Big data provide valuable insights by offering diverse information and sophisticated analysis through advanced algorithms. However, its huge volume, variety, and speed present significant challenges for effective computing. To address these, this study applies a Multi-Criteria Decision-Making (MCDM) framework to manage spatial big data, specifically in new green applications. The paper introduces a robust MCDM framework using big data, designed to address renewable energy challenges within the environmental sector. This framework systematically prioritizes and evaluates large environmental datasets, incorporating economic, environmental, and social factors. This framework is especially efficient and reliable for green energy initiatives. Moreover, a pre-processing step extracts key features to enable high-performance efficient analysis and visualization. Results show that the framework improves accuracy by 18% compared to conventional single-criterion data analysis approaches in a large-scale case study and provides system managers with an interactive 3D visualization tool to enhance decision making process in big data environmental management.

The integration of solar farms into power networks necessitates a comprehensive analysis of various parameters and conditions to optimize performance and mitigate risks. This paper aims to enhance the deployment and efficiency of solar energy systems by addressing several key aspects. Initially, critical parameters related to Direct Normal Irradiance (DNI), essential for solar energy harvesting, are identified. The impact of natural hazards on solar farms is assessed using Genetic Landscape Evolution (GLE). Additionally, the topographic position index is employed to identify low-risk areas for installing solar panels, ensuring both safety and optimal performance. Edge-assisted local processing is considered to support data handling and preliminary analysis at the site, facilitating more efficient information management. Machine learning techniques, including Support Vector Regression (SVR) and convolutional neural networks, are implemented to forecast DNI. The performance of solar panels is analyzed, considering various environmental and operational factors. The results indicate that principal component analysis reveals elevation as a significant topographical factor influencing DNI production in semi-arid areas. The GLE method shows favorable stability in areas prone to erosion, supporting the feasibility of solar panel installations in the southeastern part of the study area. Moreover, SVR proves to be an accurate method for forecasting DNI (correlation coeffient R = 0.98). The performance assessment indicates a final yield of 179.3 kW·h/kWp (kWp means kilowatt-peak) in August and the highest reference yield of 1195.47 kW·h/kWp, demonstrating the effectiveness of this approach. The findings of this paper provide valuable insights for the development of resilient and efficient solar power networks, contributing to the advancement of renewable energy technologies.