AI Driven Energy Consumption & Environmental KPI Forecasting In Hospitals
DOI:
https://doi.org/10.64252/fgnfze48Keywords:
The phrases used in this study include AI-powered forecasting, energy use optimization, ecological KPIs, machine educating, time-collection forecasting, a medical institution sustainability, lowering CO2 emissions, data infrastructure, barriers to adoption of AI, sustainability objectives, Internet Zero 2070, ecological performance, healthcare energy management, Indian hospitals and cost effectiveness.Abstract
This research was aimed at evaluating the potential of the AI-based energy consumption forecasting and KPI optimization of the environment in hospitals with the emphasis on healthcare institutions in India. The study meant addressing the question of how artificial intelligence methods, particularly machine learning and time-series forecasting, might improve energy efficiency and environmental sustainability in the hospital industry. The study has employed a secondary qualitative research methodology whereby it relied on case studies and the available literature to obtain relevant information. The results showed that AI use, both in machine learning and time-series forecasting, were able to optimize energy consumption and positively affect other environmental KPIs, including CO2 emissions and water consumption. Nevertheless, problems related to data quality, infrastructure weaknesses and the high implementation costs were also regarded as essential impediments to adoption in the Indian hospitals. It was identified on the basis of these findings that Indian hospitals needed funding to help enhance data infrastructure, utilize scalable AI, and align their operations with national sustainability targets, such as Net Zero 2070.
