Deployment of Renewable Energy Sources: Empirical Evidence in Identifying Clusters with Dynamic Time Warping

Deployment of renewable energy sources has caused a seismic shift in the world energy arena. Individual and coordinated efforts across countries and regions are shaping the world for the future, including business models which are supported globally to achieve net zero goals by 2050. In this paper, our aim is to identify clusters of countries at different levels of deployment of renewable energy sources. We propose a time series clustering method capturing the time-varying features of the renewable energy time series of 130 countries to enable the assessment of how similar or how different the usage is in relation to the Organisation for Economic Co-operation and Development (OECD) status of countries, their regional location and their income levels. We use Dynamic Time Warping (DTW) which is a method that calculates an optimal match between two given time series with certain restrictions. Using DTW, we the adopt the Partitioning Around Medoids (PAM) technique in a fuzzy framework to obtain cluster solutions. Our analysis shows that a 4-cluster solution best captures country separation based on OECD status, regional location and income grouping. Since renewable energy clusters share common advantages and difficulties in terms of transferring the key characteristics in deploying renewable energy resources across countries and regions, grouping together of countries could capture similarities or differences that has the potential to lead to common decisions that could benefit the countries in the same group.