Solar+Storage
Article | June 14, 2022
It all started about four years ago, when SUVs and pickup trucks drove uninvited onto their lands, remembers Olimpia Palmar, a member of the Indigenous Wayúu peoples, who historically have occupied the La Guajira desert in northern Colombia and Venezuela. "We started seeing these arijunas [Wayuúunaiki for non-native peoples] wearing construction helmets and boots and vests, getting out of the cars, checking the desert, and then leaving," she recalls.
Word soon began circulating across the Guajira Peninsula, from the rancherías — the community’s rural settlements — to the few urban centers: The arijunas were offering money to those who would let them plant tall, slim towers on their lands to measure the wind. On La Guajira’s dusty earth, where few things grow, towers began to sprout. By 2019, at least 30 wind-measuring towers had risen on Wayúu land, according to a report by Indepaz, a nonprofit research center.
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Strategy and Best Practices, Industry Updates
Article | August 16, 2022
According to a survey from Consumer Reports, 76% of Americans believe that expanding renewable energy is a worthwhile goal.
People are interested in renewable energy, but finding those people and turning them into customers is challenging.
That’s where lead generation for renewable energy can help. You need smart lead generation strategies to grow your business and get customers access to renewable energy.
To discover the top strategies for lead generation for renewable energy companies, keep reading!
WebFX is a top lead generation agency, and our team of more than 200 digital marketing experts has generated more than 6.3 million leads for our clients over the past five years. Contact us online or at 888-601-5359 to learn more about our lead generation services.
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Solar+Storage
Article | June 8, 2022
A high-tech greenhouse comprised mainly of solar glass generating electricity to help run it was officially opened yesterday in Western Australia.
ClearVue Technologies Limited’s solar glass involves a nanoparticle interlayer and spectral-selective coating on the rear external surface that enables 70% of natural light to pass through while redirecting infrared and UV light converted to infrared to the edge where it is harvested by solar cells. ClearVue says each 1m2 of its window product is currently rated to generate 30 watts-peak of electric power, but also mentions a new-generation product with the proven ability to generate 40 watts peak per m2 to be available sometime this year.
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Energy
Article | July 16, 2022
Machine learning and artificial intelligence (AI) are two of the most commonly used commercial phrases these days. As a result, companies across sectors are searching for methods to include them in order to optimize and automate their key operations. The energy sector is no exception!
Indeed, throughout the years, renewable energy industries (wind, solar, hydro, nuclear) have substantially gained from the potential of machine learning. They were able to reduce their expenses, make better projections, and raise the rate of return on their portfolio. And this tendency is just going to gain momentum. If your company is in the energy industry or utilizes a lot of power, machine learning and AI can help you improve your business performance. But how precisely? Let's get started.
Ways in Which AI and Machine Learning are Changing Energy Sector
There are a few methods that machine learning and AI can be applied to positively improve the energy industry. Here are a few popular applications currently under development.
Predictive Maintenance
AI helps match energy output with demand and ensure power grid stability and resilience.In 2003, a low-hanging high-voltage electricity line hit an overgrown tree in Ohio, causing a widespread blackout. There was no power system alarm and no sign of the incident. The electric company didn't notice until three additional power lines failed. This carelessness ultimately brought down the whole grid. The 50 million-person blackout lasted two days. Eleven individuals died, and $6 billion was lost.
Predictive maintenance can be implemented using machine learning and IoT
Sensors gather operational time series data from electricity lines, equipment, and stations (data accompanied by a timestamp).
Machine learning algorithms can then forecast when a component will fail (or n-steps). It can also anticipate machinery's remaining usable life or future breakdown. These algorithms detect machine failure, eliminate blackouts or downtimes, improve maintenance procedures, and reduce maintenance expenses.
Grid Management
Grid management is a promising AI application in energy. Complex networks distribute electricity to users (also known as the power grid). Generation and demand must always match in the electrical system. Other issues, like blackouts and system breakdowns, can occur.
Despite being ancient, pumped hydroelectric storage is the most common way to store energy. It operates by moving water upwards and letting it fall into turbines. Renewable energy makes predicting the grid's power generation challenging. After all, it is affected by a variety of things, like sunlight and wind.
Demand Response
Large demand shifts can be expensive for nations that depend on renewable energy. As nations migrate to green energy, it's harder to adapt to demand fluctuations. Germany plans to use 80% renewable energy by 2050.
Countries such as Germany will encounter two major challenges Demand fluctuations: On some days or times of the year, power consumption soars (on Christmas, for example) Weather volatility: Without wind or clear skies, it might be hard to meet electrical demand. In both circumstances, more stations or fossil fuel-powered facilities must meet demand
Solving demand response issues
Many nations are partnering with businesses to examine weather forecasts, power demand, etc. Germany's EWeLiNE project forecasts wind and solar energy at a specific moment. This enables the government to use non-renewable energy to meet additional power demand.
They utilize enormous historical data sets to train machine learning algorithms, as well as data from wind turbines or solar panels, to properly balance supply and demand.
Closing Lines
AI increases the potential of humans. Several renewable energy producers are investing in artificial intelligence to boost their businesses.There are numerous uses of artificial intelligence in renewable energy. The fundamental purpose of AI integrated systems is to reduce forecasting issues and incorporate renewable energy into the central energy grid as effectively as possible. AI can also assist renewable energy providers in developing successful plans and policies based on present energy consumption and demand.
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