้เผยแพร่: Hindawi

ปีที่เผยแพร่ (ค.ศ.): 2020

หน้าแรก: 119

หน้าสุดท้าย: 123

จำนวนหน้า: 5

ISBN: 9781450377645

นอก: 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

ภาษา: English-Great Britain (EN-GB)

ดูบนเว็บไซต์ของสำนักพิมพ์

บทคัดย่อ

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.

คำสำคัญ

solar power prediction