Version 1
: Received: 29 September 2024 / Approved: 29 September 2024 / Online: 30 September 2024 (14:41:13 CEST)
How to cite:
Emexidis, C.; Gkonis, P. On the Integration of Internet of Things and Machine Learning for Energy Prediction in the Wind Turbines. Preprints2024, 2024092351. https://doi.org/10.20944/preprints202409.2351.v1
Emexidis, C.; Gkonis, P. On the Integration of Internet of Things and Machine Learning for Energy Prediction in the Wind Turbines. Preprints 2024, 2024092351. https://doi.org/10.20944/preprints202409.2351.v1
Emexidis, C.; Gkonis, P. On the Integration of Internet of Things and Machine Learning for Energy Prediction in the Wind Turbines. Preprints2024, 2024092351. https://doi.org/10.20944/preprints202409.2351.v1
APA Style
Emexidis, C., & Gkonis, P. (2024). On the Integration of Internet of Things and Machine Learning for Energy Prediction in the Wind Turbines. Preprints. https://doi.org/10.20944/preprints202409.2351.v1
Chicago/Turabian Style
Emexidis, C. and Panagiotis Gkonis. 2024 "On the Integration of Internet of Things and Machine Learning for Energy Prediction in the Wind Turbines" Preprints. https://doi.org/10.20944/preprints202409.2351.v1
Abstract
Wind power has emerged as a crucial substitute for conventional fossil fuels. The combination of advanced technologies such as the internet of things (IoT) and machine learning (ML) has given rise to a new generation of energy systems that are intelligent, reliable, and efficient. The wind en-ergy sector utilizes IoT devices to gather vital data, subsequently converting them into practical insights. The aforementioned information aids among others in the enhancement of wind turbine efficiency, precise anticipation of energy production, optimization of maintenance approaches, and detection of potential risks. In this context, the main goal of this work is to combine the IoT with ML in the wind energy sector by processing weather data acquired from sensors to forecast wind power generation. To this end, three different regression models are evaluated. The models under comparison include Linear Regression, Random Forest, and Lasso Regression, evaluated us-ing metrics such as coefficient of determination (R²), adjusted R², mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). After examining a dataset from IoT devices that included weather data, the models provided substantial insights regarding their ca-pabilities and responses to preprocessing, as well as each model's reaction in terms of statistical performance deviation indicators. Ultimately, the preprocessing and the data analysis show that Random Forest regression is more suitable for weather condition datasets than the other two re-gression models. Both the advantages and shortcomings of the three regression models indicate that their integration with IoT devices will facilitate successful energy forecasting.
Keywords
internet of things; machine learning; data analysis; regression analysis
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
