Using AI to Generate Electricity? Machine Learning Inspires the Value of Wind Energy Utilization

By Super Neuro

Scene description:Introducing the application of machine learning in wind power generation scenarios: predicting wind power generation and adjusting the power supply scale in a timely manner; monitoring wind speed and direction, and adjusting blade direction and spacing in a timely manner, and other applications that greatly improve efficiency.

Keywords:Neural networks, renewable energy, weather forecasting

Wind energy utilization with AI-assisted power generation

Over the past decade, wind power has gradually become a highly regarded clean energy source. The Global Wind Energy Council (GWEC) recently stated that global wind energy utilization is also growing steadily, with more than 50 GW of new wind power capacity added each year since 2014.

The 50 GW of wind power generated globally each year is enough to support the real-time electricity consumption of five cities the size of Hong Kong. However, due to the instability of wind power generation, its potential in use has not yet been fully developed.

So what happens when AI is injected into this new renewable energy source?

DeepMind  andGoogleSince last year, we have been trying to apply machine learning algorithms to the analysis of wind power generation.Power supply recommendations are given by predicting wind power in advance to solve the mismatch between power demand and power supply, which leads to power waste and failures.

Using local weather forecasts and historical data from turbines to train a neural network, the DeepMind system is configured to predict wind output 36 hours in advance of generation, providing wind farm operators with more data-based assessments to meet actual electricity demand.

The algorithm is still being refined, but Google points out that machine learning has “It increased the value of our wind energy by about 20 percent.”They have applied this optimization to a wind farm in central America.

“We can’t eliminate wind variability, but we can predict it as accurately as possible. Using AI technology can also help bring effective advice to wind farm operations, as machine learning can help wind farm operators make smarter, faster and more data-driven assessments between power output and demand.”

Generators that can "follow the trend"

Envision, a company founded in 2007, is also using AI technology to promote the use of wind energy.

When Envision began designing and manufacturing smart wind turbines, managing and generating wind energy was considered a complex and difficult process, as the process was always dependent on the weather.

Envision's solution is to turn to digitalization. They try to use AI to find solutions from data. More than 500 sensors are installed on each generator to collect data on operation, power generation, maintenance, etc.

As the collected data accumulates, new patterns and insights begin to emerge.By monitoring factors such as wind speed and direction and making appropriate real-time adjustments to the spacing of wind turbine blades, wind farms can increase production by about 15%.

Envision also delved into modeling wind speed and overall situational intelligence. They also managed, recorded, and predicted user needs, and created a "digital model" of the wind turbine to conduct simulation tests.

As experience grows, Envision has developed a complete digital strategy and solution. Now they combine the Internet of Things (IoT), big data, artificial intelligence, and Azure cloud to fully unleash the potential contained in enterprise data.

With the support of these technologies, companies like Envision are becoming drivers of AI in the energy industry.

A hardcore player powered by love (AI)

The wind power industry has benefited greatly from technologies such as artificial intelligence over the past few years. Thanks to the introduction of AI technology, people have made better predictions about energy generation and utilization, and also achieved higher returns on investment.

So what is machine learning doing?

First, the intervention of machine learning solves a thorny problem: power generation and power demand must be matched.

Otherwise, there may be problems such as power outages and system failures. AI is using data to predict this value, such as the experimental scenarios of DeepMind and Google mentioned at the beginning.

On the other hand, using machine learning technology to monitor and maintain equipment has also become an important means to ensure the reliability and robustness of the power grid.

In addition to real-time monitoring, machine learning technology can also be used to achieve predictive maintenance.For example, predicting the remaining service life of a generator is mainly aimed at ensuring normal operation, avoiding power outages or downtime, and optimizing maintenance activities and periodicity, thereby reducing maintenance costs.

For users, providing accurate data is key, whether it is weather forecasts or wind turbine performance. Knowing the exact amount of electricity generated every day, providers can get the best benefits in terms of supply and conversion rate.

Google's blog post said, "We can use machine learning to make wind power more predictable and valuable." It also said, "We are eager to explore this attempt and want to work with experts to develop new ideas for making the most of this clean energy."

We often remind people that resources are scarce and non-renewable.

But when artificial intelligence is used, more ways to improve resource efficiency are found. This is a great blessing for the rapidly growing energy demand of human society. Let us look forward to the continued use of artificial intelligence. "Love AI" generates electricity.