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Machine learning: Future of AI

Introduction Machine learning: Future of AI

Machine learning is a fascinating field of computer science that enables computers to learn and improve without being explicitly programmed. 

It’s essentially about teaching computers to learn from data, just like humans do. Imagine a computer that can analyze mountains of data and identify patterns, make predictions, and even take decisions on its own – that’s the power of machine learning!

Here’s how it works in a nutshell:

  1. Data Collection: The first step involves feeding the machine learning algorithm with a massive amount of data. This data can be anything from text and images to audio and sensor readings.
  2. Model Training: The algorithm then analyzes the data to identify patterns and relationships. It’s like a student studying a textbook – the more data it consumes, the better it learns.
  3. Predictions and Decisions: Once trained, the model can be used to make predictions or decisions on new, unseen data. For example, a machine learning model trained on millions of customer reviews can predict whether a new user will like a particular product.

There are different types of machine learning algorithms, each suited for specific tasks:

  • Supervised learning: In this type, the algorithm is trained on labeled data, where each data point has a corresponding label (e.g., a picture of a cat labeled as “cat”). The model learns to map the input data to the desired output (e.g., identifying a new picture as a cat).
  • Unsupervised learning: Here, the data is unlabeled, and the algorithm needs to find patterns and relationships on its own. This can be used for tasks like clustering data points into groups based on similarities.
  • Reinforcement learning: This type involves the algorithm interacting with an environment and learning through trial and error. The algorithm receives rewards for making good decisions and penalties for bad ones, gradually improving its performance.

Machine learning has revolutionized various industries, from healthcare and finance to manufacturing and entertainment. 

Here are some real-world examples:

  • Recommender systems: These systems recommend products, movies, or music to users based on their past preferences and behavior.
  • Fraud detection: Machine learning algorithms can analyze financial transactions to identify fraudulent activities in real-time.
  • Medical diagnosis: Machine learning can help doctors analyze medical images and data to diagnose diseases more accurately.
  • Self-driving cars: These cars use machine learning algorithms to navigate roads, recognize objects, and make decisions in real-time.

As machine learning continues to evolve, it promises to further transform our lives in ways we can only imagine. So, the next time you see a computer doing something amazing, remember – it might just be the power of machine learning at work!

Machine learning: Future of AI

Machine learning: Supervised learning

Supervised learning is the powerhouse of many impressive real-world applications in AI! Let’s delve deeper into its workings:

Think of it like learning with a tutor:

  • Dataset: Your tutor provides you with examples, like pictures of animals labeled as their corresponding species. This labeled data is your training set.
  • Model Training: You analyze the examples, identifying features and patterns that distinguish, say, a cat from a dog. This is where the machine learning algorithm does its magic, forming connections between input data and desired outputs.
  • Predictions and Decisions: Once trained, you can take a new picture and apply what you learned. The model can now predict the animal in the picture with a high degree of accuracy.

What are the common tasks in supervised learning?

  • Classification: Categorizing data points into predefined classes, like spam/not spam emails or handwritten digits.
  • Regression: Predicting continuous values based on input data, like stock prices or weather forecasts.

What are the strengths and weaknesses of supervised learning?

Strengths:

  • Highly accurate for well-defined problems: With high-quality labeled data, models can become incredibly accurate, even surpassing human performance in some tasks.
  • Versatile: Applicable to a wide range of problems, from image recognition to sentiment analysis.
  • Interpretable models: Some algorithms allow understanding how the model arrives at its predictions, providing valuable insights.

Weaknesses:

  • Reliance on labeled data: Requires substantial, accurately labeled data, which can be expensive and time-consuming to collect.
  • Overfitting: Can memorize the training data too closely, leading to poor performance on unseen data.
  • Limited domain applicability: Models trained on specific data might not generalize well to other scenarios.

Examples of supervised learning in action:

  • Email spam filters: Analyze email content to classify them as spam or not spam.
  • Medical diagnosis: Analyze medical images and data to help doctors diagnose diseases.
  • Self-driving cars: Predict the behavior of other vehicles and pedestrians to make safe driving decisions.

Machine learning: Future of AI

Machine learning: Unsupervised learning

Ah, unsupervised learning! Where the data throws a wild party and the algorithm figures out the dance moves on its own! It’s a fascinating world where patterns emerge from the shadows, unlike supervised learning with its neatly labeled examples.

