Artificial Intelligence in Energy Efficiency

Artificial intelligence (AI) and energy efficiency are not mutually exclusive career paths. AI and energy efficiency are gradually becoming more interconnected as data collection, calculating power, storage capabilities grow exponentially every year. AI is an enabler of the fourth industrial revolution, and it is laced with the possibility of delivering the next level of performance.

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. The energy generation profile is branching out, and we see the consequences of fossil fuel utilisation on quality of life and air quality. 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.

Renewable Storage

According to Greentech Media, the US energy storage market realised a gigantic milestone in 2017’s last quarter. The numbers were only projected to double, but increased faster than the optimistic doyens had anticipated. 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, highlights energy utilisation, thus enabling its clients to keep an eye on fluctuation in energy rates to allow an effective energy storage solution.

Accident Management

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 now being utilised to identify flaws by observing the instruments. Timely detection of errors saves money, time, and lives.

Sparkcognition offers solutions for areas in energy, gas, among others. The company utilises an assortment of sensors, analytics, and available data to predict any potential failures of a crucial framework. The Department of Energy awarded the Sparkcognition in 2017 for employing AI to improve coal-fired plants.

Grid Management

Contemporary grids collect energy from several energy sources. Running and supervising enormous power grids systems is getting more complex. An AI software increments efficiency and stability to these energy sources via its ability by evaluating massive datasets in short durations. This has led to the rise of smart grids that are made to operate several sources simultaneously. For example, Active Network Management (ANM), developed by Siemens is an AI-based computer program, which independently runs grids. ANM keeps an eye on a grid’s interaction with specific loads of energy and alters the grid accordingly to increment efficiency.

DeepMind, in conjunction with the United Kingdom National Grid, plans to integrate AI into the UK’s electricity system. The project is anticipated to work on vast amounts of data to create predictive models for the rise in power demand.

Power Usage

Both developed and emerging economies are facing the same excessive energy consumption challenges. To realise sustainable energy consumption, AI is utilised to keep track of the power consumption behaviour of people and their enterprises. Numerous AI-based establishments are now giving pragmatic solutions to enhance energy utilisation. An example is Alphabet’s Nest, an intelligent thermostat that lowers power usage by adjusting to user behaviour.

Energy Prediction

Renewable energy sources are synonymous with a continuous challenge of undependability. 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. Due to the unreliable nature of renewable sources, power suppliers greatly depended on fossil fuels. Nonetheless, with the incorporation of AI in renewable energy sources, an increment in energy efficiency is not far off.