| Solar energy generation and its strategies using artificial intelligence: |
| Paper ID : 1096-ISCHU |
| Authors |
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Nada Khaled Selim1, Rawan Ahmed Ibrahim1, Ghada Ragab Mansour1, Nada Shaaban Yousef2, Mariam Abdalkader Abdalazim *3 1Faculty of science, Helwan University 2Faculty of Science Helwan University 3Faculty of Science, Helwan university |
| Abstract |
| Solar power plants are crucial for sustainable energy production, but their performance can vary due to factors like location, equipment, and weather conditions. Analyzing the energy production data and environmental conditions of two solar power plants, Plant 1 and Plant 2, is essential for optimizing energy generation and making informed investment decisions. This use case aims to compare the two plants, identify patterns, assess performance under different conditions, and guide investment and maintenance strategies. Analyzing Energy Production: By examining the energy production data from Plant 1 and Plant 2, stakeholders can gain insights into each plant's performance. Metrics such as DC Power and AC Power provide information on the direct and alternating current outputs. Daily Yield and Total Yield values capture the daily and cumulative energy generation. This analysis helps identify variations in energy production between the two plants and highlights areas for improvement. Evaluating Environmental Conditions: Incorporating weather sensor data from both plants allows stakeholders to understand the factors influencing energy production. Ambient Temperature and Module Temperature readings indicate the environmental and equipment conditions at each plant. Irradiation reflects the solar exposure received by the panels. By studying these parameters, stakeholders can assess how environmental conditions affect energy generation in each plant and identify correlations between weather patterns and energy output. Directing Investments and Maintenance Activities: Comparative analysis of the two plants enables stakeholders to make informed decisions regarding investment and maintenance. By identifying variations in performance and energy generation, stakeholders can allocate resources effectively. Investments can be directed towards improving the underperforming plant or scaling up the more productive one. Additionally, understanding the impact of weather conditions on energy generation allows for optimized maintenance activities to enhance long-term plant performance. Fine-tuning Energy Production Strategies: The use case facilitates the development of energy production strategies by analyzing plant performance under specific weather scenarios. By correlating weather sensor data with energy production metrics, stakeholders can identify optimal weather conditions for maximum energy generation. This information can be used to fine-tune energy production strategies, such as adjusting panel orientation, optimizing cleaning schedules, or implementing energy storage solutions. Ultimately, these strategies contribute to maximizing the overall efficiency and output of the solar power plants. We will create a machine learning model that supports this “Part dedicated to artificial intelligence.” We will display that model by building a website and we will design a mobile application using Flutter that supports the Android and iOS operating systems. |
| Keywords |
| Solar energy, Machine learning, Sustainability, Computer science, physics, artificial intelligence , website, mobile application, flutter, energy |
| Status: Abstract Accepted (Poster Presentation) |