Machine learning can boost the value of wind energy


Carbon-free technologies like renewable energy help combat climate change, but many of them have not reached their full potential. Consider wind power: over the past decade, wind farms have become an important source of carbon-free electricity as the cost of turbines has plummeted and adoption has surged. However, the variable nature of wind itself makes it an unpredictable energy source—less useful than one that can reliably deliver power at a set time.

In search of a solution to this problem, last year, DeepMind and Google started applying machine learning algorithms to 700 megawatts of wind power capacity in the central United States. These wind farms—part of Google’s global fleet of renewable energy projects—collectively generate as much electricity as is needed by a medium-sized city.

Using a neural network trained on widely available weather forecasts and historical turbine data, we configured the DeepMind system to predict wind power output 36 hours ahead of actual generation. Based on these predictions, our model recommends how to make optimal hourly delivery commitments to the power grid a full day in advance. This is important, because energy sources that can be scheduled (i.e. can deliver a set amount of electricity at a set time) are often more valuable to the grid.

Although we continue to refine our algorithm, our use of machine learning across our wind farms has produced positive results. To date, machine learning has boosted the value of our wind energy by roughly 20 percent, compared to the baseline scenario of no time-based commitments to the grid.

We can’t eliminate the variability of the wind, but our early results suggest that we can use machine learning to make wind power sufficiently more predictable and valuable. This approach also helps bring greater data rigor to wind farm operations, as machine learning can help wind farm operators make smarter, faster and more data-driven assessments of how their power output can meet electricity demand.


Our hope is that this kind of machine learning approach can strengthen the business case for wind power and drive further adoption of carbon-free energy on electric grids worldwide. Researchers and practitioners across the energy industry are developing novel ideas for how society can make the most of variable power sources like solar and wind. We’re eager to join them in exploring general availability of these cloud-based machine learning strategies.

Google recently achieved 100 percent renewable energy purchasing and is now striving to source carbon-free energy on a 24×7 basis. The partnership with DeepMind to make wind power more predictable and valuable is a concrete step toward that aspiration. While much remains to be done, this step is a meaningful one—for Google, and more importantly, for the environment.


AI and Robotics in Retail: Drivers, Impact, and Challenges


As the modern world seeks innovation and convenience, retail providers are faced with the new challenge — to keep up with the trend or fall behind.

Due to this, many retailers are delving into the latest technologies that seek to address the new needs of their businesses, and that may mean looking toward enterprise software development. Let’s look at how retailers are innovating and dive deeper into their artificial intelligence and robotics solutions.

Why Do Retailers Need to Modernize?

According to Statista, by 2021 online e-commerce sales are set to total a record of $4.8 trillion (USD). Meanwhile, in 2018 this amount was estimated at a lower $2.8 trillion. What this shows is an industry in rapid growth, and there are no signs of it slowing down.

This growth makes one factor exceptionally clear — if you want to stay competitive in the retail business, no matter whether you have a small corner shop or a multinational enterprise, you need to consider optimizing your operations with new technology. Across web, mobile, and in-store, such technology is poised to include AI and robotic process automation (RPA), and here’s why:

The Value Driven by AI and Robotics in Retail
  1. Better insights into inventory and supply planning
  2. No or fewer employees required in physical location management and delivery tracking
  3. Predictive analytics of customer-tailored demands
  4. Personalization of customer support
  5. Cashier-less checkout operations
  6. Better product categorization of both local and global stock units
How AI and Robotics Solutions Boost Retail Businesses

Now that we know the benefits, let’s look at how these solutions work. To begin, let’s consider retail business processes as divided into two parts:

  • Back-office operations — consisting of paperwork, staff and product management
  • Shop-front operations — serving customers and addressing their issues

Across all of these functions, AI and robotics help retailers achieve better results.

Improving Planning and Strategy

AI technologies allow retailers to gather, rework and standardize data, automatically enter it into spreadsheets, and transform it into understandable visuals such as charts. In turn, this helps build efficient business plans, reduces the time on report compilations, forecasts sales figures, generates customer profiles, and understand customers’ shopping preferences.

