AI-Driven Predictive Analysis to Enhance Operational Efficiency of Sustainable Energy using Intelligent Energy Data Management platform

Abstract

Japan is currently grappling with the energy self-sufficiency challenge, risk of fluctuations and the hikes in electricity costs. This research aims to solve client concerns of scalability of data collection by IoT devices, complex development and operation of predictive AI, and scattered data across systems. In this research the development of the Intelligent Energy Data Management Platform (IDMP) called R.E.A.L., serving as a comprehensive energy management platform that seamlessly integrates with Amazon Web Services (AWS) infrastructure is carried out. By utilizing Amazon Timestream for time-series data and Amazon Sage Maker as the AI platform, the project achieves high scalability and performance thereby enhancing the operational efficiency. The results of this research showcase a stable platform supporting various applications, contributing significantly to i-Grid Solutions' rapid business promotion and overall vision of realizing a world filled with green energy. The successful collaboration highlights the potential for widespread adoption of similar green transformation applications using AI, affirming the platform's effectiveness in revolutionizing the circulation of the sustainable energy landscape which is one of its kind in Japan.