The energy system is on the cusp of truly realizing an intelligent vision characterized by 'second-level response, autonomous optimization, and comprehensive visibility'.

Driven by the dual imperatives of achieving carbon peak and carbon neutrality, along with the push for digital transformation, the energy sector is grappling with unprecedented pressures and opportunities for change. Amidst a backdrop of escalating annual digital investments in global energy systems, artificial intelligence (AI) has emerged as a pivotal force. By optimizing resource allocation, boosting operational efficiency, and tackling safety challenges, AI is steadily becoming the 'new engine' propelling high-quality development in the energy domain.

With AI's backing, the energy industry is undergoing a historic shift from being 'factor-driven' to 'data- and intelligence-driven'.

The Transformational Challenges of the Energy Industry and the Imperative of AI Empowerment

It's widely acknowledged that the traditional energy sector has long grappled with issues such as exorbitant operational costs, formidable safety management and control, and inefficient resource synergy. Take the power system as a case in point. Despite the continuous growth in installed capacity of renewable energy sources, fault resolution in distribution networks still lingers for several hours on average, and the curtailment rates of wind and solar power remain stubbornly high in certain regions. Moreover, under the 'source-grid-load-storage' integration trend, the influx of distributed energy resources has led to a surge in scheduling complexity, rendering traditional manual operation and maintenance models inadequate for meeting real-time response demands.

The core challenges during the energy industry's transformation pain period revolve around three key areas. Firstly, there's a stark contradiction between efficiency and cost. New energy power stations are widely dispersed, and traditional fiber-optic coverage is prohibitively expensive, while manual inspections are woefully inefficient. For instance, a photovoltaic power station in Guangdong spans a vast area with complex terrain, and a single manual inspection can stretch over several weeks, with a defect identification accuracy rate of less than 60%. Secondly, there's an elevated requirement for safety and resilience. Grid control-related operations necessitate millisecond-level response, yet there are blind spots in medium- and low-voltage communication networks. A grid project in Sichuan once experienced delayed fault isolation due to communication lags, underscoring the criticality of communication reliability for grid safety. Additionally, new business models like virtual power plants grapple with challenges such as low resource aggregation and single profit models, making it arduous to support market-based transactions.

The energy industry has entered a pivotal transformation phase, and AI technology is emerging as a crucial fulcrum for addressing these challenges. Machine learning algorithms can predict equipment failures, computer vision can replace manual inspections, and intelligent scheduling models can resolve multi-energy coordination problems. Take a technology company founded in 2011 specializing in the power industry as an example. The remote intelligent inspection system developed by this company, deployed at substation sites, aids enterprises in tackling issues like incomplete control over equipment status and insufficient personnel and equipment capabilities in equipment operation and management. It provides robust technical support for the visual monitoring, fault diagnosis, status evaluation, and operation and maintenance management of substation equipment.

Energy Transition Investment Soars to New Heights, Intelligent Computing Power Fuels Transformation

According to BloombergNEF's '2025 Energy Transition Investment Trends' report, global energy transition investment surpassed the $2 trillion mark for the first time in 2024, reaching $2.08 trillion, a doubling from 2020. Although the growth rate dipped from 24%-29% in 2021-2023 to 10.7%, the absolute amount continued to climb significantly, signaling a deepening structural transformation of the global energy system. In terms of investment structure, investments in electrified transportation, renewable energy, and power grids soared to $757 billion, $728 billion, and $390 billion, respectively, all setting record highs. Energy storage investment exceeded $54 billion, marking an acceleration in the global energy system's shift towards low carbon.

The report highlighted that in 2024, the total global energy transition investment surged by $202 billion, with China contributing $134 billion as the core engine of global energy transition growth, primarily driven by its robust performance in renewable energy, energy storage, nuclear energy, and other sectors.

In this epoch-defining energy transformation, intelligent transformation is paramount. Technology companies, exemplified by China Mobile, have transcended traditional communication service boundaries, laying a digital foundation through 'connectivity + computing power + capabilities'.

