Predict and visualise energy consumption with ML and AI for telecom giants

Accurate prediction and visualization of energy use has become very important for organizations today and especially with the usage of ML and AI models, provides many advantages. Firstly, it reduces operating costs by optimizing energy supply and consumption. Organizations comply with sustainability goals by identifying inefficiencies and implementing energy efficiency initiatives. Forecasting energy demand supports strategic resource planning, ensuring operational requirements are met. Understanding consumption patterns ensures uninterrupted operations by reducing risks associated with shortages or surpluses.

Forecasting and visualizing energy consumption for a global telecommunications giant is a complex task due to the large amounts of data involved, variable consumption patterns, and regulatory compliance requirements. It requires advanced analytics, Machine Learning (ML), and real-time monitoring to optimize energy use while maintaining quality of service and meeting sustainability goals.

Within just 8 weeks, our team embarked on a rapid yet comprehensive journey to develop a Machine Learning (ML) and Generation AI (Gen AI) model tailored specifically to forecast energy consumption across approximately 24,000 sites. We used techniques including random forest and XGBoost models, we delved deep into the data realm, employing advanced strategies such as hyper-parameter optimization, meticulous data cleaning, strategic data augmentation, and intricate feature engineering. Through these rigorous processes, we achieved an impressive accuracy rate surpassing 86%, ensuring that our predictive model delivered reliable insights for effective energy management.