Here’s how it works:

Imagine being dropped into a bustling marketplace:

  • Unlabeled Data: All you hear are whispers, shouts, and murmurs – no clear labels or instructions. This is your unlabeled data, full of raw information but lacking predefined categories.
  • Pattern Discovery: You start noticing groups of people with similar accents, gestures, or clothing. Unsupervised learning algorithms do the same, finding hidden patterns and structures within the data.
  • Clustering and Dimensionality Reduction: You might group vendors selling fruits together, while musicians gather in another corner. Algorithms like clustering do this automatically, grouping data points based on similarities. Dimensionality reduction helps visualize complex data in lower dimensions for easier analysis.

What are the common tasks in unsupervised learning?

  • Clustering: Identifying groups of similar data points, like customer segments or gene clusters.
  • Dimensionality Reduction: Simplifying complex data for visualization and analysis.
  • Anomaly Detection: Finding unusual data points that deviate from the norm, like fraudulent transactions or network intrusions.

What are the strengths and weaknesses of unsupervised learning?

Strengths:

  • No need for labeled data: Works with raw, unlabeled data, saving time and resources on labeling.
  • Uncovers hidden insights: Helps discover unexpected patterns and relationships that might be missed in supervised learning.
  • Flexibility: Can be applied to various tasks without predefining specific outcomes.

Weaknesses:

  • Interpretation challenges: Understanding the meaning of discovered patterns can be difficult.
  • Subjectivity: Different algorithms can find different patterns in the same data, requiring careful evaluation.
  • No guarantees: There’s no guarantee that the discovered patterns are meaningful or useful.

Examples of unsupervised learning in action:

  • Market segmentation: Grouping customers based on their shopping habits for targeted marketing.
  • Fraud detection: Identifying unusual financial transactions that might indicate fraud.
  • Recommendation systems: Suggesting products or content based on users’ past interactions and similar user profiles.

Machine learning: Future of AI

Machine learning: Reinforcement learning

Ah, reinforcement learning! Where trial and error meets algorithmic magic, paving the path for learning through exploration and reward. Buckle up, because we’re about to dive into the fascinating world of agents navigating dynamic environments in pursuit of that sweet, sweet reward!

Imagine you’re a robot learning to walk. You stumble, you fall, you get back up – just like a reinforcement learning algorithm navigating an unknown environment. 

Every action you take triggers a response from the environment, either a rewarding pat on the back (think reaching your goal) or a gentle nudge in the right direction (negative feedback for an inappropriate action).

Here’s how it works:

  • Agent and Environment: Our robot is the agent, interacting with the environment (the room, the obstacles, etc.).
  • Actions and Rewards: The robot takes actions (stepping forward, turning left) and receives rewards (reaching the target) or penalties (bumping into a wall).
  • Trial and Error: Through repeated attempts, the algorithm learns which actions lead to greater rewards and avoids those that bring penalties.
  • Policy Improvement: With each iteration, the algorithm refines its decision-making policy, gradually optimizing its behavior to maximize long-term rewards.

What are the cool things about reinforcement learning?

  • Adaptability: It’s perfect for dynamic environments where rules are unclear or constantly changing.
  • Generalization: Once trained, the agent can adapt its skills to similar environments without needing specific instructions.
  • Scalability: Can handle complex tasks with numerous actions and state possibilities.

But it’s not all sunshine and rainbows:

  • Data Scarcity: Can be data-hungry, requiring careful design and efficient exploration strategies.
  • Interpretation Challenges: Understanding why the agent makes certain decisions can be tricky.
  • Convergence Issues: Sometimes, algorithms get stuck in suboptimal solutions, requiring careful parameter tuning.

Where can we see reinforcement learning in action?

  • Robot control: Teaching robots to walk, run, and manipulate objects.
  • Game playing: From Atari classics to complex Go matches, RL agents are conquering the gaming world.
  • Resource management: Optimizing logistics, scheduling, and resource allocation in complex systems.

Next Article: AH-Artificial Human

https://www.exaputra.com/2024/01/machine-learning-future-of-ai.html

Renewable Energy

Profound Nihilism?

