Bruk av PMI+AI+IoT for å fremme adopsjon av miljøvennlige praksiser blant hotelledere

Offentlig prosjektsammendrag

Most BMS systems operate on fixed schedules, not occupancy, leading to significant energy waste. Few hotels can afford modern demand-driven BMS, often costing over a million NOK. This project helps hotels align energy use with daily occupancy fluctuations by prompting timely actions and fostering behavioral change and accountability. It integrates real-time IoT sensor data, PMI’s ML-based occupancy forecasts, and an AI-driven sustainability best practice database. Key challenges include affordable IoT integration, accurate ML forecasting, effective staff engagement, and ensuring cost-effectiveness and scalability. The approach involves pilot testing, iterative AI and UI development, staff interaction analysis, energy savings evaluation, and adapting the system for broad hospitality use. By addressing these, the project advances eco-friendly, cost-efficient hotel operations, aligning with Skattefunn’s goals of innovation and sustainability.