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Using environmental data for IoT device energy harvesting prediction

Using environmental data for IoT device energy harvesting prediction
Using environmental data for IoT device energy harvesting prediction
There has been significant innovation in the domain of Internet of Things (IoT) as nowadays wireless data transmission is playing an essential role in various organizations like agriculture, defence, transportation, etc. Batteries are the most common option to power wireless devices. However, using batteries to power IoT devices has drawbacks including the cost and disruption of frequent battery replacement, and environmental concerns about battery disposal. Solar energy harvesting is a promising solution for long-term operation applications. However, solar energy harvesting varies drastically over location and time. Due to fluctuating weather conditions and the environmental effects on PV surface condition, output could be reduced and become insufficient. Environmental conditions including temperature, wind, solar irradiance, humidity, tilt angle and the dust accumulated over time on the photovoltaic (PV) module surface affects the amount of energy harvested. To address this issue, a novel solution is required to autonomously predict the harvested energy and plan the IoT device tasks accordingly, to enhance its performance and lifetime. Using Machine Learning (ML) algorithms could make it possible to predict how much energy can be harvested using weather forecast data. This research is ongoing, and aims to apply ML algorithms on historical weather data including environmental factors to generate solar energy predictions for IoT device energy budget planning.
IoT, Energy Harvesting, Solar Energy Harvesting, Environmental Data, Weather Data, Machine Learning
Alzahrani, Mansour
149068df-d0fe-411f-bae8-41c9b00fefd9
Weddell, Alex S.
7fb964f4-6ca7-401d-8f57-f4355055eb8b
Gary, Wills
42d5a422-9ab1-4a73-ba10-57cda550a7f3
Alzahrani, Mansour
149068df-d0fe-411f-bae8-41c9b00fefd9
Weddell, Alex S.
7fb964f4-6ca7-401d-8f57-f4355055eb8b
Gary, Wills
42d5a422-9ab1-4a73-ba10-57cda550a7f3

Alzahrani, Mansour, Weddell, Alex S. and Gary, Wills (2022) Using environmental data for IoT device energy harvesting prediction. In IoTBDS - 7th International Conference on Internet of Things, Big Data and Security. 8 pp . (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

There has been significant innovation in the domain of Internet of Things (IoT) as nowadays wireless data transmission is playing an essential role in various organizations like agriculture, defence, transportation, etc. Batteries are the most common option to power wireless devices. However, using batteries to power IoT devices has drawbacks including the cost and disruption of frequent battery replacement, and environmental concerns about battery disposal. Solar energy harvesting is a promising solution for long-term operation applications. However, solar energy harvesting varies drastically over location and time. Due to fluctuating weather conditions and the environmental effects on PV surface condition, output could be reduced and become insufficient. Environmental conditions including temperature, wind, solar irradiance, humidity, tilt angle and the dust accumulated over time on the photovoltaic (PV) module surface affects the amount of energy harvested. To address this issue, a novel solution is required to autonomously predict the harvested energy and plan the IoT device tasks accordingly, to enhance its performance and lifetime. Using Machine Learning (ML) algorithms could make it possible to predict how much energy can be harvested using weather forecast data. This research is ongoing, and aims to apply ML algorithms on historical weather data including environmental factors to generate solar energy predictions for IoT device energy budget planning.

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Using Environmental Data for IoT Device Energy Harvesting Prediction - Author's Original
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More information

Accepted/In Press date: 24 February 2022
Keywords: IoT, Energy Harvesting, Solar Energy Harvesting, Environmental Data, Weather Data, Machine Learning

Identifiers

Local EPrints ID: 455839
URI: http://eprints.soton.ac.uk/id/eprint/455839
PURE UUID: 212d6654-1c0e-425b-8db3-9ad42d8baa80
ORCID for Mansour Alzahrani: ORCID iD orcid.org/0000-0002-0458-773X

Catalogue record

Date deposited: 06 Apr 2022 16:40
Last modified: 23 Jul 2022 02:27

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Contributors

Author: Mansour Alzahrani ORCID iD
Author: Alex S. Weddell
Author: Wills Gary

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