After last week’s article on Green Bond and the funding gap, we want to explore Artificial Intelligence (AI)! There isn’t a day that goes by without the mention of AI and how it will disrupt our lives. Researchers and companies are exploring ways to leverage artificial intelligence and improve the efficiency and accessibility of renewable energy technology.
We cannot avoid this buzz word, so today let’s look at some of the top renewable energy technologies which incorporate AI. These technologies operate within three renewable energy spheres: Energy Forecasting, Energy Efficiency and Energy Accessibility.
Applications for Energy Forecasting
Who can predict the weather with 100% accuracy? No one, right? As it varies, it affects the power supply generated from the weather-dependent energy such as wind and solar. This is one of the main challenges of renewable energy.
Nnergix is a data mining and web-based energy forecasting application. Nnergix company uses both satellite data from weather forecasts and machine learning (ML) algorithms trained to analyze the industry’s data to make more accurate forecasting. The high-resolution weather forecasting is generated from satellite images. These images are then used to generate both large-scale and small-scale weather models. The ML algorithms analyze such data and predicts the state of the atmosphere in a particular region.
Xcel is implementing an AI that aims at addressing the challenges associated with the unreliability of the weather-dependent energy sources such as solar and wind. This application can tell whether the power source will fluctuate in strength (which is influenced by the varying weather). Xcel is used in accessing weather reports with higher accuracy and well detailed. The company gives the opportunity to employ greater precautions when harnessing and preserving the generated energy. To provide such a detailed report, the AI system mines data from local satellite reports, wind firms, and weather stations. The ML algorithm that drives the system is trained to identify patterns using these data sets to predict the generation of energy.
AI Applications for Energy Forecasting
DeepMind AI is an application Google bought in 2014 that works to improve energy usage. DeepMind’s aim is to reduce energy consumption as well as the emissions resulting when energy is used. The application managed to cool off Google’s data servers by 40%, reducing the energy consumption and the bill that goes with it. The graph below shows energy usage when the model is switched on and when it is switched off.
The company claims they applied machine learning for two years to achieve that level of energy usage improvement. A system of neural networks was trained using a set of data center operating scenarios and parameters. This system “Learned” how the data center functioned and was able to identify opportunities for optimization.
According to Google, this system manages to pull data from numerous sensors located in the data centers. Some of the information that it collected are power consumption and temperature. The neural networks were trained using Power Usage Effectiveness (PUE) which is defined as the ratio of total building energy usage to IT usage. The PUE model ensures efficiency by not exceeding operation constraints when the neural network system provide a recommendation.
Verdigris Technologies offers a software platform that leverages AI to optimize energy consumption. This application is designed for large commercial buildings and managers of enterprise facilities. The installation process begins with Internet of Things (IoT) hardware installation. Smart sensors are directly attached to the client’s electrical circuits to track energy consumption. The data collected by sensors are sent to the cloud and presented to the client through a dashboard. Some of the companies using this application include W Hotel San Francisco. The hotel uses the app to identify energy insufficiency in its commercial kitchen. The hotel confirmed that within three months of using the application it identified inefficiencies that were costing them over $13,000 in preventable annual losses.
AI Applications for Energy Accessibility
Verv is AI-powered and is used as a home assistant in energy management. This application supplies the data on home appliances energy consumption. This application enables the user to see the records on how each appliance in their home uses energy. It also helps the user to regulate their energy expenses.
When the connected appliances are turned on, the algorithm that drives the AI assistant recognise patterns hence capable of automating a running tally of the energy cost generated by the item. This application will also have safety features that send notifications when devices are left on for prolonged periods of time. It also provides tips for reducing a household’s carbon footprint.
PowerScout aims at improving consumer education and access to renewable energy technologies. This application uses AI to model potential savings on utility costs using industry data. PowerScout leverages data analytics to identify “smart home improvement project” that is based on unique features and energy usage in the home of clients. Basically, the AI acts as a market advisor by providing recommendations to help clients in making informed decisions when purchasing renewable energy technologies for their homes. By 2017, this system had overseen the installation of solar capacity that is nearly enough to power 250,000 homes. Some of PowerScout partners are the US Department of energy and Google.
AI applications can transform the renewable energy through increased efficiency, which in turn will fuel growth of the sector and hopefully accelerates its adoption. We can also use AI to help mitigating the challenges posed by inconsistent weather condition to the green energy sources.
If you know other AI applications in the renewable energy that we could add to this list, comment below.
Which one of these applications would be most useful for Africa? Let us know what you think and comment below.