Equipped with these reports on customer and market behavior, marketing and sales professionals can efficiently plan campaigns and target them toward real consumers. For managers, this aids in ensuring certain products remain stocked as they know which are in demand.

Optimizing Logistics and Inventory

AI programs stock, process and analyze significant amounts of information, resulting in a prediction of the outcomes and even applying them to discover new revenue channels. This can be helpful in back-office operations such as accounting and business planning, but is not limited to these areas.

For example, when paired with IoT, AI applications have already begun to improve the transportation of goods by managing their provenance and shipping conditions data. This can be tracked through the entire journey, ensuring better food security and enabling logistics enterprises to make more informed decisions.

In addition, cloud technologies assist retailers in restocking the shelves and tracking customers’ movement in-store, gathering information on the demand and forecasting the popularity of certain products.

Personalization and Customer Experience Management

According to McKinsey & Company, the retail sector is one of the foremost industries that has benefited from AI and robotics implementation. One of the reasons is that this can transform retail businesses by making them more customer-oriented.

AI-equipped systems can collect exceptionally accurate data about buyers’ preferences and habits. Relying on this data, retailers can grow their sales by recommending suitable items to customers. This is something that a few big names have already tried out with visible results:

  • NY-based company Caper has recently developed a handy computerized shopping cart. This cart helps customers to learn more about products by simply scanning them; the details then show up on the screen. In addition to this, buyers can “checkout” their goods online to avoid standing in a line.
  • Ocado, a grocery company, uses the Google technology based on speech recognition to deal with customer complaints. Google Cloud AI speeds up the process of complaint analysis, helping Ocado to promptly fix and improve their services.

In addition, robotics proves beneficial for in-store service, too. For example, robots can provide retailers with the information on the shelf inventory, price tag changes and consumer preferences, personalizing the products in stock. Robotized call-centers can help cut on the expenses while ensuring customer support is available 24 hours a day.

Finally, the buyers themselves can benefit from machine learning systems by using automated checkouts, avoiding long queues or getting quick support through digital kiosks.

Challenges of AI Adoption and Their Solutions

Despite these numerous benefits, it is an undoubtable fact that any business seeking to integrate new technologies, AI in particular, will be faced with certain challenges:

1. New working practices

As IT integrations advance, we are likely to see more changes in how we work. The current trend sees manual labor activities increasingly performed by robots, while “mental” work is performed by humans. But even this could be set to change as AI programs are gaining skills and are able to effectively work with data.

Recent research by McKinsey & Company has shown that out of 2,000 labor activities, about 800 occupations can be automated to some extent. For society, in general, this will mean a new drive in skill building and a changing job market in the future.

However, for retailers, this means having to both reconsider their staffing needs and their technology firepower to be able to keep up with the competition.

2. Costs of new software

For retail businesses that are just starting to introduce technology, the initial costs may seem off-putting. Usually, this means developing customized software and products to improve the business, and this may be more costly than off-the-shelf products. In addition, companies may need to consider hiring specialists to maintain and service such systems.

While initial roll-outs of such developments come at a price, companies should look at their long-term benefits and the overall effect on the business.

3. Security

Finally, retail providers will find new challenges in dealing with security. For many of these systems to work effectively, a large amount of information has to be collected and stored. This means that companies will be ever more responsible for data security, in the areas of individual privacy and the privacy of their whole businesses.

Safe data storage and consent management is one aspect; another is protection from hackers. This is essential to keep data from being exploited and systems from becoming corrupted.


For retailers to adapt and thrive in the new era, they will need to undertake changes to how they do business, and this may mean involving AI and robotics technologies.

These changes have both advantages and disadvantages for the retail sector and its employees. Personalization and robots taking over routine operations may be seen as positives, while the changing roles within an organization may be a negative. It will take flexibility and thought-out strategies for retailers to go with this AI flow without major disruptions to their modus operandi.