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Normally, “nihilism” means the belief that life is without objective meaning, purpose, or intrinsic value.  Trump was elected by mean-spirited idiots, but they could hardly be called “nihilists.” For example, they believe very strongly in white supremacy, the dismantling of the federal government, saving people from the lethality of vaccinations, etc.

Now, there is a secondary meaning to the word, i.e., those who reject established social systems.  In this sense, I suppose they are indeed nihilists, in that they reject lawfulness, honesty, human rights, truth, science, tolerance, and compassion.

Profound Nihilism?

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Renewable Energy

Vestas Shares Jump 20%, UK Blocks Ming Yang Factory

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Weather Guard Lightning Tech

Vestas Shares Jump 20%, UK Blocks Ming Yang Factory

Vestas doubles second quarter profit and adds €4.7 billion in market value overnight. Plus EnBW finishes He Dreiht after a V236 blade break, the UK blocks Ming Yang’s Scottish factory, and India rules turbines are movable goods.

The Uptime Wind Energy Podcast is brought to you by Weather Guard Lightning Tech, creators of the StrikeTape Ultra LPS retrofit. Subscribe to Uptime’s Substack newsletter. And check out Rosemary’s “Engineering with Rosie” Youtube channel. Have a question we can answer on the show? Email us!

The Uptime Wind Energy podcast, brought to you by StrikeTape. Protecting thousands of wind turbines from lightning damage worldwide. Visit StrikeTape.com. And now, your hosts

Allen Hall: Welcome to the Uptime Wind Energy podcast. I’m your host, Allen Hall, and I’m here with Rosemary Barnes, Matthew Stead, and Yolanda Padron. And three out of the four of us will be in Melbourne Australia talking to a number of operators and interested parties about WOMA 2027. Matthew, where will we be the couple of days we’re in Melbourne?

Matthew Stead: So, um, first of all, we’ve got the Pullman, uh, East Melbourne, which is, uh, where the venue will be for, for 2027. Um, so that’ll be our home base. Um, we’ve got around about eight meetings planned already. So what we’re doing is we’re talking to the operators and a few other industry, um, players about [00:01:00] what we need to talk about, how we’re gonna move the industry forward in Australia.

Uh, so it’s gonna be jam-packed, but there’s a little bit of time left on the Friday afternoon if there’s any late-minute, um, people that wanna get in contact and catch up with us, um, for next Thursday, Friday, or actually Friday. Uh, so yeah, it’s gonna be a, a jam-packed time. I think we’re gonna be tired, too many coffees, and talking to all the key, all the key operators, uh, about what they wanna hear about and how we can move the, the industry forward.

Allen Hall: And if someone wants to put an input into the WOMA panel about what will be discussed at WOMA 2027, Matthew, how would they do that? How do they get ahold of you?

Matthew Stead: Well, we have a wonderful website, and that’s got all the details you could ever want. Um, you can also register on the website, so please register.

Otherwise, um, I’m sure we’re gonna be a sellout this year for sure. So woma2027.com.

Rosemary Barnes: I just wanna add that when people talk to [00:02:00] me about the event, they always say how they love that the topics are so relevant, and the reason why that they’re so relevant is because we make sure to go around to operators and find out what are the issues that they’re really dealing with.

So anybody that’s thinking of attending, even if you can’t, you know, meet us up, meet up with us in Melbourne, get in touch and tell us what are the, yeah, what are the topics that you’re struggling with that you’re not, um, you’re having trouble finding enough information, having trouble finding the people that can help you.

And y- yeah, like we take all of that information, and that’s how we come up with our agenda each year. And yeah, I mean, for us, that’s the, the main thing is that this has to be really relevant, up-to-date information for the industry, and we need your help to make sure it stays that way. I

Matthew Stead: mean, that’s what we’ve done the last two years, so this is– we’re just repeating the formula, um, listening to the operators and getting the good topics and the good speakers.

Allen Hall: Well, Vestas has had a good quarter. Uh, the, for the last couple of years, honestly, s- [00:03:00] Vestas has been really thin on margins. There was questions about it continuing on. Rising costs mostly, uh, supply chains, especially during COVID, were bad. Uh, and, uh, but for the most part, the shareholders stayed attached.

