The Research and Development Center of the Dubai Electricity and Water Authority invests in artificial intelligence and machine learning to improve efficiency, reduce costs and carbon emissions.

The Research and Development Center of the Dubai Electricity and Water Authority uses artificial intelligence, machine learning and deep learning techniques to enhance the authority’s efforts to enrich the experience of customers, employees and stakeholders, reduce expenses and carbon emissions, enhance energy efficiency and smart grid integration, and improve the performance of solar photovoltaic panels.

In this context, His Excellency Saeed Mohammed Al Tayer, Managing Director and CEO of Dubai Electricity and Water Authority, said: “We are working within the framework of the directives of His Highness Sheikh Mohammed bin Rashid Al Maktoum, Vice President and Prime Minister of the UAE and Ruler of Dubai, by relying on artificial intelligence to advance With services, achieving the Emirates National Strategy for Artificial Intelligence 2031, and enhancing the position of the United Arab Emirates and the Emirate of Dubai as a global center for the Fourth Industrial Revolution and the development of disruptive technologies.The authority has started its journey in artificial intelligence since 2017 by developing a roadmap for artificial intelligence applications, and launched a set of services and initiatives that It relies on artificial intelligence, and the authority was one of the first government agencies in Dubai to adopt self-assessment tools to ensure the ethical use of basic artificial intelligence applications, and to take appropriate corrective measures.”

His Excellency pointed out that the Research and Development Center within the Mohammed bin Rashid Al Maktoum Solar Energy Complex contributes to advancing innovation in the various fields of production and operation needed by the authority, stressing that the center has become a global platform that provides innovative solutions and technologies that contribute to enhancing operational and service operations for various sectors of institutions. servicing.

Energy Efficiency

The Research and Development Center uses artificial intelligence, machine learning and deep learning to analyze load consumption data, and develop DEWA’s expansion plans and initiatives for energy efficiency and energy and water demand management.

The application of artificial intelligence to the analysis of big data related to building performance supports improved benchmarking tools, enhances the simulation of energy projects, which leads to a greater understanding of energy use, in addition to enabling the measurement of cooling loads in Dubai buildings and determining the impact of these and other loads on peak electricity demand. and water in the emirate.

The center applies artificial intelligence to smart meter data through machine learning and deep learning models, to identify electrical devices in use, monitor faulty devices, and predict peak load periods.

These technologies improve energy storage and load distribution management, identify building retrofit opportunities, increase the efficiency of energy reserve management, reduce carbon dioxide emissions, and reduce expenditures by at least 20%.

smart grid integration

The center exploits smart meter data and machine learning technology to monitor the operation of low voltage networks.

It also uses sensor and IoT data, asset load history, along with inspection and maintenance data to diagnose sensitive assets and predict faults, estimate useful remaining life (RUL), and monitor potential faults for medium voltage cables.

The center also relies on the fault data record based on artificial intelligence to predict the work of the circuit breakers of the protection devices, the peak consumption points on the high voltage network to avoid excessive loads, and the application of fault monitoring and preventive maintenance solutions to improve the main indicators of the authority such as the service interruption rate per subscriber annually, and the system indicator to measure the average Interruption Duration (SAIDI).

Solar Energy Resources and Forecasting Program

The program develops multiple models for evaluating solar resources and the amount of solar radiation and predicting the production capacity of solar energy systems, based on artificial intelligence and machine learning, and artificial neural networks of all kinds such as recurrent artificial neural network, LSTM artificial neural networks, and enhanced gradient machine learning XGBoost , and the UNET bypass neural network.

Solar Prediction Research Group

Deep learning and artificial neural networks are applied to monitor clouds and fog using satellite cameras and Meteosat satellite imagery, to improve the UNET convolutional artificial neural network, and reduce expenditures and carbon emissions by increasing solar energy production.

solar cell program

The center uses artificial intelligence and materials science or "material information science" to develop environmentally friendly and lead-free materials to make innovative, highly efficient and economical solar cells at the same time.

Improving the performance of photovoltaic solar panels

The center invests in deep learning to monitor dust and dirt build-up on photovoltaic solar panels and improve the readability of thermal images captured by camera drones and RTK.

The center has published several research papers during international scientific conferences on “Automated Monitoring and Detection of Photovoltaic Solar Panels Using Unmanned Aircraft” and “Improving PV Solar Panel Detection Using a Drone Equipped with Real-time Kinetic Technology (RTK).”

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