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YOUR GATEWAY TO THE HVACR INDUSTRY IN THE MIDDLE EAST 2026 ccme.news/digitalwww.climatecontroldirectory.com 2026 A READY REFERENCE ON HVACR MANUFACTURERS IN THE MIDDLE EAST •Brain meets Chill •GCC region refrigerant transition report •The transformative power of electricity-free cooling INSIGHTS Also available online: www.climatecontroldirectory.com PUBLICATION LICENSED BY IMPZ US$ ccme.news/digital Product-wise listing Company-wise listingPRESENTS 05 MARCH 2026 | MUMBAI INDIA EDITION ST www.climatecontrolawards.com/indiaWELCOME CPI Industry, Publishers of Climate Control Middle East, are delighted to present the 2026 edition of the YOUR GATEWAY TO THE HVACR INDUSTRY IN THE MIDDLE EAST 2026Nearly there Dear Colleagues: Welcome to the 18 th Edition of the Climate Control Guide & Directory (CCG&D), just two short of a landmark, the prospect of which we are excited about. The vast number of editions speaks of longevity and relevance in a world that is quite unrecognisable, when viewed in the context of 2008, the year of the first edition of this book. We mean, we are living in a world where Artificial Intelligence is increasingly occupying the HVACR landscape, and it has an influence on so many disciplines and sub-disciplines that this book contains in the forms of products and services. We hope you find the product-wise and company-wise listings this book contains useful. We equally hope you discover the thought-leadership articles as containing invaluable insights. Do let us know what you think and, more importantly, areas we can improve further. You probably already know this, but the contents of this book are also available at www.climatecontroldirectory.com, for your convenience. Kind Regards, The Team at CPI Industry Disclaimer: CPI Industry is not responsible for, or guarantees, the safety, reliability, energy efficiency, IEQ-enhancing properties, etc., of the products listed in this book; the onus is on the purchaser to carry out necessary evaluation measures before arriving at a buying decision. Editor Surendar Balakrishnan surendar@cpi-industry.com Editorial & Admin Assistant Ika Maryawati admin@cpi-industry.com Advertising Enquiries Frédéric Paillé +971 50 7147204 fred@cpi-industry.com In Asia (except India): Judy Wang T: 00852-30780826 E: judywang2000@vip.126.com Design Head Ulysses Galgo design@cpi-industry.com Webmaster Chris Lopez chris@cpi-industry.com Database/Subscriptions Manager Purwanti Srirejeki marketing@cpi-industry.com Published by Founder, CPI Media Group Dominic De Sousa (1959-2015) Co-Founder & Commercial Director Frédéric Paillé | fred@cpi-industry.com Co-Founder & Editorial Director Surendar Balakrishnan | surendar@cpi-industry.com Head Office PO Box 13700, Dubai, UAE Web: www.cpi-industry.com Printed by: Jaguar Printing Press L.L.C © Copyright 2026 CPI. All rights reserved. While the publishers have made every effort to ensure the accuracy of all information in this book, they will not be held responsible for any errors therein. www.climatecontroldirectory.com 4 from the editorial desk YOUR GATEWAY TO THE HVACR INDUSTRY IN THE MIDDLE EAST 2026ccme.news/event/refrigerants-review OFFICIAL PUBLICATION PRESENTS 21 JANUARY 2026 | DUBAI, UAE 5 TH EDITION THEME: ARTICLE 5, YES… BUT THE NEED FOR HASTENING THE TRANSITION, TO STEP AWAY FROM THE BRINKSalah Nezar Salah Nezar of New Murabba speaks on how AI is revolutionising District Cooling efficiency in ways unimaginable BRAIN MEETS CHILL HIS PAPER PROVIDES INSIGHTS from two novel AI-based energy optimisation projects developed under the Sustainability and Innovation function for leading global technology companies across North America. The pioneering initiatives leveraged advanced IoT mapping, granular metering and sophisticated Machine Learning models to significantly improve energy efficiency and provide greater operational visibility for large-scale data centres. The project, in the heart of Silicon Valley, California, achieved a remarkable 40% reduction in energy consumption. In contrast, the pilot, developed in Allin, Texas, is still being refined, with results currently withheld from public disclosure. Building on this global exposure and expertise, this paper also incorporates a valuable perspective from one of the novel testbed projects initiated by Saudi Arabia’s leading District Cooling provider. The pilot use TECHNICAL INSIGHT |AI and District Cooling case, conducted on a large District Cooling plant serving one of the mega-developments in Riyadh, further underscores the progress made in leveraging AI solutions for energy optimisation. Led by a well-established international consulting firm specialising in AI and digital solutions, the ongoing study produced a highly accurate predictive model demonstrating an impressive 96% accuracy within two hours. This milestone is considered significant progress in intelligent load demand forecasting and energy management for large-scale infrastructure in Saudi Arabia and the Middle East. New Murabba aims to redefine the concept of smart built environments and sustainable urban living on a global scale. More than just the world’s modern downtown, it is founded on cutting-edge natural resource optimisation strategies, integrated from the very inception of its development. New Murabba is leveraging