Wind Power Plant Site Selection using Integrated Machine Learning and Multiple-Criteria Decision Making Technique

Abstract

The growing demand for clean and sustainable energy sources has driven countries around the world to explore renewable energy options, including wind power. This research focuses on the use of machine learning techniques to optimize the site selection process for wind power plants in the Philippines. The study aims to address the challenge of identifying suitable locations for wind power plant development, which requires the assessment of various environmental and socio-economic factors. The research utilizes various datasets, including wind speed and direction, topography, land use, population density, and infrastructure availability. Additionally factors on The datasets was acquired to the Maps that contains road network, urban areas, protection areas, slope, wind speed, water courses, natural disasters and transmissions lines. These datasets are processed and analysed using SVM machine learning algorithms to identify the most suitable sites for wind power plant development. The study results indicate that machine learning techniques can provide a more accurate and efficient approach to wind power plant site selection compared to traditional methods. The model can identify areas with high potential for wind energy generation, taking into account various factors that influence the feasibility and profitability of wind power plant development. The research findings are expected to provide valuable insights for policymakers, investors, and other stakeholders involved in the renewable energy sector in the Philippines. The use of machine learning techniques can facilitate the identification of optimal locations for wind power plants, leading to more efficient and effective renewable energy development in the country.