Data Analytics Applications in Reducing the Emission Footprint of an Energy System

Abstract

With the data in the world approximating 64.2 zettabytes in 2020 and projecting to hit 180 zettabytes by 2025 (Zhang et al. 2018), data analytic has evolved overtime to be the backbone for any sector to generate valuable insights and make confident decisions. It has four main branches ranging from descriptive-analytic, diagnostic analytics, predictive analytics, and prescriptive analytics. Energy technologies are the primary sources powering the tremendous leaps in social and economic advancements throughout the decades. However, it has also adversely impacted the global climate and ecological systems largely through its emission footprint. With the pressure from ever-increasing energy demand and rallies for a positive climate change via cleaner energy generation and consumption, finding new energy resources and enhancing energy efficiency has become the priority of many nations in the twenty-first century. In this paper, we will aim to explore and analyze how data analytics through the four main pillars is playing a pivotal role in reducing the emission footprint of an energy system.