Volume 05: Proceedings of 11th International Conference on Applied Energy, Part 4, Sweden, 2019

Geospatial, Statistical Approach for Multi-Criteria Analysis of Renewable Energy Potential: A Case Study on Japan’s Onshore Wind Kenji Shiraishi, Daniel M. Kammen

Abstract

A new geospatial multi-criteria decision analysis method with spatial regression was proposed and implemented to identify Japan’s high-quality onshore wind energy potential in 2030. After identifying the economic potential of grid-connected onshore wind with a GIS-based multicriteria method, logistic regression and Conditional Autoregressive (CAR) regression was used to create a predictive model of development probability and evaluated with ROC curve. Other than economic costs, the model showed other physical, environmental, social factors, and spatial heterogeneity are incorporated to rank the overall quality of potential. The results also showed far more high-quality onshore wind potential exists in Japan than the 10 GW target in 2030.

Keywords renewable energy, geospatial multi-criteria decision analysis, spatial regression, environment and climate change, capacity expansion planning, Asia

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