Need a perfect paper? Place your first order and save 5% with this code:   SAVE5NOW

GIS and AI in the Utility Sector

The utility industry is one of everyday life’s most basic and essential industries. This industry provides services associated with gas, water, sewage, and in this case, electricity. The electricity utility industry ensures enough power is generated and distributed to homes and businesses. Though the United States utility sector provides 100% electricity coverage to Americans, it faces various challenges, especially disruptive weather being top of the list. Further, climate and environmental change concerns have increased the debate on renewable energy, creating another challenge for the utility sector (Denholm et al., 2021). The importance of this sector to homes, especially cooling and heating during the different seasons, lighting, and running different businesses, makes it necessary to ensure its smooth operation and equal distribution.

However, the main problem with the utility industry is that it still needs to improve with outdated infrastructure. This is because the current utility infrastructure was constructed decades ago when industrialization in the country was gaining momentum. In such a utility, infrastructure, and the growth of towns and buildings, and industries, maintenance becomes relatively difficult. This is because of the significant investment needed for replacement and upgrade. Also, the current generation is relatively demanding customers. This is associated with the growth of technology, and the need to communicate fast and efficiently, expecting an immediate response. Barja-Martinez et al. (2021) give an example of how when there is an outage, the first response of this population is to tweet about it forgetting the more days they had consistent power. This increased power consumption and the demanding consumers make data-gathering, poor communication, and the industry’s unpredictability urgent problems to avoid a bad public image and increase efficiency.

The utility industry is slowly integrating technology into its operations; this is evident by the digitization of the grid, where businesses and families can know their daily energy consumption. However, this technology can also be leveled to predict any possible outages, the cause, and the proper interventions. This promotes early interventions reducing damages done, primarily through reducing the outage duration in unavoidable or providing a warning. The other problem involves energy consumption; the urban areas characterized by increased population and industries should have relatively supportive infrastructure compared to places where vast fields are used for agriculture or other uses other than living or doing business. Therefore, accurate information on the energy consumption of a particular area will require months or years of manual data collection, which may have discrepancies because nearly all businesses have low days of production depending on the market (Barja-Martinez et l., 2021). For example, even the utility industry has reduced electricity demand during the summer compared to winter. Having documented information, such differences in consumption are a problem requiring real-time reporting to adjust effectively.

As established, the utility sector is slowly incorporating technology into its operations. This includes using GIS to provide pictures of different parts of the network. Though it allows for precision in what is captured in the photographs, for example, a recently built network, it requires artificial intelligence to automatically feed such data into the system in an organized manner depending on the specifics required. This solves the data collection issue where, using real-time GIS information from GPS sensors and other devices, data on places with outages are recorded immediately. In most cases, the utility sector is characterized by sketches of networks where with the real-time data provided by the GIS, the AI can be tailored to locate the problem within the sketches and recommend solutions (Zhuang & Du, 2022). This is faster than consultations from the seniors to the engineers on the ground and parties involved during an outage. In this case, GIS and AI have used real-time information to restore networks faster and more effectively. GIS collects information on the various issues within the network while the AI sorts this data into comprehendible information about the network that can predict any problems, thus, alerting the consumers.

Predictability is essential in any business and industry, especially one that may interfere with other people’s operations. For example, when GIS and AI are used to predict outages, hospitals and businesses like the meat industry that run large freezers can ensure that their backup systems are operational to last the outage. GIS and AI are beneficial during decision-making; for example, GIS provides geographical visualization of a part of a malfunctioning network. With AI recognition features, the problem is identified and solved immediately. This also prompts the utility sector to plan for future happenings (Zhuan & Du, 2022). GIS and AI are advantageous when accuracy is needed; for example, accurate information about where the problem is within the network is important because interferences with other parts of the network that have no issues can create additional problems for other users. Therefore, through image recognition software of the AI and geotagged photos produced by the GIS system accuracy is enhanced, making the utility sector more effective. AI is rich in data and can collect real-time data, which can be used to predict places where renewable energy is most efficient based on patterns of individual consumption (Denholm et al., 2021). Similarly, GIS geographic capabilities can determine where this energy is most effective in terms of production, for example, where winds blow heavily to harness wind energy. With the two combined, renewable energy can become a reality sooner; thus, the two technologies are beneficial in solving current world problems.


Barja-Martinez, S., Aragüés-Peñalba, M., Munné-Collado, Í., Lloret-Gallego, P., Bullich-Massague, E., & Villafafila-Robles, R. (2021). Artificial intelligence techniques for enabling Big Data services in distribution networks: A review. Renewable and Sustainable Energy Reviews150, 111459.

Denholm, P., Arent, D. J., Baldwin, S. F., Bilello, D. E., Brinkman, G. L., Cochran, J. M., … & Zhang, Y. (2021). The challenges of achieving a 100% renewable electricity system in the United States. Joule5(6), 1331-1352.

Zhuang, F., & Du, W. (2022). Intelligent Decision Support System for Distribution Network Planning Based on Artificial Intelligence and GIS Technology. In 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) (Vol. 5, pp. 1934-1938). IEEE.


Don't have time to write this essay on your own?
Use our essay writing service and save your time. We guarantee high quality, on-time delivery and 100% confidentiality. All our papers are written from scratch according to your instructions and are plagiarism free.
Place an order

Cite This Work

To export a reference to this article please select a referencing style below:

Copy to clipboard
Copy to clipboard
Copy to clipboard
Copy to clipboard
Copy to clipboard
Copy to clipboard
Copy to clipboard
Copy to clipboard
Need a plagiarism free essay written by an educator?
Order it today

Popular Essay Topics