# The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines
## 1 Introduction  
Given the increasing environmental concerns and the need to shift to sustainable energy sources, wind power has emerged as a significant and feasible alternative to conventional fossil fuels [1]. Wind farms, comprised of a collection of wind turbines, have become essential structures in the worldwide effort to provide environmentally friendly and sustainable energy [2]. The increasing interest in wind energy coincides with a crucial phase in the energy sector, marked by the integration of advanced technologies like the internet of things (IoT) and machine learning (ML). These state-of-the-art technologies are not simply enhancing the abilities of current energy systems; they are completely transforming them. They pledge to introduce a new era marked by intelligent, reliable, and extremely efficient energy systems that not only improve performance but also tackle urgent energy issues worldwide. The wind energy sector is progressively utilizing IoT devices to collect vital data, playing a key role in this technological transformation [3]. The features may encompass parameters like wind speed, wind direction, temperature, humidity, air pressure, and metrics related to the health of the turbine. These variables offer crucial insightsinto the elements that affect wind power output. Once gathered and examined, these data act as a catalyst for enhancing the effectiveness of wind turbines, precisely predicting energy generation, simplifying maintenance approaches, and identifying possible hazards. Furthermore, the instantaneous transfer of sensor measurements to distant control centers enables uninterrupted surveillance, although it presents certain difficulties, particularly with real-time control at the system and component levels. Currently, supervisory control and data acquisition (SCADA) systems offer advanced services and capabilities for wind energy conversion systems (WECS) that go beyond basic monitoring and management of wind turbines, utilizing meteorological observations and weather data to predict wind power generation [1]. Thoughtless usage of IoT devices can introduce unforeseen costs. A cost-effective strategy can address crucial energy and infrastructure costs incurred by IoT integration [4]. This article provides foundational insights into IoT applications in wind energy systems, incorporating data analysis and ML through three different regression models. The preprocessing phase encompasses outlier removal, data transformation, correlation analysis, cross-validation, standardization and data splitting, all of which prepare the dataset for ML processing. To this end, an innovative approach is presented that involves preprocessing followed by Linear, Random Forest, and Lasso regression, yielding valuable results. These regression models are the basis for ML-assisted prediction. The objective is to demonstrate how the utilization of these technologies can significantly enhance the efficiency, reliability, and ecological sustainability of wind energy systems, thereby facilitating the global transition towards a more sustainable and environmentally conscious energy sector. Research ContextIn recent years, numerous scientific works have dealt with the deployment of IoT in wind energy and the prediction of energy output. The work in [3] examines the implementation of IoT in wind farms and the broader energy sector. It asserts that the incorporation of IoT into wind farms enhances the existing 743 GW worldwide wind power capacity to a sufficient level to provide 20% of the world's electricity by 2030. In the same context, the obstacles related to the implementation of IoT in wind farms and the prospective role of blockchain technology and green IoT in energy systems are discussed as well. In [5], an IoT-based communication architecture is proposed to ensure a reliable connection between wind turbines and the control center, utilizing repeat-accumulate coded communication to improve reliability.