Integrating artificial intelligence into energy management: A case study on energy consumption data analysis and forecasting in a German manufacturing company

Abstract

The pressing need to enhance energy efficiency, as outlined by the United Nations within its Sustainable Development Goals, underscores the importance of reducing energy consumption in manufacturing companies. Energy management systems are essential in achieving this goal by systematically identifying inefficiencies and uncovering potential savings through the analysis of energy consumption data. With the growing utilisation of Artificial Intelligence (AI), there is significant potential to leverage advanced data analytics to predict future energy consumption and detect anomalies. However, current research focus on the theoretical development and refinement of AI models, a practical integration of AI into energy management systems within manufacturing companies remains limited, particularly in small and medium-sized enterprises (SMEs). This case study introduces a proof-of-concept implemented in a German manufacturing company to demonstrate how AI models can be integrated into energy management systems using existing data resources. Utilising the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, an AI-based evaluation algorithm was developed to detect changes in energy consumption patterns and predict future consumption. The findings reveal that AI models such as Long short-term memory enable the prediction of future energy consumption with remarkable accuracy as well as the identification of deviations that traditional systems might overlook. This study emphasises the transformative capacity of AI-driven energy management systems in enhancing operational efficiency and facilitating compliance with ISO 50001 standards. It provides a practical approach for broader adoption of intelligent data analytics in energy management, particularly for SMEs, aiming to pave the way towards a more sustainable industrial sector.