According to China Electronics News, in the industrial sector, China Mobile tailored an uplink bandwidth solution for Shanxi coal mines. Its 'Zhongyi Bailing' device slashed manual inventory at State Grid by 90% and boosted Sinopec's efficiency by 60%. China Telecom's 5G customized networks provide robust guarantees for home appliance '5G-A Light Factories' (with a latency of 8ms and reliability of 99.99%) and automotive welding lines (with a latency of 4ms and reliability of 99.999%), driving the coverage of 45,000 5G projects in industrial mines.

Furthermore, as reported by the People's Post and Telecommunications News, through the construction of an advanced green computing power network and AI-driven societal carbon reduction initiatives, China Mobile has achieved remarkable results in carbon emission management. In 2023, the energy efficiency per newly added 5G base station improved by 9% year-on-year, and the average power usage effectiveness (PUE) of large and extra-large data centers decreased by 4.5% year-on-year. Clean energy capacity, such as wind and solar, reached 210 million peak watts. The comprehensive energy consumption and carbon emissions per unit of telecommunications business volume across the entire group decreased by 13% year-on-year. The 'Guidelines on Green Supply Chain' were released, achieving a paperless procurement rate of 99.9% and a green packaging ratio exceeding 80%, leading upstream and downstream partners to jointly forge a green supply chain. By harnessing new information technologies, China Mobile actively supports societal pollution reduction and carbon reduction, biodiversity conservation, advocates for green lifestyles, unlocks the potential of low-carbon development, cultivates momentum for green development, and assists in reducing greenhouse gas emissions by 310 million tons.

When AI technology seamlessly integrates into the energy system, the industrial revolution transcends mere technological iteration and evolves into a systemic restructuring. Technology-neutral players, by constructing open platforms, amassing core capabilities, and reshaping value distribution, are propelling the energy industry from 'scale expansion' to 'quality leap'. The ultimate goal of this transformation is to construct a 'secure, trustworthy, green, efficient, open, and collaborative' new energy ecosystem, and the historical mission of technology providers is precisely to become the magical enchanters and bridge builders of this transformation.

Future Vision: Ecological Collaboration in the Intelligent Energy Era

When AI deeply integrates into the energy system, the future industry will manifest three major trends. Firstly, full-chain intelligence will prevail, with AI permeating every facet of energy, from wind turbine fault prediction to grid dynamic pricing. According to BloombergNEF's forecast, by 2030, AI-driven power system optimization will enhance global renewable energy consumption capacity by over 15%.

Meanwhile, the capital market has keenly discerned this new trend. This year, the smart energy sector in the A-share market has exhibited a significant upward trajectory, reflecting the market's high recognition of the industry's digital transformation.

The future energy system will be characterized by three distinct features. Firstly, there will be integrated 'source-grid-load-storage' collaboration, where distributed power sources, flexible loads, and energy storage facilities will achieve second-level response through AI algorithms, forming a virtual 'elastic power plant'. Secondly, there will be carbon-energy linkage management, where carbon footprint tracking systems based on 5G and IoT technologies are making the 'green value' of each unit of electricity measurable and tradable.

Additionally, in terms of ecological collaboration, virtual power plants can aggregate distributed resources such as charging stations and industrial loads, forming a three-tier collaborative system of 'microgrids-regional grids-main grids' to achieve cross-regional energy scheduling. Trusted operations can also be realized, such as combining blockchain with AI to enable carbon footprint tracking and green power trading traceability, assisting in the construction of a trusted energy data space. China Mobile can leverage virtual power plants and integrated photovoltaic-storage-charging intelligent scheduling platforms to achieve load forecasting and demand response. For instance, AI algorithms can perform load forecasting and demand response, aggregating flexible loads such as distributed air conditioners, industrial motors, and charging stations to participate in market-based regulation, helping the grid balance peak and off-peak demands.

The AI empowerment of the energy industry is not a mere technological overlay but a systemic endeavor involving architectural reshaping, mode innovation, and ecological collaboration. Technology providers can only play an irreplaceable role in the energy revolution by delving into the industry's intricacies and transforming communication advantages and AI capabilities into measurable value. With the further integration of large models and physical simulations, the energy system is poised to truly realize an intelligent vision characterized by 'second-level response, autonomous optimization, and comprehensive visibility'.