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<h2>Kelowna's Strategic Location</h2>
<p>Kelowna, nestled in the heart of British Columbia's Okanagan Valley, is quickly emerging as a significant hub for AI innovation in energy infrastructure. Its strategic location provides a unique blend of natural resources and technological advancements, making it an ideal setting for groundbreaking developments.</p>
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<h3>Collaboration with Local Universities</h3>
<p>The city is home to several leading educational institutions that are actively collaborating with tech companies to advance AI research. These partnerships foster a thriving environment for innovation, where students and professionals alike contribute to cutting-edge projects in energy management and sustainability.</p>

<h2>Supportive Government Policies</h2>
<p>The provincial government has recognized AI's potential in transforming energy infrastructure and has implemented supportive policies to encourage growth in this sector. Incentives for clean energy projects and AI development have attracted numerous startups and established companies to Kelowna.</p>
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<h3>Access to Renewable Energy Resources</h3>
<p>Kelowna boasts abundant renewable energy resources, including solar, wind, and hydroelectric power. These resources are integral to the development of AI-driven solutions aimed at optimizing energy consumption and reducing carbon footprints.</p>

<h2>Thriving Tech Community</h2>
<p>The city's tech community is vibrant and growing, with numerous tech meetups, hackathons, and conferences held regularly. These events bring together innovators and thought leaders who are passionate about leveraging AI to revolutionize energy infrastructure.</p>
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<h3>Investment in AI Startups</h3>
<p>Kelowna has seen a surge in investment in AI startups focusing on energy solutions. Venture capitalists and angel investors are drawn to the city's dynamic ecosystem, providing funding and mentorship to promising companies.</p>

<h2>Case Studies of Success</h2>
<p>Several success stories have emerged from Kelowna's AI and energy sectors. Companies are deploying AI to enhance grid reliability, predict energy demand, and optimize renewable energy integration, setting benchmarks for the industry.</p>
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<h3>The Future of AI in Energy</h3>
<p>As Kelowna continues to grow as a hub for AI innovation, the future looks promising. Ongoing research and development efforts are expected to yield more efficient and sustainable energy solutions, cementing the city's role as a leader in this transformative field.</p>

