Artificial intelligence (AI) and energy efficiency are not mutually exclusive career paths. As data collecting, computing power, and storage capacity rise significantly every year, AI and energy efficiency are increasingly becoming more intertwined. AI is a key facilitator of the fourth industrial revolution, and it has the potential to push performance to new heights.
Granting, AI is in its early stages of execution; it is positioned to disrupt the way we generate, transfer, and utilise energy. AI is capping the sector’s environmental impact when demand is gradually rising. We can see the effects of fossil fuel use on quality of life and air quality as the energy generating profile broadens. We have made a list of some of the ways AI-powered software can aid with energy management, energy storage, and energy forecasting and affecting sustainable development at the moment and in the coming future.
According to Greentech Media, the US energy storage market realised a gigantic milestone in 2017’s last quarter. The numbers were only expected to double, but they grew far quicker than the doyens had predicted. Hence, a renewable solution was sought.
With the increment in storage capacity and development of innovation, AI has come out to support efficiency and sustainability. Athena, an AI software, emphasises energy usage, allowing its clients to keep an eye on energy rate fluctuations and implement an effective energy storage solution.
Equipment failures and accidents are frequent occurrences within the energy sector. Many a time, human errors can cause colossal instrument failures and unalterable losses. AI is currently being used to spot defects in equipment by studying them. Error detection that is done in a timely manner saves money, time, and lives.
Sparkcognition provides solutions for a variety of industries, including energy and gas. To forecast any possible breakdowns of a critical framework, the organisation uses a combination of sensors, analytics, and publicly available data. The Department of Energy awarded the Sparkcognition in 2017 for employing AI to improve coal-fired plants.
Grids now collect energy from a variety of sources. Running and supervising enormous power grids systems is getting more complex. By reviewing vast datasets in short periods of time, AI software improves the efficiency and reliability of various energy sources. This has led to the rise of smart grids that are made to operate several sources simultaneously. For example, Siemens’ Active Network Management (ANM) is an AI-based computer software that manages grids independently. ANM monitors a grid’s interaction with specific energy demands and adjusts the grid as needed to improve efficiency.
DeepMind wants to incorporate AI into the UK’s power grid in collaboration with the National Grid of the United Kingdom. The project is anticipated to work on vast amounts of data to create predictive models for the rise in power demand.
Excessive energy consumption is a problem that both developed and developing countries are addressing. AI is being used to track people’s and businesses’ energy usage habits in order to achieve sustainable energy use. Numerous AI-based establishments are now giving pragmatic solutions to enhance energy utilisation. Alphabet’s Nest, for example, is an intelligent thermostat that reduces energy use by adapting to user behaviour.
Renewable energy sources are associated with a constant threat of unreliability. Despite being sustainable, renewable sources often fluctuate in their energy, proving inefficient in powering companies in the long haul. Xcel utilises AI-dependent data mining techniques to get weather reports with utmost precision and exhaustive details. The algorithms running these systems then point out motifs in the collected data sets to make significant forecasts.
The need to develop and integrate renewable sources of energy has been redundantly emphasised. Because renewable energy sources are inherently unstable, electricity providers have relied heavily on fossil fuels.