Well, that story is changing rapidly. The world’s largest turbine maker posted second quarter operating profits of $400- €46 million, more than double what the analysts had expected, and it’s raised its full-year margin guidance alongside half-year results for the first time in a decade. The shares climbed about 20% in Copenhagen, adding roughly €4.7 billion of market value in a single session.

Now, the chief executive, uh, Henrik Andersen, ha- put it plainly to, uh, uh, in a couple of news sources that something much bigger is happening and Vestas is gonna be the, the leader in wind. That’s how I read it, that everybody [00:04:00]at Vestas was super happy with the, the change in direction and things were moving up steadily.

But a 20% jump in a day is remarkable. You don’t see that in large industrial businesses like wind energy. Matthew, this has real implications on what happens next for Vestas because success like this usually means more orders.

Matthew Stead: Yeah, I wonder what’s going on under the hood there. Um, I mean, Vestas is a quality company, although, although can I just do a quick segue?

How many turbines were installed in Denmark in the last, uh, two years? Like last year and the year before?

Allen Hall: I don’t know. How many?

Matthew Stead: I believe it was eight turbines installed onshore in Denmark last year, and the year before it was 12. So, you know, maybe, maybe Vestas needs to focus on their own backyard a little bit as well.

Allen Hall: I’m not sure there’s a lot of opportunity there. Yeah, onshore.

Matthew Stead: How can you ever be full? I mean, there’s always, um, [00:05:00] uh, you know, um, you know, resiting or, um, you know, upgrades and-

Rosemary Barnes: You know what? Allen and I are probably gonna get some time in Jutland, uh, later this year, um, and that area and the old wind turbines there was actually the inspiration for my whole YouTube channel.

It just, ’cause there’s, you know, there’s turbines there from, the earliest one is, um, from the ’70s and still going. I think it’s one and a half megawatts, actually huge for, for that time. Um, and it was like community made, um, at Tvind. But anyway, I’m interested to revisit the site and have a look and see are these, you know, all these old turbines still there.

It’s only, like six years since I went through and did the experience but for the most part, they don’t seem to be yet pulling down the, the small old ones and putting up big ones. There’s a lot of, a lot of them are community owned. Um, and yeah, I mean, Danish people love wind turbines, but there’s only so many that you can have onshore.

Like, people are happy to live near them by, you know, the standards of people in other countries, but you don’t want [00:06:00] one in your literal backyard. I think that there is, there, there is a, a limit to how many more onshore wind turbines that you can get in that area and offshore expansion is the more likely way to go.

Um, and also I think it’s, it’s, it’s good to recognize that if you have a domestic only or a domestic first strategy, that will only get you so far and then you have to expand, and I think Denmark did that really well. I think Germany a little bit less. I think that Enercon were a bit surprised, um, by their strategy.

It, uh, they had a real hard time anyway when they had to transition away from mostly Germany to getting overseas. And obviously, like if you look at China, they have most of their installations are in China. They are trying so hard to get outside of China because it’s not, like even a market as big as China, it’s got decades to go before it will be full.

Um, you can still recognize that that’s not your, like long-term strategy for growth has to involve expansion, I think.

Allen Hall: I think Vestas, regardless of what happens in Denmark, is making a play for the United States. That seems to be [00:07:00] where a significant effort is happening at the moment and on offshore. Their– Vestas seems very excited about the offshore opportunities.

Of course, there’s a ton of wind turbines gonna be installed in the UK and, and all around Northern Europe. Offshore, the opportunities to buy turbines, there’s only a couple that you could get today. Uh, uh, the GE Vernova offerings I, I don’t think are gonna fit the mold, and I don’t know if GE’s even actively selling.

So their competitor realistically is Siemens Gamesa, which does seem like the smaller player at the minute versus Vestas, which is heavily pushing the V236 and will fill order books like crazy, I think, uh, just based upon the, the history they’ve had and everybody knowing who they are. So Also on the move in Australia, right?

Vestas is huge in Australia right now.

Rosemary Barnes: I think it’s really good that their, um, yeah, finances, uh, are [00:08:00] looking a bit better ’cause it’s been funny. Like, I tried early on in my wind career to invest in, you know, wind turbine manufacturers knowing that there would be immense growth, and I was right. There, there was immense growth.

