Transmission & Distribution Technology
so power companies forecast for future power consumption to efficiently balance the supply with demand. Tis sector is faced with twin problems associated with outages in peak demand, while too much supply leads to wasted resources. With advanced demand forecasting techniques provided by machine learning, utilities can ascertain hourly demand and peak hours for a day, allowing them to optimise the power generation process. Using information such as historical demand data, regions, population, weather patterns, events and so on, organisations can predict demand on any day or period in the future. Tis is essential for ensuring the utility can produce the resource required reliably and on time. With the increasing use of smart meters in the home, energy companies can now tap into this data provided by the IIoT to give a more accurate picture of supply and demand by gaining more insight from individual habits. Machine learning can provide tailored insight into the user to inform utilities that energy use during working hours is low, for example, so they can offer more efficient thermostats.
Energy usage and load shedding In businesses such as retail, where refrigeration accounts for nearly a third of the electricity bill, there are many techniques already in place that can be enhanced or even automated with machine learning. Areas such as load shedding, energy efficiency and alarm management will benefit greatly. For example, algorithms can be used to analyse alarms and identify which alarms are false and which are critical. From these examples, we see how machine learning can be a benefit to both the organisation and the customer, for predicting outage and system failure, and long-term replacement decisions, accurate load forecasting at the meter/ sub-meter level, better balancing of supply and demand, and detecting early warnings quicker, to providing tailored energy use, recommendations and reports, respectively. As already seen, machine learning capabilities will help users realise insights from the large amounts of data provided by sensors and the IIoT. Bringing it all together is visualisation through engineering models for structures such as substations and
Modelling a tower enables operators to monitor the asset throughout its lifecycle
plants. Engineering models or digital twin, are the computerised 3D model version of the physical asset, which maps everything associated to the asset using sensors to represent near real-time status, such as condition, performance and location. Where 3D models do not exist, users can quickly and easily create 3D models with technology such as ContextCapture, Bentley’s 3D reality modelling software. Here, using high-resolution photos, drones aid in the creation of digital engineering models of any structure. Te photos are transformed into detailed 3D models of all infrastructure data – in a less labour-intensive, cheaper and more efficient manner when compared to traditional methods. IT/OT convergence has become an accepted practice, with operators gaining new insight from known information. But misalignment in corporate strategy still results in silo building across many areas, especially within engineering technologies (ET), where engineering models often remain stranded, inhibiting the ability to leverage this information to optimise operations. Tey should be included with existing IT/OT conversation, driven by the IIoT as well as machine learning. Designing and testing new products, systems, and even plants in a virtual environment makes a compelling case, particularly from a cost perspective. Virtual models can tie these domains together over the whole lifecycle of an asset using its embedded digital DNA. From an asset management perspective, it’s about predicting a problem before it occurs and enabling maintenance to be performed at optimum rates and costs. Tis will be accelerated with the application of machine learning to make the decision-making process smarter, faster, and, more importantly, in context. Continually modelling a substation, transformer, or tower means that personnel can survey the asset throughout its lifecycle, from initial design to current condition, applying the difference in data to maintain up-to-date information on the equipment’s condition along the way. Tese models become the context within which utility companies can design, build, and operate their infrastructure projects. Reality modelling can link engineers in the field directly to the office, sharing information and data collaboratively. Trough the IIoT data provided by the
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