everWeather: A Low-Cost and Self-Powered AIoT Weather Forecasting Station for Remote Areas

Sofia Polymeni (), Georgios Spanos (), Dimitrios Tsiktsiris (), Evangelos Athanasakis (), Konstantinos Votis (), Dimitrios Tzovaras () and Georgios Kormentzas ()
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Sofia Polymeni: Centre for Research and Technology Hellas
Georgios Spanos: Centre for Research and Technology Hellas
Dimitrios Tsiktsiris: Centre for Research and Technology Hellas
Evangelos Athanasakis: Centre for Research and Technology Hellas
Konstantinos Votis: Centre for Research and Technology Hellas
Dimitrios Tzovaras: Centre for Research and Technology Hellas
Georgios Kormentzas: University of the Aegean

A chapter in Advances and New Trends in Environmental Informatics 2023, 2024, pp 141-158 from Springer

Abstract: Abstract Weather constitutes a crucial factor that impacts many of the human outdoor activities, whether they are related to obligations or pleasure. In the contemporary era, due to climate change, the weather is more unstable and the forecasting task is more challenging than ever. By combining the Internet of Things (IoT) with Artificial Intelligence (AI), a new research field emerges that is called Artificial Intelligence of Things (AIoT) and could offer significant possibilities for the research community in order to efficiently tackle the short-term weather forecasting. Renewable energy sources constitute solutions for the achievement of sustainability development goals and could also offer power autonomy in a weather forecasting station. In the present research study, everWeather is proposed as a low-cost, self-powered weather forecasting station based on the AIoT paradigm and renewable energy. The proposed solution combines a variety of low-cost environmental sensors, the prowess of solar energy and an appropriate lightweight Machine Learning (ML) algorithm such as the Multiple Linear Regression (MLR) in order to forecast physical weather for the next half hour. Preliminary experiments have been conducted for the proposed solution validation and the corresponding results highlighted that the performance of the everWeather station is quite satisfactory, in terms of reliability and forecasting accuracy.

Keywords: Internet of Things; Machine Learning; Weather forecasting; Artificial Intelligence of Things (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-46902-2_8

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