What is it about?
This study assists city planners and engineers in determining the optimal locations for solar power plant development in city. The goal is to harness the city's sunlight to generate clean energy efficiently and cost-effectively. To achieve this, the research utilizes satellite data and innovative computer tools, such as Artificial Neural Networks (ANN) and Gene Expression Programming (GEP), to analyze factors including sunlight, temperature, rainfall, land type, and the distance of areas from cities. By comparing the two computer models, the study found that the GEP model gives more accurate results. The analysis revealed that approximately 9% of the area is ideal for building solar power plants, while around 53% is moderately suitable, and 37% is unsuitable. This type of analysis can help governments and energy companies determine where to invest in solar energy, which is crucial for reducing fossil fuel use and combating climate change.
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Why is it important?
This research is significant because it contributes to the global shift toward clean and renewable energy. As cities grow and energy demands rise, relying on fossil fuels leads to pollution and exacerbates climate change. Solar energy offers a sustainable solution, but not every location is equally suitable for solar power. By utilizing advanced tools to carefully identify the optimal locations for solar power plant installations in a city, this study helps prevent the wastage of resources on suboptimal sites. It ensures that solar projects are built where they will be most effective and efficient, saving money, maximizing energy output, and minimizing environmental impact. In short, this work helps decision-makers plan smarter, greener cities that use the sun’s energy wisely, benefiting people, the economy, and the planet.
Perspectives
From my perspective, this publication represents a meaningful contribution to the future of sustainable urban development. As someone deeply involved in architecture, planning, and sustainability, I believe that integrating technology, such as machine learning and GIS, into renewable energy planning is not just innovative but necessary. What excites me most is how this study bridges scientific modeling with real-world decision-making. The application of GEP and ANN extends beyond theory, providing practical tools that cities like Lima—and others worldwide—can utilize to optimize solar energy deployment. This approach supports environmental goals and enhances energy equity by guiding infrastructure investment in an innovative, data-driven way. I view this work as part of a larger mission to make cities more resilient and energy-independent. It underscores our responsibility to utilize every available tool to design better futures, grounded in both technology and sustainability.
Ngakan Ketut Acwin Dwijendra
Universitas Udayana
Read the Original
This page is a summary of: Optimal Location to Use Solar Energy in an Urban Situation, Computers Materials & Continua, January 2023, Tsinghua University Press,
DOI: 10.32604/cmc.2023.034297.
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