Comparative analysis of predictive modeling across key Domains: Insights and applications

Prediction is widely used for various purposes and in many fields of human activity. The techniques employed for making predictions are a subject of great scientific interest within the research community due to their diversity, level of accuracy, and adaptability to data. The challenge is to determine the factors that affect the choice of an optimal technique suited to each prediction objective. In this article, we conduct a review of models used in the literature to make predictions in different domains to understand the factors influencing the selection of a specific predictive model in relation to their areas of study. A comparative analysis of prediction techniques such as statistical algorithms, Data Mining, and Machine Learning has been performed. It follows that the selection of an adequate prediction technique for the best decision-making should take into account the projection horizon, uncertainty around the prediction, data availability and reliability, and the associated cost of prediction

Optimizing Sustainable Cultivation Through Smart Irrigation

This paper presents a comprehensive study of a predictive irrigation system, an innovative approach in smart agriculture focusing on integrated irrigation management through advanced predictive techniques. Employing a blend of Internet of things (IoT) technology, Machine Learning (ML) algorithms, and data analytics, this system marks significant improvements in agricultural irrigation strategies. It is designed to optimize water use, improve crop yields, and promote sustainable farming practices in the face of evolving environmental challenges. The paper outlines the system's architecture, including the deployment of IoT sensors for continuous data collection, the integration of ML models for predictive analysis, and the implementation of adaptive irrigation scheduling algorithms. A detailed examination in a study case of the system's performance reveals substantial improvements in water usage efficiency compared to traditional irrigation methods. Additionally, the paper discusses the challenges and limitations encountered, such as the high initial setup costs, technical complexities, and the necessity for continuous data accuracy. The study concludes by underscoring the Irrigation predictive system's potential in transforming agricultural practices. It highlights its role in enhancing resource management and sustainability in farming, while also pointing out the areas for future research to further refine of system for wider applicability

Optimiser la culture durable grâce à l’irrigation intelligente

This paper presents a comprehensive study of a predictive irrigation system, an innovative approach in smart agriculture focusing on integrated irrigation management through advanced predictive techniques. Employing a blend of Internet of things (IoT) technology, Machine Learning (ML) algorithms, and data analytics, this system marks significant improvements in agricultural irrigation strategies. It is designed to optimize water use, improve crop yields, and promote sustainable farming practices in the face of evolving environmental challenges. The paper outlines the system's architecture, including the deployment of IoT sensors for continuous data collection, the integration of ML models for predictive analysis, and the implementation of adaptive irrigation scheduling algorithms. A detailed examination in a study case of the system's performance reveals substantial improvements in water usage efficiency compared to traditional irrigation methods. Additionally, the paper discusses the challenges and limitations encountered, such as the high initial setup costs, technical complexities, and the necessity for continuous data accuracy. The study concludes by underscoring the Irrigation predictive system's potential in transforming agricultural practices. It highlights its role in enhancing resource management and sustainability in farming, while also pointing out the areas for future research to further refine of system for wider applicability.Ce travail présente une étude complète d'un système de prédiction de l'irrigation, une approche innovante dans l'agriculture intelligente axée sur la gestion intégrée de l'irrigation grâce à des techniques prédictives avancées. En utilisant un mélange de technologies d’Internet des objets, d'algorithmes d'apprentissage automatique et d'analyse de données, ce système apporte des améliorations significatives aux stratégies d'irrigation agricole. Il est conçu pour optimiser l'utilisation de l'eau, améliorer les rendements des cultures et promouvoir des pratiques agricoles durables face aux défis environnementaux en constante évolution. L'article présente l'architecture du système, y compris le déploiement de capteurs IoT pour la collecte continue de données, l'intégration de modèles d'apprentissage automatique pour l'analyse prédictive et la mise en œuvre d'algorithmes adaptatifs de planification de l'irrigation. Un examen détaillé dans une étude de cas de la performance du système révèle des améliorations substantielles de l'efficacité de l'utilisation de l'eau par rapport aux méthodes d'irrigation traditionnelles. De plus, l'article aborde les défis et les limitations rencontrés, tels que les coûts initiaux élevés de la mise en place, les complexités techniques et la nécessité d'une précision continue des données. L'étude conclut en mettant en avant le potentiel du système prédictif d'irrigation pour transformer les pratiques agricoles. Elle souligne son rôle dans l'amélioration de la gestion des ressources et de la durabilité de l'agriculture, tout en indiquant les domaines de recherche future pour affiner le système en vue d'une application plus large

Analyse comparative de la modélisation prédictive dans des domaines clés : regards et applications

Prediction is widely used for various purposes and in many fields of human activity. The techniques employed for making predictions are a subject of great scientific interest within the research community due to their diversity, level of accuracy, and adaptability to data. The challenge is to determine the factors that affect the choice of an optimal technique suited to each prediction objective. In this article, we conduct a review of models used in the literature to make predictions in different domains to understand the factors influencing the selection of a specific predictive model in relation to their areas of study. A comparative analysis of prediction techniques such as statistical algorithms, Data Mining, and Machine Learning has been performed. It follows that the selection of an adequate prediction technique for the best decision-making should take into account the projection horizon, uncertainty around the prediction, data availability and reliability, and the associated cost of prediction.La prédiction est largement utilisée à différentes fins et dans de nombreux domaines de l'activité humaine. Les techniques utilisées pour faire des prédictions sont un sujet de grand intérêt scientifique pour la communauté de recherche, compte tenu de leur diversité, de leur degré de précision et de leur adaptabilité autour des données. Le défi est de déterminer les facteurs qui affectent le choix d'une technique optimale adaptée à chaque objectif de prédiction. Dans cet article, nous réalisons un examen des modèles utilisés dans les travaux littéraires pour effectuer des prédictions dans différents domaines, afin de comprendre les facteurs influençant la sélection d'un modèle prédictif particulier en relation avec leurs domaines d'étude. Une analyse comparative des techniques de prédiction telles que les algorithmes statistiques, la fouille de données et l’apprentissage automatique a été réalisée. Il en résulte que la sélection d'une technique de prédiction adéquate à la meilleure prise de décision, doit considérer : l'horizon de projection, l'incertitude autour de la prédiction, la disponibilité et la fiabilité des données et le coût associé à la prédiction

DFIG-Based Wind Turbine with Shunt Active Power Filter Controlled by Double Nonlinear Predictive Controller

This paper presents a wind turbine based on the doubly fed induction generator (DFIG) connected to the utility grid through a shunt active power filter (SAPF). The whole system is controlled by a double nonlinear predictive controller (DNPC). A Taylor series expansion is used to predict the outputs of the system. The control law is calculated by optimization of the cost function. The first nonlinear predictive controller (NPC) is designed to ensure the high performance tracking of the rotor speed and regulate the rotor current of the DFIG, while the second one is designed to control the SAPF in order to compensate the harmonic produces by the three-phase diode bridge supplied by a passive circuit (rd, Ld). As a result, we obtain sinusoidal waveforms of the stator voltage and stator current. The proposed nonlinear predictive controllers (NPCs) are validated via simulation on a 1.5 MW DFIG-based wind turbine connected to an SAPF. The results obtained appear to be satisfactory and promising