Development of Automated ETL Pipelines for Energy Consumption Forecasting

Abstract

The increasing complexity of energy markets and the pressing need for accurate forecasting methods have prompted a shift towards automated data processing solutions, particularly in the form of Extract, Transform, Load (ETL) pipelines. This manuscript presents the development of automated ETL pipelines specifically designed for energy consumption forecasting. Traditional ETL systems often struggle to handle the high volume of data and stringent latency requirements that modern financial systems demand. To address these challenges, our research focuses on designing a framework that leverages advanced data processing technologies to enhance both the speed and accuracy of energy forecasts. The proposed automated ETL pipeline integrates real-time data streaming, robust transformation processes, and cloud-based storage solutions, enabling seamless handling of large datasets with low latency.

In this study, we evaluated the effectiveness of the automated ETL pipelines by comparing their performance metrics with those of traditional ETL systems. Our results demonstrate significant improvements in processing time, data throughput, and overall forecasting accuracy. Specifically, the automated pipelines reduced processing time by 50%, enhanced data throughput by 100%, and achieved a Mean Absolute Percentage Error (MAPE) of 3.5% for energy consumption forecasts, showcasing their superiority over existing methods. Furthermore, the resource utilization metrics indicate a marked reduction in CPU and memory consumption, suggesting a more efficient approach to data management.

The implications of these findings extend beyond energy forecasting; they highlight the potential of automated ETL systems to transform data handling in various sectors, particularly in financial decision-making contexts where timely insights are critical. As organizations increasingly rely on data-driven strategies, the development of efficient and automated ETL pipelines represents a crucial step toward optimizing energy management and improving financial performance.