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Despite the increasing attention on smart farms as a solution to rural issues such as aging agricultural populations, a shortage of young farmers, decreased production area, and reduced investment leading to stagnant income, exports, and growth rates, many farms still rely on traditional methods like cultivating tangerines in open fields. Despite this, increasing farm income requires producing high-quality tangerines and selling them at premium prices, with fruit sweetness being a crucial factor. Therefore, there is a need to examine the close correlation between tangerine quality and sweetness. In this paper, we use deep learning with the PyCaret library to predict and analyze tangerine sweetness using data from seven regions in Jeju and 13 comprehensive factors influencing sweetness, including terrain, temperature, humidity, precipitation, sunlight, wind speed, acidity, sugar-acid ratio, and others. Although applying all 13 factors could achieve over 90% accuracy, our study, limited to seven factors, still achieves a respectable 82.4% prediction accuracy, demonstrating the significant impact of weather data on sweetness. Moreover, these optimistic predictions enable the estimation of tangerine quality and price formation in the market for the coming year, allowing tangerine farmers and related agencies to respond to market conditions proactively. Furthermore, by applying these data to smart farms to control factors influencing tangerine sweetness, it is anticipated that high-quality tangerine production and increased farm income can be achieved. [ABSTRACT FROM AUTHOR]

Demand for electric kickboards is increasing specifically in tourist-centric regions worldwide. In order to gain a competitive edge and to provide quality service to customers, it is essential to properly deploy rental electric kickboards (e-kickboards) at the time and place customers want. However, it is necessary to study how to divide the region to predict electric mobility demand by region. Therefore, this study is made to more accurately predict future demand based on past regional customers' electric mobility demand data. We have proposed a novel electric kickboard demand prediction in spatiotemporal dimension using clustering-aided bagging regressor. We have used electric kickboard usage data from a Jeju, South Korea-based company. As a result of the experiment, it was found that the accuracy before using clustering-based bagging regressor and when the region was divided by the clustering method, the performance was improved, and we have achieved a regression score (R[sup.2]) of 93.42 using our proposed approach. We have compared our proposed approach with other state-of-the-art models, and we have also compared our model with different other combinations of bagging regressors. This study can be helpful for companies to meet the user's demand for a better quality of service., Author(s): Prince Waqas Khan [1]; Se-Joon Park [1]; Sang-Joon Lee (corresponding author) [1]; Yung-Cheol Byun (corresponding author) [1] 1. Introduction With the increasing demand for fuel-efficient vehicles, due to growing [...]

Wind turbines are becoming increasingly important in the generation of clean, renewable energy worldwide. To ensure their dependable and accessible operation, advanced real-time condition monitoring technology must be implemented to guarantee efficient wind power generation and financial viability. Machine learning (ML) has emerged as a crucial technique for condition monitoring in wind power systems in recent years. This is especially relevant because dedicated condition monitoring systems, primarily focused on vibration measurements, are prohibitively expensive. Preventive maintenance is the most effective way to detect and address issues before they impact performance. This article provides a comprehensive and up-to-date review of the latest condition monitoring technologies for fault detection, diagnosis, and prognosis in wind turbines, with a particular focus on ML algorithms for critical faults and failure modes, preprocessing methods, and evaluation metrics. Numerous references have been analyzed to evaluate past, present, and potential future research and development trends in this field. Most of these references are based on recent journal articles, theses, and reports found in the open literature. [ABSTRACT FROM AUTHOR]

Introduction: Distributed power supply has increasingly taken over as the energy industry's primary development direction as a result of the advancement of new energy technology and energy connectivity technology. In order to build isolated island microgrids, such as villages, islands, and remote mountainous places, the distributed power supply design is frequently employed. Due to government subsidies and declining capital costs, the configured capacity of new energy resources like solar and wind energy has been substantially rising in recent years. However, the new energy sources might lead to a number of significant operational problems, including over-voltage and ongoing swings in the price of power. Additionally, the economic advantages availed by electricity consumers may be impacted by the change in electricity costs and the unpredictability of the output power of renewable energy sources. Methods: This paper proposes a novel framework for enhancing renewable energy management and reducing the investment constraint of energy storage. First, the energy storage incentive is determined through a bi-level game method. Then, the net incentive of each element is maximized by deploying a master-slave approach. Finally, a reward and punishment strategy is employed to optimize the energy storage in the cluster. Results: Simulation results show that the proposed framework has better performance under different operating conditions. Discussion: The energy storage operators and numerous energy storage users can implement master-slave game-based energy storage pricing and capacity optimization techniques to help each party make the best choices possible and realize the multi-subject interests of energy storage leasing supply and demand win-win conditions. [ABSTRACT FROM AUTHOR]