How Artificial Intelligence is disrupting the Energy Sector

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Why AI is needed in Energy sector

In the wake of concerns over environmental imbalance due to global warming, the depletion of non-renewable energy sources is drawing nearer to extinction. The rapid depletion, coupled with the machines and industries growing more extensive over time, has resulted in a massive demand for power usage. With the onset of exploration of alternative power sources, has led to the development of the clean energy industry. There has been a surge in the development of the renewable energy sector with modern technology playing a crucial role in the past decade.

As is the case with every new technology, there are a few shortcomings in the production of renewable energy due to weather and other climate-related vulnerabilities. Although, some promising developments are aiming at eliminating the problems, however certain technologies are not yet tested thoroughly to achieve the confidence of being implemented at a large scale. 

The energy industry needs a reliable technology to deliver at its full potential, and here is the answer to that: Artificial Intelligence. Today, almost every possible industry has accepted AI, and the success of AI has been immense and contributing tremendously.

We are covering AI and IoT applications, their usage and benefits in different sectors in our posts in the Tech Bites section.

Here’s how AI technology has been aiding in the improvement and reliability of renewable energy sources:

Integration of Smart Grids

The AI-powered technology has been effective in balancing the energy flow within the grid by solving the congestion and quality issues within the primary grid. Another signification contribution of AI algorithms could be the provision of real-time control as well as optimization among the new devices and the generation sources.

Smart Grid architecture. Source

Smart Control Centers

In the day and age of smart devices, AI can be productive in monitoring the data collected from the devices and sensors for better control of the grids and facilitating in lowering the computation cost.   

Energy Allocation Efficiency

The AI-based algorithms have strong computational power, thus making them reliable to collect a large amount of data on usage. Based on the collected data, timely decisions can be made related to efficiently allocating the energy as per the usage data.

Improvement of safety and Reliability 

AI algorithms have the analytical capacity, thus making them the appropriate technology to adhere to. From the collection of data to monitoring of the health of the equipment over wear and tear, the operator or the control centre can have alerts before any failure at an early stage leading to reduction of maintenance cost.

The analytical capabilities of AI make them suitable for collecting meaningful information from the data collected from the devices and sensors and have an insight into the overall health of the equipment and alert the operator when the maintenance is needed.

AI applications in renewable energy

From energy-based forecasting to accessing of weather reports with higher accuracy, energy consumption usage monitoring, to home assistant energy management systems, the applicability of AI and its implementation has been widespread. Reduction of operating and maintenance cost can lead to an overall improvement in the energy sector. Some of the companies that have made their mark in the world of AI-powered renewable energy are Nnergix, Xcel, DeepMind, Verdigris Technologies, Verv, PowerScout.

A forecast of the AI trend in the energy sector

The energy companies have identified the potential of AI-powered solutions as not only for bringing efficiency but a lucrative investment which is future-ready. According to Allied Market Research, the expected growth of the smart grid systems market will be almost US$170 billion by the year 2025 as compared to US$67 billion in 2017. Here is a look at the past and projected energy consumption analysis in the US.

US energy consumption data in quadrillion by 2040. Source

The trend is shifting towards an increase of 15.3% by 2040. As per the US Department of Energy, a fully automated network that can monitor and control the smart grid to ensure proper flow of electricity. AI is considered as the preferred technology to drive this intelligent grid system. The technology is expected to monitor and collect a large amount of data from multiple sensors to make timely decisions based upon the data; machines will learn to detect the anomalies from large data sets using deep learning algorithms. This shift towards AI is a revolution in meeting the demand and supply requisites.

The renewable energy ministry of India aims at achieving 175 gigawatts (GW) capacity for its renewable energy by 2022 and 500 gigawatts by 2030. According to the reports, the current renewable energy that is installed is about 74Gw. The ambitious project is likely to benefit from the adoption of AI in its bid to increase renewable energy capacity.

Google announced that its DeepMind AI technology had achieved the forecasting of electricity generation by a wind farm. With a trained neural network on the available weather forecasting and turbine data, the AI technology could predict wind power output 36 hours ahead of its actual generation. Based on the results, the wind farm could deliver precise electricity generation at a particular time. 

Google’s DeepMind AI testing results from a typical day in a live data centre. Source

The results were consistent and achieved a 40 % reduction in the energy used for cooling that equates to about 15 % reduction in the overall required power usage effectiveness (PUE) costs after deduction of the electrical losses and other non-cooling inefficiencies.

AI Solar Forecasting

Australian Energy Market Operator (AEMO) announced the possibility of solar plants being able to forecast the amount of electricity generated. Australian Researchers are prompted towards the development of technology to improve weather forecasting as well as improve the ability of solar farms to predict the output. The move is shifted towards AI to be able to avoid the need to pay for frequency control and gain maximum output in the form of renewable energy.

Solcast has developed a revolutionary technology that can detect and predict cloud characteristics, irradiance and PV power through solar forecasting data with the help of modern algorithms.

AI & IOT Integration for Renewable Energy

Recent inventions have led to a robust integration of AI and the Internet of Things (IoT). It has become all-encompassing. IoT has garnered attention with the ability to allow remote-based controlling more effective. Today, IoT is used in every possible mechanism we can think of- computers, traffic lights, cars, phones, refrigerators, home lightings, to name a few.

While AI in the energy sector will improve the efficiency of prediction, computational power, IoT will be responsible for the successful implementation of automation. Automated devices for the energy sector will require options such as turning on and off when they are needed and not needed.

A significant contribution of AI and IOT could benefit smart city expansion ideas. For instance, solar streetlights can be controlled, perform more functions and made intelligent with AIoT. 

 Some of the areas where these technologies made a change are in-home lighting systems with motion sensors, that can notify a person to switch off the light or have an auto-switching off capability after a state of idle for several seconds. 

A Look at some of the Pros and Cons

AI-based technology is driving a revolutionary change in the renewable energy industry. With a positive outlook on new technology, the underlying concerns will have to be addressed before its full implementation in any industry. 


  • Better integration of all the connected sources for energy generation.
  • Faster and automated process.
  • Analytical capability with a large number of data.
  • Improvement in the centralization of the control centres.
  • Consumer-driven data monitoring for meeting demand and supply.
  • Reliability and safety.
  • Provision of intelligent storage systems for energy.
  • Accuracy in decision making.


  • Cost of development.
  • Availability of well-trained workforce for the smart grid systems.
  • Vulnerability to cyberattacks.
  • Over-dependability on automation could lead to lack of maintenance.
  • With the advancement in technology, a systematic upgrade will be required and lead to additional costs.
  • Failure to train and test the AI model appropriately will lead to inaccurate results.

With smart systems occupying a vast majority of our daily lives, the need for reliable technology to keep track of these systems will continue to increase. Artificial intelligence has revolutionized the world. It is only feasible that AI coupled with renewable energy is the way forward as the world moves into an era of digital evolution that will transform the renewable energy, making them cheaper and reliable in the future.

Kaushik Das
PhD (Artificial Intelligence) Kaushik is working as a data scientist ( healthcare analytics, also into artificial intelligence research)by profession. I am passionate about sports, photography, travelling and technology. I love to explore new technologies.