For renewable energy generation to take on a greater role in the global energy mix, artificial intelligence (AI) may be the key to achieving the highest efficiency. Let’s explore what this concept means and how it could affect the energy industry as a whole.
What is AI?
Artificial intelligence is a concept that emerged almost at the same time as computer science. From the beginning, researchers tried to imitate the way people think, seeking to equip computers with some form of intelligence. Currently, the most widely accepted definition of the field is the study of “intelligent agents.” “Intelligent agents” are systems that are, in some way, capable of perceiving their environment and responding to it in some way, with the aim of achieving a set of goals.
One of the simplest examples of an intelligent agent is a thermostat. It senses the temperature of a room (its environment) and interacts with it to achieve a specific temperature (its goal). In this case, the environment is part of the real world, but that is not necessarily true of all intelligent agents. The goals of this system also require relatively simple logic to achieve, but that would not be enough as the complexity of the goals or the environment increases.
Many techniques and technologies have been proposed over the decades, with varying degrees of success. Some were so successful in their respective fields that they became commonplace and stopped being considered “intelligence.” Other techniques have experienced waves of renewed interest as technology advances, such as artificial neural networks. These are a subset of “machine learning”: the design of systems that learn from experience and adapt to their environment. Other techniques rely on mathematical optimization, statistics, or logic systems to achieve this goal
How can artificial intelligence be used in the renewable energy sector?
In a survey of more than 100 renewable energy workers, over 90% said their jobs would benefit from digitization and automation, and that some part of their workflow could be automated by AI, particularly in design, operation, and maintenance.
How can this be done? There are two main ways to apply artificial intelligence in the energy sector (or any sector): automated decision-making and assisted decision-making.
Automated decision-making involves computer systems processing information without human intervention, making it possible to handle complex tasks faster than people could in the same amount of time. One example is automated AI systems for scheduling preventive maintenance at a solar photovoltaic installation.
A limitation of this approach is that automated decisions can be distorted by different kinds of bias, which may result in incorrect decisions. This can cause problems in critical systems and, therefore, automated decision-making is not viable for all applications.
Assisted decision-making can address this by keeping responsibility for the actual decision with people. AI is therefore focused on providing information that enriches the process. This brings the added flexibility of people applying their knowledge and experience to the computer system’s analysis. AI-assisted systems can be used for tasks that require nuanced decision-making, such as environmental impact analysis for proposed solar and wind farm sites.
Risk and opportunity analysis
Image recognition, a form of artificial intelligence, has a key role to play in validating data. Investors and developers can use image recognition and other algorithms to identify the potential risks and opportunities of a prospective project.
Algorithms can then generate preliminary cash flow models that show developers whether a project has good potential. By automating the project modeling process, AI tools can quickly identify profitable projects and free up teams to focus on closing deals efficiently.
Manage supply and flow
Electric utilities and grid operators can use artificial intelligence and machine learning to forecast generation, schedule maintenance, and manage power flows. At the same time, consumers can receive up-to-date supply information to help manage consumption, distributed generation, and storage to reduce energy bills. The growing adoption of smart meters is creating data that AI algorithms can use to forecast demand and load of the grid, shifting the load to optimize consumption from solar and battery systems .
Output power forecasting
AI systems can use wind speed and irradiance forecasts to predict short-term renewable energy generation in minutes rather than days. This helps maintain grid stability and dispatch, optimize plant availability, and schedule maintenance.
Smarter economics
AI programs can combine machine-learning weather models, historical datasets, real-time information from local weather stations, satellite imagery, cameras, and sensor networks. Improved forecasting can lead to more efficient management of conventional generators and reduce the costs of starting up and shutting down units, making optimal use of plants by adjusting output to changing weather conditions . This can also reduce the cost of solar energy curtailment.
More accurate forecasting of renewable energy generation allows energy producers and traders to submit bids in wholesale and balancing markets.
The long-term impact of AI on renewable energy
AI applications are revolutionizing the technology and digital sectors. They have become an integral part of everyone’s everyday interaction with the digital world: search engines are one of the main gateways to the internet; recommendation systems curate the content presented by social networks, streaming services, and online marketplaces; and virtual assistants are present on almost every platform. Such groundbreaking changes are gradually emerging in many other industries, but there is still significant untapped potential for AI applications in other fields.
The renewable energy industry in particular is constantly reinventing itself, embracing research and innovation to adapt to ever-increasing demand. As a result, it is ideally placed to successfully adopt AI innovations and tailor them to its specific needs. Even today, new and established tools use elements of AI to streamline processes and create capabilities that were previously impossible. If the trend continues as it is today, improved AI tools could become as important to the future of engineering as computer-aided design (CAD) software is today.
Automate your photovoltaic project with RatedPower
AI can be used to tackle the complex challenges of solar plant design, resulting in cost savings and improved performance, and ultimately a better return on investment.
