Energy management in hospitality using AI models

OPT-e-FLOW: OPTimized Energy Flow

From Energy Data to Optimized Energy Decisions

OPT-e-FLOW (OPTimized Energy Flow) is a collaborative R&D initiative aimed at the design and development of an AI-driven energy forecasting and decision-support platform for intelligent energy planning and optimization. The project seeks to advance the state of the art in time-resolved energy management by combining multi-year historical energy consumption data with high-resolution climate and weather information to predict future energy demand with increased accuracy and temporal detail (hourly, daily, and seasonal).

The core objective of OPT-e-FLOW is to develop and validate algorithms and models capable of translating these predictions into actionable insights for the optimized utilization of renewable energy sources (RES) and energy storage systems. The project will investigate methods for forecasting-driven energy flow optimization, including strategies for demand smoothing, peak reduction, and enhanced self-consumption, under realistic operational constraints.

Beyond operational optimization, OPT-e-FLOW aims to develop a planning and assessment framework for cases where renewable generation or storage infrastructure is partially available or entirely absent. In such scenarios, the platform will be designed to quantify future energy needs, identify supply gaps, and simulate alternative RES and storage configurations. The outcome will be a set of data-driven recommendations regarding the type, sizing, and capacity of the required systems, supported by techno-economic indicators such as expected performance, investment requirements, and potential cost and emissions reductions.

The project is conceived as a modular and scalable research platform, with a strong focus on real-world applicability and future industrial deployment. Target use cases include industrial facilities, commercial buildings, microgrids, and energy communities. Through close collaboration with industrial partners, OPT-e-FLOW aims to co-develop, test, and validate its methodologies using realistic datasets and operational scenarios, laying the foundation for a future industry-ready solution that supports the transition toward data-driven, low-carbon energy systems.