Real World Applications of Machine Learning in Utilities

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Machine Learning
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Machine learning provides huge promises in utilities to deliver better customer services.  

In today’s digital transformation age, a majority of utility companies are focusing their IT effort on implementing software integration and data provisioning. They are increasingly turning to technologies like machine learning and advanced analytics to improve forecasting of energy consumption, destructive storms and equipment maintenance, in particular, as the volume of data produced by sensors on meters and pipelines continues growing.

Using machine learning with the internet of things (IoT), utility companies can create algorithms that use data and industry intelligence to envisage potential breakdowns in machinery and wider systems. It allows organizations to predict and spot outages, enabling better resource management and minimized downtime. As leveraging IoT will help gather data, machine learning will use the data collected to derive actionable insight for utilities.

Here are some real-world applications of machine learning that redefine the utilities industry.

Predictive Asset Maintenance

Utilities face tremendous pressure owing to aging assets and workforce, intelligent devices and an immense volume of data. In this way, the efficiency of machine learning algorithms is tremendous. With machine learning algorithms and text mining methods, utilities companies will be able to leverage current and historical data that can help create data analytics models. Predictive analytics solutions then enable utilities to take well-timed and accurate decisions concerning asset health. Besides, active application of probability modeling assists in increasing performance, predicting occasional failures in the functioning and consequently, lowering maintenance costs.

Energy Consumption Management

Machine learning helps forecasting energy consumption by processing historical data. Effective management of energy data opens new possibilities in the collection and analysis of data, as well as in creating more accurate predictions. Smart energy management systems have fortified abilities to coalesce smart end-use devices, distributed energy resources, and advanced control and communication. Using big data analytics here empowers dynamic management systems in Smart Grids, contributing to the optimization of the energy flows between the providers and consumers. In turn, the efficiency of the energy management system relies on load forecasting and renewable energy sources.

Energy Theft Detection and Prevention

As modern smart grids rely on advanced metering infrastructure networks for monitoring and billing purposes, energy theft is one of the most expensive types of financial consequences in utilities. This could cause significant damage to power grids, affecting power supply quality and lessening operating profits. In this regard, using machine learning algorithms can provide real-time data on non-technical losses. The algorithms employed train on a customer’s historical energy consumption that predicts future energy usage. ML algorithms flag potential electricity theft by detecting abnormality between the predicted energy usage and current energy usage.

Real-Time Statistics for Operational Efficiency

Utilities companies can use smart data applications and software to detect the matters, operations and functions worth of optimization. Real-time monitoring and visualization provide data pertaining to time, activity rate, and state of some operations. Already, machine learning models are being utilized to seize real-time data, fueling operational productivity, reducing costs, complying with regulations, and attaining a timely understanding of customers’ energy use. Moreover, the availability of timely and diverse data drives smart decisions across the whole value chain, delivering safe and cost-effective operations, and better customer experience.