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AI to control solar panels, and enable power sharing? How US Energy Sector startups are leading the AI race

As the global energy landscape evolves to meet the necessities of climate change and burgeoning demand, Artificial Intelligence (AI) and Machine Learning (ML) are rapidly emerging as linchpins for sustainable energy solutions. The nexus between these advanced technologies and energy sustainability is not merely theoretical but manifests in real-world applications, driving tangible benefits. The global AI market in the energy sector is predicted to reach a staggering $19.2 billion by 2028, and the potential of AI to unlock $1.6 trillion in savings by 2030, underpins the seismic shift underway. This transition is not just an indicator of optimized energy management and reduced greenhouse gas emissions but also a fertile ground for startups to innovate and contribute to a more sustainable, efficient, and resilient energy ecosystem.

The Energy Sector in the USA is Booming At a Rapid Pace

The energy sector stands at the cusp of a transformative phase, with AI and ML being the vanguards of this transformation. 

The global AI market in the energy sector is set to ascend at a CAGR of 25.1%, reaching a valuation of $19.2 billion by 2028, a testament to the growing affinity towards AI-driven solutions. The prowess of AI extends to a potential saving of $1.6 trillion for the global energy sector by 2030, embodying the financial prudence of embracing AI.

In terms of efficiency and sustainability:

What AI Can Do for the Energy Sector in the USA?

A few years back, no one knew how AI could be used in the US energy sector. Now, however, we have pretty splendid examples of companies using AI to enhance the customer experience and sustainability further. Let’s go through a few examples.

Customer Experience

AI is still in its infancy and there’s a lot to come. However, thanks to tech partners like Mantra Labs, energy companies in the US are able to leverage modern technology to enhance their customer experience exponentially. GreenBrilliance (a leading solar panel installer in the US) is one such example. The solution developed by GreenBrilliance helps customers know how many solar panels will be required to power a house, how much power does a solar panel produce, and more. Further, it would help them monitor, control, and troubleshoot their solar panels on their smartphones.

Customer experience is one such thing that has started getting attention in the last few years. B2C companies, irrespective of their industry, not only build products or render services but try to provide the best customer experience possible. Reports predict that Solar could fulfill 40-50% of U.S. electricity demand by 2050. Also, the cost of installing and servicing solar panels has also been reduced by 60% over the last decade. This has been possible only through implementing automation, simplifying operations, and bringing transparency to the customer, along with many other things. 

Efficiency and Sustainability

Efficiency and sustainability are the USPs of solar power and AI is helping to boost that further. 

For example, accurate demand forecasting is pivotal for energy efficiency and cost-effectiveness. Startups like GridX are leveraging AI to predict energy demand and optimize power flows, thereby reducing energy costs and enhancing system efficiency.

Companies like Power Ledger are using AI systems to modernize and decentralize grid systems, enabling efficient trade of solar power among neighbors. This fosters a balanced supply and demand, optimized power flows, and improved grid reliability. Moreover, AI-driven innovative energy storage solutions are instrumental in integrating renewable energy into the grid and enhancing energy security.

These diverse applications underscore the boundless potential of AI and ML to revolutionize the energy sector, making it more sustainable, efficient, and resilient.

What Future AI Promises to the US’s Energy Sector?

As AI technology continues to evolve, the horizon of possibilities in the energy sector broadens. Large corporations like Google, Amazon, and Microsoft, alongside agile startups, are exploring the AI energy landscape continuously, indicating a robust and growing ecosystem.

And it’s not only the private sector that is putting effort into harnessing the power of AI. The US Department of Energy (DOE), the National Renewable Energy Laboratory (NREL), and other governmental bodies are also leveraging AI to pioneer new generations of nuclear reactors, develop new solar and wind technologies, and create smart grid systems.

Integration of AI and ML with the energy sector is a narrative of innovation, sustainability, and vast potential. The journey of companies like Green Brilliance Predictum, Heliogen, GridX, Amperio, and Power Ledger illustrates the transformative power of AI and ML in forging a sustainable energy future. As the global AI market in the energy sector burges, the call for startups to innovate and contribute to this burgeoning ecosystem is loud and clear. The narrative of AI and ML in the energy sector is still being written, and startups have a golden opportunity to be the authors of many success stories in this narrative.

