Conference Location Hyatt Regency Clearwater Beach Resort and Spa (website) 301 South Gulfview Blvd, Clearwater Beach, FL 33767, U.S. Apple Map Google Map
Ground Transportation Several options are available from Tampa International Airport (TPA) to Hyatt Regency Clearwater Beach.
Taxi Services – Yellow Cab and United Cab are available at the curbside outside the airport’s Baggage Claim Level. Both companies charge the same fares: $2.50 for the first 1/8 of a mile or part thereof, plus $2.40 per mile. The minimum fee from the airport is $19, with a flat rate to Hyatt Regency at $70.*
Premier Airport Transportation – Shared ride airport service via Lincoln Town cars, SUVs, minivans and large passenger vans. Reservations may be made online at https://premierairportlimo.com/ or by phone at +1 866-276-0882. Prices start at $79.95.*
Lyft – approximately $45,* depending on time of day. Estimate your fare on https://www.lyft.com/ride-with-lyft?. Follow provider instructions for pickup location. A service fee of $5.00 will be added to the cost of the fare.
Uber – Approximately $50,* depending on time of day. Estimate your fare on https://www.uber.com/fare-estimate/. Follow provider instructions for pickup location. A service fee of $5.00 will be added to the cost of the fare.
*Ground transportation rates as of August 2024 may be different at the time of booking.
Achieving carbon neutrality in industrial automation demands a holistic approach that integrates cutting-edge technologies and efficient energy management practices. This paper examines the essential automation requirements for attaining carbon neutrality, the current challenges in adopting CIP Energy, and a proposed reference architecture to address these issues.
To reach carbon neutrality, industrial automation systems must incorporate real-time energy monitoring, dynamic demand-response capabilities, and energy optimization algorithms. These systems should seamlessly integrate renewable energy sources, manage energy storage, and optimize energy consumption across all processes. Additionally, implementing predictive maintenance can minimize energy waste and enhance overall efficiency. CIP Energy offers fundamental building blocks for such solutions, meeting most of the necessary requirements.
However, implementing these solutions is not without challenges. The limited portfolio of devices supporting CIP Energy, the scarcity of compatible software applications, and the lack of knowledge and training among system designers, automation engineers, and end users pose significant obstacles.
To overcome these challenges, we propose a straightforward reference architecture that assumes partial to full CIP Energy implementation at the device level. This architecture outlines how to build energy-aware and dynamic demand-response capabilities at each layer, based on the degree of CIP Energy implementation, including workarounds to address gaps. It also discusses scaling the solution for larger implementations and cross-functional integration. The objective is to encourage the adoption of CIP Energy in devices and other OEM software, and to solidify the architecture through ODVA, potentially including necessary upgrades to the CIP Energy specification.
