Quanam was responsible for the data lake implementation and associated analytical and advanced analytical solutions. Within the scope of the project, Quanam participated in the implementation of the Data Lake Data Governance Strategy by defining policies, processes and controls to measure compliance for ingestion, self-service, catalog, security and data quality. Some of the use cases implemented with Machine Learning Models are:
Enhances electrical power grid efficiency, reliability, and sustainability through accurate load forecasting, renewable energy integration, demand response management, predictive maintenance, energy storage optimization, and network improvement.
create effective and efficient pricing strategies that meet both business objectives and customer needs while complying with regulatory requirements.
streamline operations, enhance service reliability, and promote sustainable resource management.
Predicting energy demand and analyzing consumption patterns at a granular level for enhanced operational efficiency, customer service, and strategic planning.
create a more engaging, satisfying, and value-driven customer experience, ultimately driving business growth and customer loyalty.
manage and influence the energy consumption patterns of consumers, particularly during peak demand periods.
detect, analyze, and mitigate losses, which can be due to technical issues, such as leaks or faults in the system, or non-technical reasons, such as theft or inaccurate metering.
optimize the maintenance, performance, and lifespan of physical assets, leading to increased operational efficiency, reduced costs, and enhanced reliability.
optimize operations, enhance service delivery, and plan for future infrastructure needs.
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