Not that that was so hard to figure out that there would be, but it did not lead to any kind of, um, return on, on anything, you know. Like, that did not keep pace with the just general market. Um, so I, I stopped trying to, stopped trying to invest to that. But it has been really, really hard for the companies to, you know, raise money or y- you know, do any of the things that they need to do because they’ve always, like, they’re growing, growing, growing, but finances has been so tight that it has been a real constraint on the amount of engineering that they could do, and I really hope that Vestas are gonna take this opportunity that they’ve got compared to, you know, a lot of the other manufacturers.

Vestas do have really strong, um, innovation and, yeah, engineering capabilities for doing– you know, developing new technologies and improving them, and I really hope that they’re taking this opportunity to build that up. There are a lot [00:09:00] of very good engineers with a lot of experience in the industry in that area that are working in other fields at the moment because, you know, there’s been a lot of contraction in Denmark.

So I don’t know, it seems like a really good time to hire back some of that really in-depth knowledge and, yeah, get a- get ahead of, you know, some of the future quality problems. We’re going through such a hard time at the moment from the fast development that happened in the 20-teens when there wasn’t a whole lot of money around.

We’re dealing with quality problems now, so, you know, maybe we can get ahead and not have the next round of them if we can invest in just a lot more, uh, engineering capacity.

Allen Hall: When you have success like Vestas has, usually the upper level management and some of the executive team starts getting pilfered, that they’ll get offers to repeat that success at another company, and it sounds like that process has started already.

There’s a couple of executives that have recently departing or are in the midst of departing from Vestas. [00:10:00] I would see that continuing f- at least for the next six months, uh, because everybody wants to repeat that, right? If you can get a 20% increase in your valuation overnight, uh, I can, I can list a number of companies, regardless of industry, that would love to participate.

Even in a 5% increase, that would be remarkable. So, um, Vestas is gonna have a hard time holding onto this. That’s just the nature of the business where things are successful, people will wander. And Rosemary, I, I think they’re– And Yolanda In, in my book, Vestas should sort of s-stand down and just make quality products.

I’m not sure you sh-should tinker too much at the time being and just make the good stuff better. That seems like a way to really increase profits.

Yolanda Padron: Yeah, I mean, solving a lot of the issues that– And, and that’s not just a Vestas exclusive thing, right? All of these OEMs have some sort of issue that maybe– I know Rosie’s touched a lot on, on it, where [00:11:00] you build this version A and then version B solves one of the small little issues, but now it creates another little problem, and then you have version C, and then everything just kinda has its own niche little issue, um, that really expands over time.

So if they could solidify what they already have in, in a, in a model that, that would help them just even keep a lot of their customers, I think that’d be great, and it would help, certainly help them, um, not continuously, like, rotate around the customers, ’cause it almost feels like, at least in the States, right, you, you get GE to be really, really strong and have a huge market share, and then GE starts focusing more on gas turbines, so then they all go onto Vestas, and then they all go onto Ontara now.

Um, and then just, you know, just kind of everybody starts cycling through them because they just kind of want something that’s better quality than what they’re getting in the long haul.

Matthew Stead: Allen, you, you talked about you think there’s something big under the hood. I think you, you [00:12:00] thought that maybe Vestas was angling towards something or being quite bullish.

Do you think that they might take over GE Vernova?

Allen Hall: I don’t think they’re gonna grab Vernova, and I don’t think Vernova is for sale at the minute, but I wonder if Siemens Gamesa is, or Nordex. I mean, Nordex has done terrific the last couple of quarters and is making inroads in places that I didn’t think possible three, four years ago.

Uh, the European marketplace is be- becoming really unique in that sense that there’s a lot of money being put out. But is there a sole perfect solution for Europe? Not at the minute, ’cause you got two competitors there, and then China trying to, to work its way in. Will the Europeans come together and form something more united, even if it’s just a partnership, a loose partnership, versus letting China on the shores?

We’ll see. 64 of the largest machines that Vestas has builds are standing off the German coast, but one blade is missing a [00:13:00] piece. We’ll talk about that when we come back.

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Allen Hall: Well, Germany’s largest offshore wind farm is now fully installed, and EnBW confirmed this, uh, past week that all 64 of the Vestas V236 15-megawatt turbines are s- standing at the He Dreiht wind farm about 85 kilometers northwest of Borkum. Uh, 960 megawatts, [00:14:00] 2.4 billion euros invested. Man, these offshore projects are expensive to get installed.