AI-powered predictive modelling, digital twin technologies and integrated energy-efficient T www.climatecontroldirectory.com 6solutions to reduce energy demand and carbon emissions, while enhancing real-time resource management based on informative data. These advancements are being delivered through strategic partnerships with leading global technology firms and innovation hubs. At the heart of this transformative vision stands the Mukaab – an iconic, immersive megastructure that masterfully fuses cultural heritage with cutting-edge technological innovation. It serves as a powerful emblem of New Murabba’s commitment to low-carbon design, AI-integrated operations and future-ready infrastructure at every level, in full alignment with Saudi Arabia’s Vision 2030. These groundbreaking initiatives position Saudi Arabia’s giga and mega projects as regional pioneers and influential global contributors to the evolving narrative of sustainability, adaptive design and intelligent building innovation. Towards livable and sustainable urban environments Amid the climatic challenges and rapid urban expansion shaping major cities in the Middle East, the pursuit of livable, sustainable and energy-efficient urban environments has become a top priority from various perspectives and scales. District Cooling systems represent a transformative approach for modern climate control in this evolving landscape. District Cooling offers scalable, modular configurations that enhance efficacy, sustainability and operational excellence. The District Cooling concept centralises the cooling process by chilling water and delivering it through underground piping, producing a cooling effect on a district-wide scale. The approach offers many benefits related to energy efficiency, carbon emission reduction, integration of clean technologies and appealing urban space planning. In a region where cooling demand can represent 65% of the power demand in summer, these benefits become more than enticing; they become essential to meeting short- and long-term economic growth and prosperity goals. To achieve a stable and efficient operational scheme, cooling demand must be predicted accurately and granularly for a meaningful period. AI analytics represent a powerful enablement tool that provides enormous opportunities for energy optimisation, operational excellence and asset performance management across the lifespan. Seamless integration with Computerised Maintenance Management System (CMMS) is critical for optimising operational efficiency and maintaining data integrity. Predictive analytics capabilities can proactively manage fluctuations in cooling demand by varying the outputs and limiting systems’ interdependency inefficiencies during different operational modes. Role of AI in demand predictions and failure diagnostics AI-powered load prediction makes District Cooling plants much more innovative and resilient. By anticipating demand profiles, AI helps operators predict anomalies through alerts and equipment failure diagnosis before they increase in severity. These proactive features are essential in this region, where cooling demands are high and volatile and represent a severe burden on the power grid during the hot season. Key elements monitored by Machine Learning models include the following factors: Weather: Air temperature, humidity, wind speed and solar radiation are all great contributors to cooling usage. AI models can layer on localised microclimate data to provide sufficient coverage of the multiple weather types present in the Kingdom. Occupancy and use: Buildings generate a lot more heat at peak occupancy. AI learns the unique and sometimes rapid changes in usage transition of the diverse building typologies, such as residential tall towers, malls, multi-dimensional commercial buildings and large-scale mixed-use developments. Variations in part loads and peak loads: AI can detect and monitor hourly, daily, www.climatecontroldirectory.com 7 2026weekly and seasonal changes in cooling demand and mitigate the risk of service requests during part-load and peak-load phases. Various techniques are used to integrate the above factors in a single or multiple prediction model to meet the intended goals. Leveraging AI techniques for real- time decisions Utilisation of advanced AI methods for load predictions and consequent operational improvements is paramount to achieving maximum efficiency in District Cooling while reducing energy wastage. By using accurate predictive models, District Cooling operators and end-users can foresee spikes and drops in cooling demand, allocate resources more efficiently and minimise the overall operation costs. This requires an effective pair of statistical models and Machine Learning implementations, as follows: Statistical Models: These provide high-level understanding of consumption patterns and cyclical variations, and can often be considered baselines or components to be used within hybrid models, such as: Linear Regression, which is a traditional method that studies past cooling demand with regard to major predictors, such as ambient air temperature, humidity or occupancy proxies. It is only capable of identifying fundamental consumption trends and would typically use hyperbolic TECHNICAL INSIGHT |AI and District Cooling www.climatecontroldirectory.com 8Next >