Source: https://ratedpower.com/
What is AI?
Artificial intelligence is a concept that emerged almost at the same time as computer science. From the beginning, researchers tried to imitate the way people think, seeking to equip computers with some form of intelligence. Currently, the most widely accepted definition of the field is the study of “intelligent agents.” “Intelligent agents” are systems that are, in some way, capable of perceiving their environment and responding to it in some way, with the aim of achieving a set of goals.
One of the simplest examples of an intelligent agent is a thermostat. It senses the temperature of a room (its environment) and interacts with it to achieve a specific temperature (its goal). In this case, the environment is part of the real world, but that is not necessarily true of all intelligent agents. The goals of this system also require relatively simple logic to achieve, but that would not be enough as the complexity of the goals or the environment increases.
Many techniques and technologies have been proposed over the decades, with varying degrees of success. Some were so successful in their respective fields that they became commonplace and stopped being considered “intelligence.” Other techniques have experienced waves of renewed interest as technology advances, such as artificial neural networks. These are a subset of “machine learning”: the design of systems that learn from experience and adapt to their environment. Other techniques rely on mathematical optimization, statistics, or logic systems to achieve this goal
How can artificial intelligence be used in the renewable energy sector?
In a survey of more than 100 renewable energy workers, over 90% said their jobs would benefit from digitization and automation, and that some part of their workflow could be automated by AI, particularly in design, operation, and maintenance.
How can this be done? There are two main ways to apply artificial intelligence in the energy sector (or any sector): automated decision-making and assisted decision-making.
Automated decision-making involves computer systems processing information without human intervention, making it possible to handle complex tasks faster than people could in the same amount of time. One example is automated AI systems for scheduling preventive maintenance at a solar photovoltaic installation.
A limitation of this approach is that automated decisions can be distorted by different kinds of bias, which may result in incorrect decisions. This can cause problems in critical systems and, therefore, automated decision-making is not viable for all applications.
Assisted decision-making can address this by keeping responsibility for the actual decision with people. AI is therefore focused on providing information that enriches the process. This brings the added flexibility of people applying their knowledge and experience to the computer system’s analysis. AI-assisted systems can be used for tasks that require nuanced decision-making, such as environmental impact analysis for proposed solar and wind farm sites.
Risk and opportunity analysis
Image recognition, a form of artificial intelligence, has a key role to play in validating data. Investors and developers can use image recognition and other algorithms to identify the potential risks and opportunities of a prospective project.
Algorithms can then generate preliminary cash flow models that show developers whether a project has good potential. By automating the project modeling process, AI tools can quickly identify profitable projects and free up teams to focus on closing deals efficiently.
Manage supply and flow
Electric utilities and grid operators can use artificial intelligence and machine learning to forecast generation, schedule maintenance, and manage power flows. At the same time, consumers can receive up-to-date supply information to help manage consumption, distributed generation, and storage to reduce energy bills. The growing adoption of smart meters is creating data that AI algorithms can use to forecast demand and load of the grid, shifting the load to optimize consumption from solar and battery systems .
Output power forecasting
AI systems can use wind speed and irradiance forecasts to predict short-term renewable energy generation in minutes rather than days. This helps maintain grid stability and dispatch, optimize plant availability, and schedule maintenance.
Smarter economics
AI programs can combine machine-learning weather models, historical datasets, real-time information from local weather stations, satellite imagery, cameras, and sensor networks. Improved forecasting can lead to more efficient management of conventional generators and reduce the costs of starting up and shutting down units, making optimal use of plants by adjusting output to changing weather conditions . This can also reduce the cost of solar energy curtailment.
More accurate forecasting of renewable energy generation allows energy producers and traders to submit bids in wholesale and balancing markets.
The long-term impact of AI on renewable energy
AI applications are revolutionizing the technology and digital sectors. They have become an integral part of everyone’s everyday interaction with the digital world: search engines are one of the main gateways to the internet; recommendation systems curate the content presented by social networks, streaming services, and online marketplaces; and virtual assistants are present on almost every platform. Such groundbreaking changes are gradually emerging in many other industries, but there is still significant untapped potential for AI applications in other fields.
The renewable energy industry in particular is constantly reinventing itself, embracing research and innovation to adapt to ever-increasing demand. As a result, it is ideally placed to successfully adopt AI innovations and tailor them to its specific needs. Even today, new and established tools use elements of AI to streamline processes and create capabilities that were previously impossible. If the trend continues as it is today, improved AI tools could become as important to the future of engineering as computer-aided design (CAD) software is today.
Automate your photovoltaic project with RatedPower
AI can be used to tackle the complex challenges of solar plant design, resulting in cost savings and improved performance, and ultimately a better return on investment.
Source: https://ratedpower.com/