Uh, so it’s power for roughly 1.1 million households, and there’s no state subsidy behind any of it. And so this is a little bit of a u- unique situation. Uh, th- well, the one footnote about the wind farm is they had a V236 blade break and fall into the North Sea, and they had fished it out and I think I passed along s- pictures that I saw online of, uh, one of the police boats pulling the shear web out of the water I don’t know what to think anymore about some of these offshore blade issues.

Obviously, Vestas is very conscientious about it and will be doing RCAs and engineering reviews and all the above to go identify what the problem is. But it does just lead to a little bit of a pause of do– what is going on for some of these offshore [00:15:00] wind blade installations or, or whatever’s causing these blades to break?

Do we have a good handle on it? Yolanda, is– are we following up on all the design details so that we can prevent these things in the future?

Yolanda Padron: I mean, I’d, I’d hope you’d be following up on the design, right? Like, and, um, but I think there is still a little bit of a disconnect from, from what we’ve seen, and again, not just Vestas exclusive, um, between the people who are designing and the people who are manufacturing, the people who are in operations, right?

So, uh- The, from what we’ve heard, uh, this could have potentially been a, um, partially because of a transportation issue, which is what happens a lot in onshore. It’s a lot more common than we would like it to be. Um, and so that even goes beyond what would go on in the design studio and what would go on in the manufacturing and what would [00:16:00] go on even just for the people that are running the site, right?

So, so some sort of, um, in between, uh, EPC error. Um, but yeah, I just think that, like in a lot of industries, there should be a lot more communication between all of these teams on the lower level, so that way a lot of these problems can, can be avoided.

Allen Hall: I’m wondering if it’s actually an issue on the, the testing side.

And, uh, the one question that just popped up, and we saw from the ORE Catapult, uh, survey that’s being conducted at the moment, and if you haven’t participated in that, you just visit ORE Catapult and answer some of the survey questions. But torsion on a blade, which is very difficult to test for, and it really isn’t tested for today, but does happen during the move and the transportation of these big offshore blades.

Is it one area that we need to do a little more work in or maybe spend some more time focusing on it to see what is happening as blades are [00:17:00]moved?

Rosemary Barnes: The thing about te- torsion is that it is much more significant as blades get longer. I can’t, I can’t remember the equation off the top of my head, which is, um, bothering me.

But I think it scales with, like, the fourth power or something of, of length. And so whilst it was always a bit of a problem, it’s much more of a problem as it gets, as blades get bigger. I mean, they’ve never, like, fully tested a blade, and there was always a lot of reliance on, hey, y- you know, like we’ve tested certain things that is possible to test in a test facility on the ground.

But they also rely on their decades of experience of how blades actually behave in the field. But, you know, remember, that’s a real lagging, lagging indicator because y- you know, their decades of experience is mostly with lots smaller blades. Now, blades are really different because they’re longer and different effects are, are taking over.

It’s not just, uh, torsion, but it’s also the laminates get much thicker, and then y- you know, you, you have issues with the way that they’re curing, [00:18:00] and there’s a lot more just space for, um, defects to be present in a really thick laminate All of those things add up. Oh, yeah, then add in addition, like new materials, carbon fiber is new, and then new ways of producing it, you know, pultrusions, um, all kinds of different materials like balsa’s being replaced with foams and, um, like, you know, 10 times that number of what sounds like a small innovation, but all of these things have the potential for damage and don’t have a really long track record in the field to be able to kind of calibrate.

We do need to remember that, like, when you do something new, things are gonna break, uh, sometimes, they’re gonna fail sometimes. If they don’t, then you’re definitely being too conservative, and your product is costing more than it should, and nobody wants more expensive wind energy, right?

Matthew Stead: Rosie, Rosie, I, I know you’re doing some, some excellent work on, um, industry studies around erosion and temperature and so forth.

Um, I just wanted to let a little secret out of the bag that, um, in the future there will also be some [00:19:00] other studies on torsion and blade twist and blade dynamics. So, um, just a few things are in, in train at the moment, which I can’t share, share, but, uh, watch this space around better understanding blade twist.

Allen Hall: The Hydride wind farm runs on European turbines, but the next one might not. Two governments with two very different answers on who gets to build Europe’s wind fleet.

