According to an early-2021 report, the predictive analytics market was valued at roughly USD 5.7 Billion in 2019, and is expected to balloon into a whopping USD 22.1 Billion industry by the end of 2026.

The increasingly tech-savvy businesses are deploying predictive analytics at every level of their organization to unlock hidden efficiencies, identify new opportunities, and maximize their bottom line. Sales, marketing, operations, asset management, risk management – you name it, predictive analytics is everywhere.

Businesses across the spectrum utilize predictive analytics in creative ways to gain a competitive edge in their respective industries. However, the sudden and widespread adoption of predictive analytics by businesses is no mere coincidence. It was made possible, in a big way, by the internet of things (IoT) revolution unfolding at the moment. While there were only 4.7 billion IoT devices in 2016, there will be an estimated 11.6 billion of them worldwide by 2025. All those devices collecting and churning out colossal amounts of data offer businesses an excellent opportunity to extract unprecedented actionable insights from them. Predictive analytics is precisely doing that!

Utilities is one such area where predictive analytics is revolutionizing the entire industry.

Impact of Predictive Analytics on Utilities

There are numerous applications of predictive analytics in the utilities sector, with far-reaching implications for how the industry operates as a whole. Here’s a closer look at a few of them:

  • Predictive Maintenance

The modern utility infrastructure is highly digitized and produces vast amounts of highly specific data. The power generation equipment, transmission infrastructure, and even last-mile distribution systems are equipped with sensors that generate a variety of data, ranging from the status of operating machinery to granular-level power consumption patterns. Using this data, businesses can apply predictive models to estimate the remaining useful life of the machinery and schedule preventive maintenance before the equipment encounters failures.

A proactive maintenance strategy reduces machine failures and their consequences – downtime, reputation hit, drop in revenues, expensive repairs, and even threats to human safety.

  • Demand Management & Forecasting

Today’s consumer expects the best quality of service. They want uninterrupted power, and if there are scheduled interruptions, they demand to be notified of the same well in advance. However, utilities have traditionally struggled with forecasting demand. Historical consumption data, weather data, hourly consumption changes, seasonality, performance, and other factors affect the optimum power needs of consumers.

This poses a problem for the utilities. Storing power for a long duration drastically increases their costs, while under-production of power leads to outages. Thankfully, predictive analytics makes it possible to use historical data for more reliable predictions regarding power demand and generate optimum power to meet their needs.

How Can Artificial Intelligence Help Predictive Analytics?

Although predictive analytics allows utilities to extract extraordinary amounts of actionable insights from their data, it’s primarily limited in its scale, scope, and effectiveness. Artificial intelligence, together with predictive analytics, improves its usefulness on all three fronts.

What makes AI so powerful is its nature: it can “make assumptions, test them, and learn continuously from their results.”

While predictive analytics, although automated, is largely based on a specific set of rules. Its predictive models are rigid, and any changes are made by humans. AI, with its capability to actively learn from new information and make changes to its predictive models, can make your predictive analytics truly “intelligent.”

Let’s take a closer look at how the introduction of AI can transform predictive analytics for utilities:

  • Preventive Maintenance

Instead of merely predicting failures with a certain degree of probability, AI-powered predictive analytics can identify the source of the problem based on the symptoms and isolate the issue for the maintenance people. By discovering the root cause of “potential” failures, AI-driven predictive analytics can help resolve them before they explode into expensive failures.

A more nuanced problem in asset-intensive businesses is the identification of operational inefficiencies. Besides discovering potential failures before they happen, AI can also help companies unearth hidden or invisible inefficiencies in the workforce. What’s more, with built-in learning mechanisms, AI is virtually limitless in terms of scale and scope. It can be deployed across the entire grid and configured to work with any type of data.

  • Demand Management & Forecasting

Predictive analytics can only play by a set of predetermined rules. AI, on the other hand, can explore endless possibilities. Combining AI with predictive analytics for demand management opens up unlimited potential for utilities. This means analyzing massive amounts of disparate data from a complex mix of sources – weather, location, age of equipment, holidays, real-time demand patterns, etc. Unlike standalone predictive analytics, AI can detect patterns in data and dive deeper into the context of the said data.

What’s even more important is that AI can use these insights to make decisions in real-time, or provide forecasts for short-term (minutes) or longer-term (hours). For instance, customers who understand the grid demands are able to better optimize their generation, and provide an accurate forecast of generation to the grid. Further, the AI can tailor its projections to the responses and actions to the individual operator’s behavior. This gives customers a degree of personalization that no other tool can offer, ultimately leading to maximum profitability.

The UptimeAI Advantage

The impact of AI on predictive analytics goes beyond accuracy, effectiveness, and scale. In many cases, it is getting closer to a near-human level of contextual insights at superhuman efficiency. UptimeAI specializes in utilizing AI-augmented predictive analytics and forecasting capabilities to give utility companies granular-level visibility into their vast infrastructure – equipment condition, maintenance needs, potential sources of failure, and their origins – and guide the workforce to manage generation without operational mishaps and inefficiencies.

As the industry’s first AI expert, UptimeAI offers the most advanced and accurate fault prediction and forecasting system, which gets “smarter” each day. This translates to fewer alarms, more accurate predictions, and highly contextual recommendations. UptimeAI is baked with thousands of failure modes and historical equipment performance data. In short, expect it to perform right off the bat and save your engineers precious time, energy, and effort by identifying specific issues and their root causes, instead of bombarding you with alarms.

More importantly, UptimeAI combines maintenance, measurement, and knowledge management into a highly reliable, efficient, and accurate service. UptimeAI turns these aspects into an “as-a-service” model that’s largely automated. UptimeAI delivers 2x value, seamlessly assimilates and works with existing data, and breaks even on your ROI within 12 months.

To learn more about UptimeAI – how it works, what benefits it offers, and how it can transform your operations and save you substantial costs – schedule a demo today.


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