A No-Code Automated Machine Learning Platform for the Energy Sector

Abstract

The energy sector is increasingly leveraging machine learning (ML) to enhance efficiency, sustainability, and decision-making processes. However, the complexity of ML model development and deployment poses significant challenges, particularly in adapting to diverse data environments and operational requirements. This paper presents the development of a No-Code Automated ML (Auto-ML) platform specifically tailored for the energy industry. We introduce a framework that automates critical steps in the ML pipeline, including data preprocessing, feature engineering, model selection, and hyperparameter tuning, while integrating domain-specific knowledge to handle unique industry challenges such as fluctuating energy demands and regulatory compliance. The platform employs a modular architecture that allows for customization and scalability, facilitating its adaptation across various energy sub-sectors, including renewable energy, oil and gas, and power distribution. The results underline the platform's potential to democratize advanced analytical capabilities within the energy industry, making sophisticated data-driven insights accessible even to non-expert users. This paper contributes to the field by detailing the architectural considerations, implementation challenges, and practical impacts of Auto-ML in a sector critical to global infrastructure and economic stability.