Artificial Intelligence (AI) is continuously expanding its scope and gaining wider coverage in countless industries. One such sector is the energy industry which is currently undergoing fundamental changes.
The Need for Artificial Intelligence in Energy and Utilities
At its core, the energy industry encompasses three major areas of operation:
- Electric Power Generation – this includes fossil fuels as well as renewable sources
- Transmission and Distribution – a combination of high-voltage power lines and the electric grid
There are clear driving factors behind the shifts taking place in these operational sectors. On one hand, there is an increasing pressure to reduce reliance on fossil fuels for power generation. Carbon emissions and the rising cost of electricity are other concerns.
Due to this combination of factors, the global demand for renewable energy is similarly on the rise. However, there are challenges associated with renewable energy as well. To start with, since it is mostly weather-dependent, it is rather inconsistent. A string of cloudy or calm days for instance could lead to solar and wind power shortfalls. Similarly, there is always the possibility of generating much more energy than the level of consumption.
Artificial intelligence in energy and utilities has as a result gained widespread application to address the weaknesses of the different models. Notably, there are already lots of practical applications illustrating the confluence of the two fields. In this post, we examine some of the outstanding ways in which AI is powering the energy industry.
Applications of Artificial Intelligence in the Energy Sector
- Optimized Energy Use
Artificial intelligence is facilitating the efficient monitoring of energy users’ consumption habits. Access to such data allows users to get solutions for optimized use. A great example of such a system is Alphabet’s Nest Learning Thermostat.
Following installation, the device uses machine learning to analyze consumption habits in the user’s household. With this information, it adjusts temperatures to reduce the heating bill. For instance, it will automatically turn down heating when no one is home. As a result, it claims to save users between 10 and 12% on heating costs and 15% on cooling.
Nnergix, a Barcelona company is another great example. It makes use of AI and machine learning-based algorithms to forecast weather conditions. Consequently, it can predict the amount of renewable energy a plant can produce.
- Forecasting Energy Generation and Demand
At present, the aforementioned inconsistency associated with renewable energy sources makes it necessary to have backup power sources. In most cases, these come in the form of coal plants and diesel generators, both of which have a high carbon footprint. Similarly, there is a need for costly energy storage solutions for managing excess production.
AI is making it possible to forecast demand for electricity as well as weather conditions and generation capacity. With this information on hand, the management of fluctuations in power generation is becoming more efficient. Eventually, the technology can reduce the need for costly storage and backup solutions.
- Artificial Intelligence Energy Management
Renewable energy is rapidly growing and it is said to account for at least a fifth of global electric energy production. However, many fail to realize that with this growth come unique challenges. System operators have to find ways to integrate renewable sources into the existing grid.
In order to maintain consistency and reliability in power generation, there has to be a management system in place to merge the two worlds. Artificial intelligence energy management has proved invaluable in this respect.
A good example of this is Stem’s Athena system which combines AI and energy storage. It creates virtual power plants that use machine learning to determine the best time to purchase energy. It also aggregates power from Distributed Energy Resources (DERs), mostly in the form of rooftop solar systems. By aggregating multiple data streams and combining forecasting and machine learning, it optimizes renewable energy management and operations.
- Failure Management
The energy industry has experienced numerous catastrophic failures over the years. For example, in 2017, a boiler exploded at a power plant in India causing 32 fatalities. Investigations pointed to blockages in a gas pipe as the root cause.
This is by no means an isolated case in the sector, where equipment failures are frequent when there are no constant checks. The use of artificial intelligence for equipment observation and failure detection is one of the most effective ways to address this. It makes use of sensor data from equipment units and as such greatly reduces downtime and maintenance costs.
A company known as SparkCognition is among the startups attempting to address this need. It uses a combined approach consisting of sensors, analytics and operational data to make predictions. Thus, it provides useful information on when a critical piece of equipment is likely to fail.
- Autonomous Grid
In the US, the Department of Energy is working on the development of an autonomous grid using AI and machine learning. The project is meant to draw from Stanford’s VADER system. VADER (Visualization and Analytics for Distributed Energy Resources) is a project working on developing tools for oversight of electricity distribution frameworks.
On the existing system, there should only be one-way movement of energy from the supplier to the user. But due to the prevalence of renewable energy sources, loads are currently traveling both ways. Some users generate excess energy and send the extra back to the grid.
In addition, there is an increase in electric energy demand as such equipment as electric vehicles become more and more popular. The system aims to integrate data and offer real-time intelligence through a unified platform so as to address such problem areas and improve the resilience of the grid.
- Investment Optimization
Another area in which AI is powering the energy sector is in optimizing investment. To illustrate, BP Venture’s AI company, Beyond Limits, is using the technology for what it refers to as upstream explorations.
In simple terms, they use artificial intelligence to increase their chances of success in locating oil reserves and drilling wells. To achieve this, they dig through geological and seismic models to drive efficiency and tip the odds in their favor.
The Other Side of the Coin
The use of artificial intelligence in energy and utilities is not without its risks. One of the most outstanding challenges has to do with the possibility of cyberattacks. As the energy industry becomes more and more automated, it is becoming more vulnerable to potential attacks. There is a need to ensure security and minimize the potential damage of such attacks.
Another risk has to do with the privacy of customer data. Artificial intelligence systems create datasets from users’ behavior patterns. The collection and storage of such data from individual users raise concerns about data privacy. Malicious actors can infer all kinds of information from such data, posing a risk to users.
The Future of Artificial Intelligence in Energy and Utilities
Ultimately though, the benefits of artificial intelligence on the energy sector far outweigh the above potential risks. Not only will it enhance the efficiency of the industry as a whole but it also has what it takes to empower consumers. With foresight over their energy profiles, ability to monitor consumption and potential cost reductions, consumers have more to gain than what they stand to lose.
Similarly, stakeholders in the industry can reduce operational and maintenance costs as well as minimize risks associated with failure. Fortunately also, players in the industry are already aware of these potential dangers and are taking steps to keep them in check.
Overall, the convergence of artificial intelligence and energy is setting the pace for the future of electricity production and use.
Latest posts by Janica San Juan (see all)
- 7 Steps to Correct Data Preparation for Machine Learning - January 22, 2020
- Machine Learning Chatbots: What You Should Know about Neural Conversation Agents - December 18, 2019
- Microsoft Cognitive Services – Democratization of AI - December 4, 2019