Further Readings: Bringing Solar Renewable Energy Closer to Consumers in the USA

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10 Analytics Tools to Guide Data-Driven Design

Analytics are essential for informing website redesigns since they offer insightful data on user behavior, website performance, and areas that may be improved. Here is a list of frequently used analytics tools to guide data-driven design that can be applied at different stages of the website redesign process. 

Analytics Tools to Guide Data-Driven Design

1. Google Analytics:

Use case scenario: Website Audit, Research, Analysis, and Technical Assessment
Usage: Find popular sites, entry/exit points, and metrics related to user engagement by analyzing traffic sources, user demographics, and behavior flow. Recognize regions of friction or pain points by understanding user journeys. Evaluate the performance of your website, taking note of conversion rates, bounce rates, and page load times.

2. Hotjar:

Use case scenario: Research, Analysis, Heat Maps, User Experience Evaluation
Usage: Use session recordings, user surveys, and heatmaps to learn more about how people interact with the website. Determine the high and low engagement regions and any usability problems, including unclear navigation or form abandonment. Utilizing behavior analysis and feedback, ascertain the intentions and preferences of users.

3. Crazy Egg:
Use case scenario: Website Audit, Research, Analysis
Usage: Like Hotjar, with Crazy Egg, you can create heatmaps, scrollmaps, and clickmaps to show how users interact with the various website elements. Determine trends, patterns, and areas of interest in user behaviour. To evaluate various design aspects and gauge their effect on user engagement and conversions, utilize A/B testing functionalities.

4. SEMrush:

Use case scenario: Research, Analysis, SEO Optimization
Usage: Conduct keyword research to identify relevant search terms and phrases related to the website’s content and industry. Analyze competitor websites to understand their SEO strategies and identify opportunities for improvement. Monitor website rankings, backlinks, and organic traffic to track the effectiveness of SEO efforts.

5. Similarweb:
Use case
scenario: Research, Website Traffic, and Demography, Competitor Analysis
Usage: By offering insights into the traffic sources, audience demographics, and engagement metrics of competitors, Similarweb facilitates website redesigns. It influences marketing tactics, SEO optimization, content development, and decision-making processes by pointing out areas for growth and providing guidance. During the research and analysis stage, use Similarweb data to benchmark against competitors and guide design decisions.

6. Moz:
Use case scenario: Research, Analysis, SEO Optimization
Usage: Conduct website audits in order to find technical SEO problems like missing meta tags, duplicate content, and broken links. Keep an eye on a website’s indexability and crawlability to make sure search engines can access and comprehend its material. To find and reject backlinks that are spammy or of poor quality, use link analysis tools.

7. Ahrefs:
Use case scenario:
Research, Analysis, SEO Optimization

Usage: Examine the backlink profiles of your rivals to find any gaps in your own backlink portfolio and possible prospects for link-building. Examine the performance of your content to find the most popular pages and subjects that appeal to your target market. Track social media activity and brand mentions to gain insight into your online reputation and presence.

8. Google Search Console:

Use case scenario: Technical Assessment, SEO Optimization
Usage: Monitor website indexing status, crawl errors, and security issues reported by Google. Submit XML sitemaps and individual URLs for indexing. Identify and fix mobile usability issues, structured data errors, and manual actions that may affect search engine visibility.

9. Adobe Analytics:
Use case scenario:
Website Audit, Research, Analysis,
Usage: Track user interactions across multiple channels and touchpoints, including websites, mobile apps, and offline interactions. Segment users based on demographics, behavior, and lifecycle stage to personalize marketing efforts and improve user experience. Utilize advanced analytics features such as path analysis, cohort analysis, and predictive analytics to uncover actionable insights.

10. Google Trends:

Use case scenario: Content Strategy, Keyword Research, User Intent Analysis
Usage: For competitor analysis, user intent analysis, and keyword research, Google Trends is used in website redesigns. It helps in content strategy, seasonal planning, SEO optimization, and strategic decision-making. It directs the production of user-centric content, increasing traffic and engagement, by spotting trends and insights.

About the Author:

Vijendra is currently working as a Sr. UX Designer at Mantra Labs. He is passionate about UXR and Product Design.

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