Data centers and data transmission networks are major consumers of electricity, accounting for roughly 1–1.5% of global electricity use in 2022, with data centers alone consuming 240–340 TWh and networks 260–360 TWh globally . In the U.S., data center electricity demand could double by 2030, potentially consuming up to 9% of national electricity generation, driven largely by AI and high-performance computing . AI queries, for example, require about 10 times more electricity than traditional internet searches, highlighting the energy intensity of modern big data applications . Despite efficiency improvements, the rapid growth of workloads in hyperscale data centers has led to annual energy growth of 20–40% in some segments .
Big data analytics plays a crucial role in modern power grids, enabling better forecasting, grid optimization, and renewable energy integration. Platforms using Hadoop, Spark, and data lake architectures allow utilities to analyze electricity prices, load demand, and consumption patterns, improving operational efficiency and reducing costs . AI-driven analytics can predict maintenance needs, optimize energy distribution, and support decision-making for both utilities and consumers .
The massive volume, velocity, and variety of data generated by energy systems and internet services create scalability and storage challenges. Traditional databases often cannot handle these large-scale datasets, prompting the adoption of NoSQL databases, cloud platforms, and emerging technologies like blockchain and peer-to-peer data management . These solutions improve data accessibility, sharing, and regulatory compliance while enabling real-time energy monitoring and optimization.
While ICT energy use has grown moderately compared to data traffic increases, the sector still contributes to greenhouse gas emissions and energy demand. Data centers connected to grids with higher renewable energy shares produce fewer emissions, and efficiency improvements have helped limit growth . Policymakers and industry stakeholders must balance digital growth with sustainable energy practices, including renewable integration, energy-efficient hardware, and AI-driven optimization strategies .
In summary, to realize an energy-efficient hybrid big data optimization method for 5G-IoT, the following key limitations
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