The Utility industry is rapidly shifting from a traditional and regulated environment to a technology oriented market. There is a constant struggle in collating data and optimizing manpower. With the pandemic these issues have been expedited and the reliance on technology for smarter optimization of infrastructure has increased monumentally. There is an urgent need to create a balance between the supply and demand. This is where Artificial Intelligence (AI) and Machine Learning (ML) can help. Data Science aided with AI and ML is causing tremendous positive developments for the utility space. According to McKinsey & Company, Digital optimization can boost profitability by 20 to 30 percent in Utilities by utilizing smart meters for grids, digital productivity tools and automating back-office processes.
AI in Customer Service
According to a report by Gartner, the majority of utilities’ investment in AI is earmarked for customer service. 86% of utilities use AI for digital marketing, call center support and customer application. This stands as a testament to the fact that investment in AI and ML can deliver high ROI by improving speed and efficiency and enhancing customer experience. Customer facing AI is a low risk investment as customer enquiries are mostly repetitive like billing enquiries, payments, new connections etc. AI can deliver tangible results for business in the customer service front.
Automatic Meters for Energy conservation
The Automatic Meter Reading (AMR) System has made a breakthrough in the record of energy consumption. Manual entry and billing systems are time consuming, error prone and expensive. AMR can enable large infrastructures to collect data with ease and conduct analysis to find cost centers and opportunities to improve efficiency in electric, natural gas, water industries and more. It provides real time billing information for budgeting. It has the advantage of being completely accurate when compared to manual entry. Additionally it stores the data at distribution points within the utility networks. This can be conveniently accessed on a network using mobile and handheld devices. Energy consumption can be tracked to aid conservation and end fraudulent consumption of energy.
Smart grid options using Predictive analytics
Utilities can benefit immensely from forward thinking technologies in the energy sector and help in building smart power grids. The energy sector heavily relies on a complex infrastructure which can face multiple issues as a result of maintenance issues, failure in system or equipment, demand surges, weather conditions and misallocation of resources. A lot of energy can be wasted due to overloading and congestion. The Energy grids also produce a huge amount of data which can help in risk mitigation if utilised properly. With the large volume of the data that constantly passes through the grid it becomes challenging to collect and aggregate it. The operators at times miss these insights which leads to malfunction or outages. With the help of ML algorithms the insights can be obtained for smooth functioning of the grids. To maintain the data accurately resources are required. Automated data management can significantly aid in this aspect. With the help of predictive analytics the operators can predict grid failures before they reach the customers and create better customer satisfaction and save the financial loss.
Sustainable and efficient energy consumption
A major benefit will be in terms of energy consumption as allocation of energy based on demand can save resources and help in load management and forecasting. Additionally AI can analyse operational data or statistics about vegetation or growth patterns and proactively deal with wildfires. This can lead to a sustainable and efficient system. To overcome weather related maintenance issues automation can help in receiving signals and prioritizing which area needs to be attended to save money and reduce downtime. To achieve this the energy sector is adopting ML capabilities as they need fast and easy access to automation.
Building codes and architecture is a mammoth task that could require weeks to achieve. Solutions are available that can help developers to test these applications seamlessly without system interruption. Integrating AI and ML in data management platforms can help developers and enable the data-science teams to spend more time innovating and less on maintenance. With the rise in computational power and accessibility to cloud the deep learning algorithms train faster and their cost can be optimized as well. AI and Machine Learning is demonstrating their impact on different aspects of business.Keeping the remote working in account AI can enhance human jobs. They can help with data collection and analysis and provide actionable inputs. Data analytics platforms can throw lights on areas of inefficiency and help providers keep their costs down.
Digital transformation might appear intimidating but its opportunities are worth more than the cost and risk associated. Slowly every utility will undergo digital transformation as it has already started taking roots in the industrial sectors. This AI led transformation will improve productivity, revenue gains, make networks more reliable and safe, accelerate customer acquisition, and facilitate entry into new areas of business. The global digital utility market is expected to grow to $244 bn by 2022 according to a study by MarketsandMarkets.com. As the sector evolves the AI and ML advantages will come into play and lead to smarter grids, efficient operators and high customer satisfaction. The companies that will take advantage of this opportunity will be ready for future market challenges.