Why Kelowna Is a Hub for AI Innovation in Energy Infrastructure

<h3>What 21 pages of field data say about catching insulator failure before it becomes an outage or an ignition.</h3><p>INMR is where the transmission and insulator world goes to read serious engineering. This week they published our technical contribution, authored by CRWN's Jordan Edwards: <a data-mce-href="https://www.inmr.com/iot-devices-ai-machine-learning-predict-failures-on-remote-transmission-lines/" href="https://www.inmr.com/iot-devices-ai-machine-learning-predict-failures-on-remote-transmission-lines/" target="_blank" rel="noopener"><strong>Applying IoT Devices, AI and Machine Learning to Predict Failures on Remote Transmission Lines.</strong></a></p><h3>The problem: inspection schedules vs failure schedules</h3><p>Most transmission lines are inspected every 2 to 5 years, by foot patrol, helicopter, or drone. An inspection tells you what an insulator looked like on inspection day. It says nothing about what the asset is doing right now.</p><p>Degrading insulators announce themselves. Partial discharge, the localized electrical activity that both signals and accelerates insulation breakdown, shows up weeks or months before failure. Surface discharge is the dangerous one: it carbonizes tracks across the insulator surface, and tracked insulators are at significantly higher risk of catastrophic flashover. Often, a tracked insulator is only found after a flashover has already started a fire.</p><figure id="figure-dloe1j5vs7msz38655"><img id="dloe1j5vs7msz38655" src="https://cdn.durable.co/blocks/8Oq0SEJ1qJLJ265EmVCE7QOEVYXV0fEI4zR8QY64dIy26dwfwgG07m0W8KQMEf4f.jpg" alt="" title="" class="object-cover bg-gray-100 " style="aspect-ratio: 1/1; object-position: center center;" data-corners="default" data-aspect="1:1" data-caption="A heavily tracked insulator. The carbonized paths are conductive, and each one shortens the distance to flashover. This is what continuous monitoring finds before it fails." data-positionx="null" data-positiony="null" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/8Oq0SEJ1qJLJ265EmVCE7QOEVYXV0fEI4zR8QY64dIy26dwfwgG07m0W8KQMEf4f.jpg" data-mce-style="aspect-ratio: 1/1; object-position: center center;"><figcaption class="body-normal text-gray-500 mt-2">A heavily tracked insulator. The carbonized paths are conductive, and each one shortens the distance to flashover. This is what continuous monitoring finds before it fails.</figcaption></figure><h3>Why simple monitoring fails: the environment lies</h3><p>Temperature, humidity, and barometric pressure all influence partial discharge in non-linear, sometimes contradictory ways. Published research has recorded winter readings over 100 times higher than summer readings on identical equipment, purely from environmental effects. Lab studies conclude humidity suppresses discharge; on aged, contaminated insulators in the field, humidity is a primary driver of the surface discharge that causes flashover.</p><p>A monitoring system that ignores this produces false alarms in January and false confidence in July. That is why the research pairs every electrical measurement with real-time environmental data, and trains machine learning models on the combination.</p><h3>From lab to live lines</h3><p>The models were built the slow way. Controlled laboratory experimentation with high-fidelity ultrasonic and RF sensors established labeled discharge signatures. Then field deployment tested them against everything a lab cannot simulate.</p><figure id="figure-o9uvvxp3gmsz3aspk"><img id="o9uvvxp3gmsz3aspk" src="https://cdn.durable.co/blocks/6oRmRueEHhz64ECzXgFhg7kbcz2hBzOPX06k8GuNfJX8gA79iNVZXt7U7oawU9Il.jpg" alt="" title="" class="object-cover bg-gray-100 " style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="Laboratory partial discharge experimentation. Controlled testing established the labeled signal library the machine learning models train on." data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/6oRmRueEHhz64ECzXgFhg7kbcz2hBzOPX06k8GuNfJX8gA79iNVZXt7U7oawU9Il.jpg" data-mce-style="aspect-ratio: 16/9; object-position: center center;"><figcaption class="body-normal text-gray-500 mt-2">Laboratory partial discharge experimentation. Controlled testing established the labeled signal library the machine learning models train on.</figcaption></figure><p>In the field, our pole-mounted Cricket device does the listening: solar-powered, weatherproof from -30°C to +60°C, installed on live structures without de-energization. It captures ultrasonic, RF, and environmental data on continuous cycles, processes at the edge, and transmits over LoRa or LTE. A patent-pending sensor fusion approach cross-references the ultrasonic and RF domains: true partial discharge registers in both, which filters out ambient noise that fools single-sensor systems.</p><figure id="figure-2yo3kzxpgvmsz398wt"><img id="2yo3kzxpgvmsz398wt" src="https://cdn.durable.co/blocks/35uScnVFrwKvmrba7IvOj58P1Ijgo9VzmzppXOl2wgXFXThVNHFKw7xcmUOuvPQE.jpg" alt="" title="" class="object-cover bg-gray-100 " style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="Ultrasonic imaging of discharge activity on live pole-top hardware. The asset is producing signal. The question is whether anything is listening." data-positionx="null" data-positiony="null" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/35uScnVFrwKvmrba7IvOj58P1Ijgo9VzmzppXOl2wgXFXThVNHFKw7xcmUOuvPQE.jpg" data-mce-style="aspect-ratio: 16/9; object-position: center center;"><figcaption class="body-normal text-gray-500 mt-2">Ultrasonic imaging of discharge activity on live pole-top hardware. The asset is producing signal. The question is whether anything is listening.</figcaption></figure><h3>What the field data showed</h3><p><strong>The paper closes with a case study from live transmission corridors in Alberta: 100 transmission structures and over 50,000 environmental readings.&nbsp;</strong></p><p>The headline finding: temperature was a significantly stronger predictor of surface discharge than any other environmental factor, and discharge behavior diverged sharply around freezing, where condensation and ice transform how insulators fail. Statistical testing of four environmental hypotheses produced results that contradict common lab-derived assumptions.</p><p>The practical output for a utility is simpler than the physics: a ranked, continuously updated view of which structures are degrading, so the next truck roll goes where the data says it counts.</p><p>Detection tells you a line went down. Prediction tells you which one is about to.</p><h3>Connect with us:&nbsp;</h3><p>If you're interested in learning how CRWN can predict failures in your assets <a data-mce-href="https://crwn.ai/contact" href="https://crwn.ai/contact">please reach out to us.</a></p>

The Research Behind Prediction: Our Work in INMR

CRWN AI

CRWN AI emerged from TerraSense Analytics, where our founding team built successful solutions for complex infrastructure challenges. After three years of intensive development and field testing, we launched CRWN AI as a dedicated platform focused exclusively on transmission grid resilience.

Our approach is different because we've lived the problems we're solving. We understand the pressure of maintaining aging infrastructure, the regulatory scrutiny utilities face, and the critical balance between safety, reliability, and cost-effectiveness.

Today, with wildfires causing billions in damages and threatening entire communities, our mission has never been more urgent. CRWN AI doesn't just prevent outages—we help utilities fulfill their responsibility to protect lives, property, and the environment from catastrophic fires caused by failing grid infrastructure.

Blog

Why Kelowna Is a Hub for AI Innovation in Energy Infrastr...

The Research Behind Prediction: Our Work in INMR