This article is about “Green energy and Green AI for sustainable services“. The article was first published in October 19, 2022 on Greenfrastructures.com then rewritten and republished on 15th December 2023.
Green energy, also known as renewable energy, refers to energy that is generated from natural resources that are replenished on a human timescale.
Unlike fossil fuels, which are finite and contribute to environmental pollution, green energy sources are considered more sustainable and environmentally friendly.
What is Green energy?
We all know that green energy is generated from natural resources, such as sunlight, wind or water. It often comes from renewable energy sources. The key with these energy resources that they don’t harm the environment as not releasing greenhouse gases.
Green energy often comes from renewable energy technologies for example
- solar energy harvested power from the sun with solar panels,
- wind power using wind turbines,
- geothermal energy,
- biomass and
- hydroelectric power using the flow of water etc..
To see energy as green a resource can not produce pollution.
As we understand not all sources used by the renewable energy industry are green e.g.: power generation that burns organic materials from sustainable forests may be renewable, but it is not necessarily green, due to the CO2 produced by the burning process.
Green energy sources are naturally replenished and often avoid mining or drilling operations that can be damaging the eco-systems.
What is AI?
For a starter artificial intelligence is a computer system that is able to perform tasks normally requiring human intelligence, such as
- visual perception,
- speech recognition,
- decision-making, and
- translation between languages.
What is Green AI?
The term covers sustainable AI services, which help to
- optimize and
- increase the efficiency of corporate processes,
with innovative solutions, by
- minimizing energy consumptions,
- reduce waste of resources,
- optimize energy generation and distribution,
- reduce CO2 emission.
In the last year everyone realized what AI is, because of Large Language Models, however when the article was first published not everyone would know about it.
Benefits AI bring to Renewable Energy Industries
As a growing proportion of energy comes from renewables, there is a decreased baseload generation from energy sources for example coal, which are responsible for grid inertia through the presence of heavy rotating equipment such as gas and steam turbines.
(Grid inertia: the inherent ability of an electrical grid to maintain a stable frequency in the face of changes in power demand and generation. In an electrical power system, maintaining a consistent frequency is crucial for the reliable operation of electrical devices and equipment.)
With little or no grid inertia, power grids could become less stable and more prone to power cuts.
AI & automation can help mitigate risks.
The real-time data collected by sensor technologies from wind and solar generation sources, as well as datasets of historical weather information derived from sky cameras and satellite imagery, can all be interpreted by AI.
In this way it can predict
- capacity levels and
- downtime periods, and act accordingly.
This helps to maintain stable energy grids. Using AI to interpret data enable grid operators to optimise the use of power grids by tailoring operations to weather conditions at any time.
As James Kelloway, Energy Intelligence Manager at National Grid ESO, explains, “Now, with AI, we can predict more accurately what renewables are likely to do, so we can control other power plants more accurately, like coal plants that take many hours to ramp up.”
According to Hendrik Hamann, Chief Scientist for Geoinformatics and Distinguished Researcher at IBM, AI can help to decrease operational costs: “We found that improved solar forecasts decreased operational electricity generation costs, decreased start and shutdown costs of conventional generators, and reduced solar power curtailment.”
AI able to predict when energy is most needed by consumers, meaning it can play
- a big role in battery storage and
- providing demand flexibility.
Moreover AI has a part to play when it comes to maintenance. It can almost instantly detect disturbances and system malfunctions according to the NES Fircroft Blog.
Case study I.
Data Reply has developed a quantum algorithm that allows to quickly plan the maintenance work performed by units operating throughout Italy. The solution also makes the use of resources more efficient, by reducing costs.
Case Study II.
2. Lavazza has chosen Amazon Web Services as its cloud platform and Reply, AWS Premier Consulting Partner, to support them in the adoption of machine learning models on AWS.
This improve the efficiency of their production processes, in order to have a positive impact on many aspects, from waste reduction to a higher level of quality and customer satisfaction.
Quality is a major concern, and the related data collected plays an important role in the definition of a good outcome of the production line.
Sensors – data – AI tools
Data gathered from sensors and unstructured sources may be used to build Artificial Intelligence tools, which can
- predict the quality of the next batch,
- reduce the quantity of waste products, as well as
- suggest improvements in the settings of the machines.
Tracking the product quality levels and monitoring the machine working state in real-time allows the production managers to identify potential anomalies in advance, preventing low production quality.
Among the challenges to keep high quality standards there are many variables that impact the quality of the output, like time and temperature to be used in the process.
Green AI Ballet Around Circular Economy
In the grand dance of technology and sustainability, Green AI emerges as the choreographer of a circular economy ballet.
Picture AI as the maestro orchestrating the harmonious movements of products on a circular stage. With a virtuoso flair, it guides designers in selecting materials that perform an environmental pas de deux.
In the recycling spotlight, AI, the master of ceremonies, directs automated sorting routines with a precision that would make a prima ballerina jealous.
The audience, a global community eager for eco-friendly encores, witnesses AI’s predictive maintenance prowess, ensuring that
- every machine pirouettes smoothly,
- minimizing waste and
- extending the encore of their mechanical performances.
This AI-powered ballet isn’t just a spectacle. It’s a transformative experience where products and services twirl gracefully through the stages of
- reuse, and
leaving a green footprint on the stage of sustainability.
AI for renewable energy systems
1. To learn more about AI for renewable energy systems I recommend a book about the topic. In this book you can read more about how Artificial Intelligence for Renewable Energy Systems addresses the energy industries remarkable move from traditional power generation to a cost-effective renewable energy system, and the paradigm shift from a market-based cost of the commodity to market-based technological advancements.
Featuring recent developments and state-of-the-art applications of artificial intelligence in renewable energy systems design, the book emphasizes how AI supports
– effective prediction for energy generation,
– electric grid related line loss prediction,
– load forecasting,
– predicting equipment failure prevention.
Looking at approaches in system modeling and performance prediction of renewable energy systems. This volume covers power generation systems, building service systems and combustion processes, exploring advances in machine learning, artificial neural networks, fuzzy logic, genetic algorithms and hybrid mechanisms. The book edited by Ashutosh Kumar Dubey, Sushil Narang , Arun Lal Srivastav, Abhishek Kumar, Vicente García-Díaz.
AI in the Green Energy Environment
2. To learn more about AI in the Green Energy Environment I recommend the Advances of Artificial Intelligence in a Green Environment book written by Pandian Vasant.
The book reviews new technologies in intelligent computing and AI that are reducing the dimension of data coverage worldwide. This handbook describes intelligent optimization algorithms that can be applied in various branches of energy engineering where uncertainty is a major concern.
Including AI methodologies and applying advanced evolutionary algorithms to real-world application problems for everyday life applications. This book considers distributed energy systems, hybrid renewable energy systems using AI methods. Moreover new opportunities in blockchain technology in smart energy.
Covering state-of-the-art developments in a fast-moving technology, this reference is useful for engineering students and researchers interested and working in the AI industry.
The article was first published: October 19, 2022| Topics: Artificial Intelligence, environment, green AI, green energy, greenfrastructures, smart city, smart energy, sustainable development
Others source: https://www.engerati.com; https://www.nationalgrideso.com; https://www.reply.com