The current residential solar panels lack an adequate monitoring system, which hinders their optimal utilization. This research aims to design an Internet of Things (IoT) monitoring system and employ machine learning techniques to predict the current and voltage generated by solar panels. Experimental studies have revealed a correlation between dust accumulation and the current output of solar panels. The proposed system facilitates the prediction of the optimal time for cleaning solar panels.
ประเทศไทยนั้นเป็นประเทศในเขตร้อนที่เหมาะสมต่อการติดตั้งโซลาร์เซลล์เป็นอย่างมาก แต่เนื่องด้วยปริมาณฝุ่นเพิ่มขึ้นในทุกๆ ปี ซึ่งปริมาณฝุ่นนี้มีผลกระทบต่อแผงโซลาร์เซลล์ จึงมีแนวคิดในการพัฒนาระบบติดตามดูแลแผงสำหรับครัวเรือน ที่โดยส่วนใหญ่ไม่ได้มีการดูแลอย่างเหมาะสม
คณะบริหารธุรกิจ
This research aimed to develop the mixed tea from longan peels and seeds. Population studied were longan farmers who planted longan and preserved the longan product in Ampur Wang Nam Yen, Sa Kaeo Province. From the results, it was found that from By-product in the production of dehydrated longan, longan peels and seeds, which can be processed into ready-to-drink powdered tea. This not only helps reduce waste from the production process but also contributes to generating additional income from these by-products.
คณะเทคโนโลยีการเกษตร
Currently, urban agriculture is gaining increasing attention as it helps enhance food security and expand green spaces in cities. However, some people remain uninterested in urban farming, possibly due to living in urban areas or having limited space, making them perceive agriculture as something distant from their daily lives. The development of an urban agriculture card game aims to promote learning about urban farming through an engaging and enjoyable gameplay experience.
คณะเทคโนโลยีสารสนเทศ
This report is part of applying the knowledge gained from studying machine learning models and methods for developing a predictive model to identify customers likely to cancel their credit card services with a bank. The project was carried out during an internship at a financial institution, where the creator developed a model to predict customers likely to churn from their credit card services using real customer data through the organization's system. The focus was on building a model that can accurately predict customer churn by selecting features that are appropriate for the prediction model and the unique characteristics of the credit card industry data to ensure the highest possible accuracy and efficiency. This report also covers the integration of the model into the development of a website, which allows related departments to conveniently use the prediction model. Users can upload data for prediction and receive model results instantly. In addition, a dashboard has been created to present insights from the model's predictions, such as identifying high-risk customers likely to cancel services, as well as other important analytical information for strategic decision-making. This will help support more efficient marketing planning and customer retention efforts within the organization.