Pros and Cons about Tidal Wave Energy

Solar+Storage, Strategy and Best Practices

Article | September 17, 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.

Strategy and Best Practices, Industry Updates

Article | August 16, 2022

To maintain the goals of the Paris Agreement and save the Earth from ecological breakdown, one of the most important things experts agree we need to do is transition to a renewable energy economy. While most of us may associate renewable energy with wind energy and solar energy, there are several other sources of clean energy that are growing in popularity. One such source is geothermal energy.

Solar+Storage

Article | June 8, 2022

In the last forty years, there has been a dynamic increase in the use of solar energy in the United States. As recently as 2018, an additional 10.6 GW of solar power was harnessed, bringing the country's total use up to 64.2 GW. Yet this apparently successful addition still only contributes 1.6% of the total electricity used across the States. However, with many new solar power technologies on the horizon, the increase could soon be much greater.

Article | April 15, 2020

Covid-19. It’s everywhere, and it’s probably the reason that your food cupboards are unusually more stocked than usual, or the fact that you’re likely reading this blog from the confines of your own home, as opposed to at your office or during your daily commute. But, despite the impact to business, economies, daily life and public health, there’s one bittersweet development which we can all take away from the outbreak – and that’s the considerable reduction of global CO2 emissions, and a resurgence of hope that it is fully possible for us to slow the onset of climate change and preserve our planet for future generations.