KMITL Innovation Expo 2025 Logo

Solar Panel Dust Monitoring System

Abstract

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.

Objective

ประเทศไทยนั้นเป็นประเทศในเขตร้อนที่เหมาะสมต่อการติดตั้งโซลาร์เซลล์เป็นอย่างมาก แต่เนื่องด้วยปริมาณฝุ่นเพิ่มขึ้นในทุกๆ ปี ซึ่งปริมาณฝุ่นนี้มีผลกระทบต่อแผงโซลาร์เซลล์ จึงมีแนวคิดในการพัฒนาระบบติดตามดูแลแผงสำหรับครัวเรือน ที่โดยส่วนใหญ่ไม่ได้มีการดูแลอย่างเหมาะสม

Other Innovations

il n'y a rien à faire

คณะสถาปัตยกรรม ศิลปะและการออกแบบ

il n'y a rien à faire

This artwork was created based on the universal concepts of global warming and post-apocalyptic world, which has caused disturbances and chaos in ecosystems, leading to the extinction of many living beings on Earth due to human actions. Repairing and restoring this world may therefore be a false hope, connected to my personal experience of losing loved ones and the sorrow from setting high hope, through the artistic process using Animation Art and Sound Art.

Read more
Graphic design for vending machine

คณะสถาปัตยกรรม ศิลปะและการออกแบบ

Graphic design for vending machine

Design a graphic concept for a vending machine and its surrounding area (5x6 meters) featuring INGU skincare products

Read more
In Silico Drug Discovery of Emerging Immune Checkpoint TIGIT-Binding Compounds for Cancer Immunotherapy: Computational Screening, Docking Studies, and Molecular Dynamics Analysis

คณะวิทยาศาสตร์

In Silico Drug Discovery of Emerging Immune Checkpoint TIGIT-Binding Compounds for Cancer Immunotherapy: Computational Screening, Docking Studies, and Molecular Dynamics Analysis

Cancer remains a major global health challenge as the second-leading cause of human death worldwide. The traditional treatments for cancer beyond surgical resection include radiation and chemotherapy; however, these therapies can cause serious adverse side effects due to their high killing potency but low tumor selectivity. The FDA approved monoclonal antibodies (mAbs) that target TIGIT/PVR (T-cell immunoglobulin and ITIM domain/poliovirus receptor) which is an emerging immune checkpoint molecules has been developed; however, the clinical translation of immune checkpoint inhibitors based on antibodies is hampered due to immunogenicity, immunological-related side effects, and high costs, even though these mAbs show promising therapeutic efficacy in clinical trials. To overcome these bottlenecks, small-molecule inhibitors may offer advantages such as better oral bioavailability and tumor penetration compared to mAbs due to their smaller size. Here, we performed structure-based virtual screening of FDA-approved drug repertoires. The 100 screened candidates were further narrowed down to 10 compounds using molecular docking, with binding affinities ranging from -9.152 to -7.643 kcal/mol. These compounds were subsequently evaluated for their pharmacokinetic properties using ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis, which demonstrated favorable drug-like characteristics. The lead compounds will be further analyzed for conformational changes and binding stability against TIGIT through molecular dynamics (MD) simulations to ensure that no significant conformational changes occur in the protein structure. Collectively, this study represents the potential of computational methods and drug repurposing as effective strategies for drug discovery, facilitating the accelerated development of novel cancer treatments.

Read more