With the energy landscape about to change dramatically, now is the time to combine machine learning with the grid.
Bill Gates said in 2017: "If I were starting out today and looking for the same kind of opportunity to make a big difference in the world, I would look at three areas. One is artificial intelligence; The second is energy; The third is biological sciences.
There is no doubt that the future of energy lies in sustainable, reliable and "smart" power generation and distribution systems, and active rather than passive networks. Power companies have a large and growing amount of data related to network failures, network models, operating information from generators, and asset databases.
Data has great potential to predict network failures and assist in maintenance. In the future, with machine learning, adding network failure logs will be part of the solution, not the problem. By adding more records, you can provide more analytical data to the model, which can make more accurate, more accurate predictions.
For example, machine learning algorithms can access databases with type, location, lifetime or lifetime profiles and asset status, circuit and load data, as well as existing fault data, and return the probability and cost of a failure as well as when it might occur, such as in hours, days, weeks, or months.
Machine learning has the potential to be used as an economic modeling tool to evaluate strategic developments and decisions related to the use of power grid reinforcement solutions through cost-benefit analysis. In the future, we will not only respond to failures, but also anticipate and avoid them using models that predict failures by analyzing technical and economic data. Thus, through machine learning, the power industry has taken a step towards developing active rather than passive systems.
In the post-pandemic era, the most pressing challenge is climate change, and in the UK, for example, they have committed to a transition to a zero-net economy by 2050, with electricity grids on a more renewable basis. We can already see that with clean energy generating 40% of the UK's electricity in the first three months of 2020, the role of renewable energy is growing, with renewables overtaking fossil fuels.
Analysts say the renewable and sustainable energy industries should play a greater role and promote a green economic recovery, as they did during the last recession. While not without challenges, it is possible, and machine learning can solve some problems.
Even with the weather forecasts used, it is difficult to accurately predict fluctuations in electricity generated from renewable sources such as wind and solar. In addition, the small distributed generation and storage of internally installed equipment (such as photovoltaic and batteries) (50 million worldwide) adds to the uncertainty of the system.
Machine learning and artificial intelligence may solve these problems because these algorithms can be used to predict demand more accurately, as well as the output of renewable power generation, both in the short and long term.
The use of installed energy storage devices, including batteries, has now begun to reduce the uncertainty of renewable energy generation and to help achieve a higher percentage of renewable energy demand. However, the solution may have reliability issues and limitations, such as battery degradation and unexpected failures that require constant monitoring and maintenance.
Using machine learning as a tool to monitor and predict potential failures in energy storage systems may lead to more reliable and efficient systems, and by using AI and machine learning algorithms, power demand and renewable energy generation will be more predictable and energy storage more reliable and efficient.
The scientific community is already studying the promise of "smart" energy and machine learning in power networks. Much has been made of predictions of energy demand, solar power generation, and even precise predictions of the amount of energy that can be collected from food waste in the urban environment. Given the deep understanding and widespread use of AI and machine learning in other areas, the possibilities in the grid area are exciting as we transition to a zero-net economy and society.