Siemens, Tarshid to reduce emissions

Siemens is turning buildings and organizations in the Kingdom into high performing assets by maximizing efficiency, minimizing costs and reducing environmental impact.
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Updated 29 December 2019

Siemens, Tarshid to reduce emissions

Tarshid, the National Energy Services Company, and Siemens Saudi Arabia have signed a smart energy scheme to reduce 4,300 tons of CO2 emissions and support the National Information Center (NIC) save 28 percent on energy consumption annually. Leveraging on the Energy Savings Performance Contract (ESPC) model to accelerate smart building performance initiatives and reduce national domestic energy usage, Siemens aims to support businesses in the Kingdom along their journey toward a more sustainable and profitable future.
Siemens is partnering with Tarshid in the implementation of a holistic building performance and sustainability solution for the NIC.
With this agreement, the company aims to serve the Kingdom’s strategic sustainability goal of achieving significant energy savings by 2030.
“With buildings now becoming an essential part of clean energy transition strategies in the Kingdom, Siemens’ energy-saving measures for its cooling, lighting and occupancy-based energy scheme ensures the best outcomes for their project value and minimizes operating expenditures within the ESPC’s 10-year payback scheme,” the company said.
Combining the expertise of Siemens’ data analytics and digital services capabilities to deliver new levels of building performance and insights, the NIC will be able to reduce their strategic and operational goals, while increasing their competitive advantage.

HIGHLIGHTS

• Siemens is working with Tarshid to support the National Information Center save 28% on energy consumption annually through an Energy Savings Performance Contract (ESPC).

• The project reduces 4,300 tons of CO2 emissions from the environment, equivalent to planting 21,600 trees.

Elangovan Karuppiah, CEO of Siemens Smart Infrastructure, Regional Solutions and Services, Middle East and Asia, said: “Siemens has been a trusted partner of Saudi Arabia for nearly a century. This exciting new energy efficiency project is evidence of our firm commitment to jointly build the smart infrastructure that will power the Kingdom’s smart cities and create a sustainable future for the next generation.”
Siemens has expanded its investment in Saudi Arabia by transferring its know-how in energy efficiency, as well as its regional competency centers and global know-how to enable the Kingdom to reduce its dependency on oil and cut power consumption of its critical facilities, like the NIC. Backed by a strong global network of building performance and advisory services and proven track record in energy efficiency projects, Siemens is turning buildings and organizations in the Kingdom into high-performing assets by maximizing efficiency, minimizing costs and reducing environmental impact.


MBRSC opens registration for space science camp

Updated 09 August 2020

MBRSC opens registration for space science camp

The Mohammed Bin Rashid Space Centre (MBRSC) has announced the opening of registration for the Deep Learning Camp for university students, which will be held virtually from Aug. 18 to 20. The Deep Learning Camp is part of the initiatives of the MBRSC to support the UAE’s strategic directions in the science and technology fields, promoting the building of a generation capable of adopting advanced technologies to tackle future challenges.

Through the camp, MBRSC aims to provide the youth with a unique experience to explore space science through theoretical and practical lessons, activities conducted by experts and specialists in the field from the center. The camp is a great opportunity for students interested in the field of space sciences to acquire new skills about remote sensing and satellite analysis.

Yousuf Hamad Al-Shaibani, director general, MBRSC, said: “The Mohammed Bin Rashid Space Centre is keen to make effective contributions that support the UAE’s strategic directions in the science and technology field. Young people can explore the important aspect of space science and modern technologies that are the basis for advanced science in all fields through this camp. The UAE’s ambition to strengthen its leadership in the space sector is clear, as the country is taking prominent initiatives in space technology and enhancing its role in this field. This has acted as a catalyst for younger generations to study such scientific disciplines.”

Saeed Al-Mansoori, head of application development and analysis section, MBRSC, said: “Through these initiatives, we are working to prepare the youth and develop their skills to keep pace with the future of deep learning and artificial intelligence technologies. To keep pace with the developments in the field of space sciences and technology in the region and globally, it has become necessary to possess such important skills and knowledge.”

“This camp is a great opportunity for students interested in space science. The camp includes theoretical and practical activities, including sessions on satellite image analysis, remote sensing and artificial intelligence, and other topics related to enriching young people’s knowledge and attracting them to this field,” added Al-Mansoori.

The Deep Learning Camp is divided into theoretical and practical sessions. The theoretical sessions deal with the definition of the principles of deep learning, its applications and the techniques used therein, with focus on satellite image analysis. The practical sessions will involve learning the applied aspects of deep learning through specialized machine learning applications, including Python programming with Google Colab. The practical session will also engage the participants in a technological challenge through a hackathon, with great prizes awaiting the winners.

During the three-day camp, participants will learn 3 key elements: Principles of deep learning and artificial intelligence, how to use satellite imagery, and the ability to analyze and classify satellite images from a set of satellite image data.