research-article

Solar energy prediction for constrained IoT nodes based on public weather forecasts

Authors:

Frank Alexander Kraemer,

Anders Eivind Braten,

Nattachart Tamkittikhun,

David PalmaAuthors Info & Claims

IoT '17: Proceedings of the Seventh International Conference on the Internet of Things

Article No.: 2, Pages 1 - 8

https://doi.org/10.1145/3131542.3131544

Published: 22 October 2017 Publication History

Abstract

Solar power is important for many scenarios of the Internet of Things (IoT). Resource-constrained devices depend on limited energy budgets to operate without degrading performance. Predicting solar energy is necessary for an efficient management and utilization of resources. While machine learning is already used to predict solar power for larger power plants, we examine how different machine learning methods can be used in a constrained sensor setting, based on easily available public weather data. The conducted evaluation resorts to commercial IoT hardware, demonstrating the feasibility of the proposed solution in a real deployment. Our results show that predicting solar energy is possible even with limited access to data, progressively improving as the system runs.

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Cited By

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https://doi.org/10.1109/ICDCS60910.2024.00101
Inokuchi YKobayashi KGuillerault JHenmi YGonzalez PAritsugi MMendonca I(2024)Development of a Red Tide Early Detection System Using Satellite Images2024 International Conference on Green Energy, Computing and Sustainable Technology (GECOST)10.1109/GECOST60902.2024.10474956(159-163)Online publication date: 17-Jan-2024
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Khamitov ROrel DZorbas D(2024)Predicting Solar-Harvested Energy for Resource-Constrained IoT Devices Using Machine Learning2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)10.1109/DCOSS-IoT61029.2024.00103(661-668)Online publication date: 29-Apr-2024
https://doi.org/10.1109/DCOSS-IoT61029.2024.00103
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Published In

cover image ACM Other conferences

IoT '17: Proceedings of the Seventh International Conference on the Internet of Things

October 2017

211 pages

ISBN:9781450353182

DOI:10.1145/3131542

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 22 October 2017

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European Union

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IoT '17

IoT '17: 7th International Conference on the Internet of Things

October 22 - 25, 2017

Linz, Austria

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Overall Acceptance Rate 28 of 84 submissions, 33%

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Cited By

Fahmida SChavan AModekurthy VSaifullah ABrocanelli M(2024)A Battery Lifespan-Aware Protocol for LPWAN2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS60910.2024.00101(1050-1061)Online publication date: 23-Jul-2024
https://doi.org/10.1109/ICDCS60910.2024.00101
Inokuchi YKobayashi KGuillerault JHenmi YGonzalez PAritsugi MMendonca I(2024)Development of a Red Tide Early Detection System Using Satellite Images2024 International Conference on Green Energy, Computing and Sustainable Technology (GECOST)10.1109/GECOST60902.2024.10474956(159-163)Online publication date: 17-Jan-2024
https://doi.org/10.1109/GECOST60902.2024.10474956
Khamitov ROrel DZorbas D(2024)Predicting Solar-Harvested Energy for Resource-Constrained IoT Devices Using Machine Learning2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)10.1109/DCOSS-IoT61029.2024.00103(661-668)Online publication date: 29-Apr-2024
https://doi.org/10.1109/DCOSS-IoT61029.2024.00103
Macias JTrilles S(2024)Machine learning-based prediction model for battery levels in IoT devices using meteorological variablesInternet of Things10.1016/j.iot.2024.10110925(101109)Online publication date: Apr-2024
https://doi.org/10.1016/j.iot.2024.101109
Yuan ZGe YWei JYuan SLiu RMo X(2023)Energy Prediction for Energy-Harvesting Wireless Sensor: A Systematic Mapping StudyElectronics10.3390/electronics1220430412:20(4304)Online publication date: 18-Oct-2023
https://doi.org/10.3390/electronics12204304
Shin SYang H(2023)Estimation of Power Generation and Consumption based on eXplainable Artificial Intelligence2023 25th International Conference on Advanced Communication Technology (ICACT)10.23919/ICACT56868.2023.10079678(201-205)Online publication date: 19-Feb-2023
https://doi.org/10.23919/ICACT56868.2023.10079678
Rachna Singh A(2023)Prediction of Photovoltaic Power Generation using Machine Learning - A Review2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)10.1109/InCACCT57535.2023.10141769(1-5)Online publication date: 5-May-2023
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Kim JOh HChoi J(2022)Learning based cost optimal energy management model for campus microgrid systemsApplied Energy10.1016/j.apenergy.2022.118630311(118630)Online publication date: Apr-2022
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Adnan AMuhammed AAbd Ghani AAbdullah AHakim F(2021)An Intrusion Detection System for the Internet of Things Based on Machine Learning: Review and ChallengesSymmetry10.3390/sym1306101113:6(1011)Online publication date: 4-Jun-2021
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Cornetta GTouhafi A(2021)Design and Evaluation of a New Machine Learning Framework for IoT and Embedded DevicesElectronics10.3390/electronics1005060010:5(600)Online publication date: 4-Mar-2021
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