The power sector landscape has ev­olved significantly over the last deca­de and is no longer limited to generation, transmission and distribution. With power being increasingly interconnected with transport and buildings, and the greater penetration of intermittent renewables, distributed generation, and electric vehicles into the grid, there is some concern regarding the efficient management of power systems. In the case of renewa­bles, specifically wind power, proper forecasting and scheduling of generation has become extremely important so as to all­ow for better management of power and demand supply by grid operators. Further, to match the actual energy generated with the predicted generation, proper operations and maintenance (O&M) pra­ctices need to be employed with the support of advanced tools and techniques. Thus, a high degree of coordination and flexibility is required between the different systems in the power sector value chain as well as in the O&M of individual wind power generation assets.

Artificial intelligence (AI) has emerged as a key enabler in this respect, offering a host of benefits from efficient integration of in­termittent wind power into the grid, to enabling automation of various construction and O&M activities, to better grid ma­na­gement. According to John McCarthy of Stanford University, AI “is the science and engineering of making intelligent ma­chines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.” IBM simplifies AI as “a field, which combines computer science and robust datasets, to enable problem-solving.” AI is different from automation, as the latter makes systems perform a certain set of repetitive tasks, whereas AI uses large volumes of data to extract insights and patterns and predict outcomes, and then learns to do this more effectively. The im­plications of AI application in the context of wind power are far-reaching, and thus there is a global thrust from developers, manufacturers and operators to incorporate AI in their strategies.

A recent white paper by the World Econo­mic Forum titled “Harnessing Artificial Inte­lligence to Accelerate the Energy Transi­ti­on”, produced in collaboration with Bloom­berg NEF and Deutsche Energie-Agentur (dena), discusses the need and applications of AI in energy transition and provides recommendations for the future.

REGlobal relates the key findings from this white paper with respect to applications of AI in the wind power space.

Applications of AI

According to the white paper, the most promising AI applications can be categorised under four focus areas: renewable power generation and demand forecasting, grid operation and optimisation, management of energy demand and distributed resources, and materials discovery and innovation. A variety of input data is used in AI to serve these applications. From a wind energy perspective, this input data could be:

Market, commodity and weather data: These data series are often collected over regular time intervals through various sources to identify patterns or outcomes. In wind power plants, electricity data and weather data might be useful for project operations.

Images and videos: Images and videos are used and observed to predict irregularities or outcomes. Images can be used for inspection and prediction of faults in wind turbines.

Equipment and sensor data: Sensor data from equipment with advanced co­m­­munication techniques can enable ac­c­urate real-time monitoring of wind turbines and help in improving energy output and preventing costly repairs.

AI can be applied in wind power plants in the following ways:

Renewable power generation and demand forecasting

With the increasing size and scale of wind power projects, and greater penetration of wind energy in the grid, it is of utmost im­portance to have quality assets with ad­van­ced mechanisms for precise prediction of wind generation. AI can help with all of these. AI is being used to identify suitable wind power project sites, and further, to determine the best location for wind turbines at a project site. AI can also help during the construction phase by managing schedules and cost plans, and tracking equipment delivery. Moreover, it can play a role in improving the product design of wind turbines by using past data of common failures, and help enhan­ce product performance.

O&M of wind power projects is a costly and time-consuming task and becomes even more complicated in the case of offshore wind assets. AI can help predict faults before they occur so as to prevent project downtime and repair costs. Fur­ther, with the use of sensors and remote monitoring systems, alarms can be raised if a possible fault is diagnosed, so that timely corrective action can be taken. AI can also be an effective tool for the O&M team to keep track of maintenance schedules.

Forecasting wind power generation is important from the grid management point of view, and AI can help in accurate forecasting of energy output based on data such as weather, wind speeds, and historical wind generation in specific con­ditions. This wind power supply has to be met with equal demand so as to prevent generation curtailment. Thus, AI can help ascertain power demand based on consumer data and load profiles, and help in better integration of wind power into the grid.

Grid operation and optimisation

To achieve the massive renewable energy targets set by global economies, significant expansion and upgradation of power evacuation and transmission systems is required. However, since the gestation time of transmission projects is significa­n­tly longer than that of renewable power projects, unavailability of ready transmission systems might become a hindrance in renewable power growth. AI can be used to optimise grid operation through planning and careful design, which can be quite beneficial in ensuring that existing transmission capacity is utilised to its full potential.

AI can play an important role in ensuring proper O&M of equipment and monitoring grid performance. Further, AI can help in maintaining grid stability, especially with the increasing penetration of intermittent renewables such as wind power and electric vehicles, and ensure grid security. 

Management of energy demand and distributed resources

AI has a key role in the management of energy demand and supply from various distributed energy sources. It can help in the integration of battery energy storage systems at wind power plants and control the energy flows in the entire set-up so as to ensure optimised wind power performance. It can help in efficiently managing facility loads. Further, it can facilitate the creation of virtual power plants by aggregating a number of small and stand-alone wind projects that cannot otherwise be connected to the grid.

Materials discovery and innovation

As companies race to develop the most efficient and advanced wind power technologies, the raw materials used in these products also assume importance. Thus, at the material level, AI can help in auto­nomous materials discovery to screen and identify the most suitable materials for a particular application. Further, AI can be used in automated synthesis and ex­pe­rimentation, to test these materials in specific sets of conditions.


The future of energy systems is heavily dependent on automation and digitalisation, with increased integration of AI wherever feasible. While in certain app­li­ca­tio­ns, the cost of advanced AI tools re­mains a key concern, the constant innovation and uptake of these is likely to result in drastic price reductions in the co­ming years. Many activities such as location siting, maintenance schedules, predictive maintenance and generation forecasting already use some level of AI, and there is immense potential for the incorporation of this digital marvel in other applications as well.