A Study on the Implementation of Dynamic Pricing Mechanisms for Sustainable Energy Management Using AI-Driven Demand Prediction

The pressing need for sustainable energy management drives the exploration of innovative strategies to optimize energy consumption patterns and alleviate strain on the grid. Investigating the effectiveness of dynamic pricing mechanisms, underpinned by AI-driven demand prediction models, we aim to incentivize consumers to adjust energy usage, thereby promoting energy efficiency and grid stability. In Japan, challenges in energy self-sufficiency and fluctuating demand necessitate inventive solutions, leading us to focus on integrating AI, Big Data, and IoT technologies to enhance smart clean energy management and demand prediction. This research introduces a comprehensive Intelligent Energy Data Management Platform (IDMP) called R.E.A.L., seamlessly integrating with Amazon Web Services (AWS) to utilize Amazon Timestream for time-series data and Amazon SageMaker for AI. At the core of our research lies the optimization of energy consumption through AI-driven demand prediction, aligning with principles of the circular economy. By accurately predicting energy demand and supply, our approach enhances energy efficiency and resource utilization, contributing to sustainability. Through historical data analysis and machine learning algorithms, waste is minimized, and reliance on fossil fuels reduced, thus advancing principles of the circular economy. Overall, this research revolutionizes sustainable energy management by improving operational efficiency, balancing supply and demand, and optimizing resource consumption. The study demonstrates the stability and versatility of the R.E.A.L. platform, offering valuable insights for stakeholders in the energy sector.