Utility trends: cloud computing, machine learning, IoT, robotics, blockchain and cybersecurity are being deployed by utilities in their operations. These are the big current and future investment avenues for utilities. The challenge will be to make the large volumes of data useful and generate actionable insights from it. Consequently, utilities will need to deploy a range of new IT solutions to collect the data in consistent ways, transport it, secure, analyse and store it.

Listed below are the leading utility trends in big data, as identified by GlobalData.

Big data for predictive maintenance of wind turbines

The wind turbine generator is a key component of a wind farm. Smooth and continuous functioning is important for a wind farm’s steady output. Big data analytics is being used by energy companies to prevent turbine failures and ensure minimal downtime. Predictive models are being used by companies to get insights into different factors that contribute to the failure. The models process large sets of data collected from the generators. This can indicate if a certain turbine has more chances of an impending failure.

Edge computing for faster analyses

The traditional methods of performing analytics may no longer be viable. This is because the amount of data that is being generated is growing exponentially. Moreover, it may not be feasible to keep moving large volumes of raw data to centralised data stores or the cloud because of the sheer size of the data. Therefore, the prevalence of the IoT model of connected devices has led to an increased focus on edge computing.

Edge computing is gaining momentum among utilities. An increasing number of IoT vendors are flooding utilities and their consumers with their devices, thereby creating large volumes of data. While Cisco was previously the only prominent vendor, other vendors such as Dell, Intel, IBM, Microsoft, HP, and ARM are also providing these services now.

Big data for wind turbine placement accuracy

Metrological data is usually collected in vast stretches of land at a resolution of around 30 square kilometres. This is used by developers to identify large sites with high suitability for wind power. However, finding smaller zones where wind speeds are better than other zones within the site is a difficult task. With the advent of big data, the resolution of this spatial data has increased significantly giving separate data points for each 3 Sqkm land pockets making it easier to place wind farms more accurately in the high wind speed locations.

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Utility-linked smart thermostats

Utilities and electricity retailers have been trying to enter the automated home market the same way telecom and cable companies have done leveraging their large existing customer base. They have already tested the waters by supplying their customers with Energy Management Systems (EMS) and smart thermostats. These equipment are connected to the utility and are integrated into the smart meters in order to use the thermostats in the most optimal manner and maximise energy savings.

Big data generation and ownership by power utilities

Utilities that convince consumers to install smart meters and smart thermostats also gather and process data from these meters. This is in order to use it to better match demand and supply. Players such as smart home device manufacturers and service providers would be keen to partner with utilities. This would allow them to reach their existing customer base, and also to use the utilities’ historic smart meter and thermostat data.

This is an edited extract from the Big Data in Utilities – Thematic Research report produced by GlobalData Thematic Research.