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Well, two countries and two decisions, one question. In Scotland, the UK government blocked plans for the Chinese manufacturer Mingyang to build a turbine factory, uh, near Inverness on national security grounds. 1.5 billion pounds of investment, up to about 1,500 jobs. And First Minister John Swinney has asked the new prime minister to reconsider.

And the UK energy secretary minister called that request irresponsible. Meanwhile, up in Denmark, Vattenfall has just won two offshore wind farms and will not say whether it will buy European turbines. Danish suppliers are not taking that quietly. So [00:21:00] the Scotland question about the Mingyang factory is at least being discussed again with the new prime minister in the UK.

It does seem like there’s a lot to do and get the government formed and make all this stuff happen. But I don’t see a Burnham administration changing the outcome for Mingyang, but I could be wrong. At the, the same time, Vestas is pushing for a more Eurocentric focus and to really keep out the Chinese.

Uh, something has to give here pretty soon.

Matthew Stead: I actually think Mingyang should, um, set up a factory in Scotland. I, I mean, what’s wrong with that? I mean, uh, why is that a security issue?

Rosemary Barnes: Set up the factory and put the, like, whatever you’re worried about, put protections in place for it, require it to be a local joint venture or whatever.

You know, we’ve seen the blueprint in many of what used to be, you know, less rich countries. That’s how they, you know, got a head start on some of these technologies. It’s not like, I don’t think that China [00:22:00] has a head start on wind, wind turbine technology, but they certainly have different ways of doing things that, um, yeah, we could, we could learn from.

But I think across the board, wind turbines, batteries, solar panels, whatever, let them set up factories, put the rules in place that mean that your country benefits from it and you’re getting the, you know, the information transfer.

Yolanda Padron: Do you think that’ll, like, impulse a lot of these more established European companies to maybe start fixing some of the issues that they’ve known about for, for a while, um, particularly regarding the blades and everything that we’ve talked about earlier?

Like, there’s enough competition there, so maybe they need to start looking a little bit more deeply into their problems.

Allen Hall: Do we think that Chinese operations have been out front, forward, honest, I’ll even use, about their blade issues?

Rosemary Barnes: No, but this is a good way to find out, isn’t it?

Allen Hall: Governments decide who is allowed to build a turbine after a discussion on Scotland.

Uh, but, but [00:23:00] occasionally, a court decides what a turbine legally is. India has just settled that question, and the reasoning should be of interest to anybody who ships machines across a border right after this. As wind energy professionals, staying informed is crucial and let’s face it, difficult. That’s why the Uptime Podcast recommends PES Wind Magazine.

PES Wind offers a diverse range of in-depth articles and expert insights that dive into the most pressing issues facing our energy future. Whether you’re an industry veteran or new to wind, PES Wind has the high-quality content you need. Don’t miss out. Visit peswind.com today. A tax fight in India has produced a definition every turbine supplier should read.

Is a wind turbine bolted to a concrete foundation movable goods, or is it immovable property? State tax authorities argued immovable, which would have [00:24:00] taxed erection and commissioning contracts at 18% instead of 5%. The Andhra Pradesh, uh, High Court disagreed, and on the 12th of August, the Supreme Court declined to interfere.

The reasoning rests on something this whole industry takes for granted. A turbine can be taken down, moved, and put back up. So a turbine is a movable object, and it has less taxation. Bonus. So this is a really interesting discussion that’s happening in India because it’s probably symptomatic of things we’re seeing elsewhere across the world about taxation for wind turbines, right?

That, um, if there’s a way to tax a wind turbine, we’re gonna try to do it. This is a unique way, uh, that happens in India where depending on if it’s permanent or movable, the tax rates are different. I, I guess that would apply to a lot of components inside a wind turbine too, Matthew, don’t you? Like the, the generator, the, the big heavy things, [00:25:00] gearbox, generator, blades, rotors, tower sections, would be taxed at a, a lesser rate.

Matthew Stead: I agree with the court case that it’s all movable and, uh, you can actually buy turbines on the secondhand market, can’t you? I mean, if I wanted to buy, yeah, whatever, whatever, I could buy one and, and put it up in my backyard if I had a bigger backyard. Um, so yeah, I vote for movable. I vote for lower taxes.

Yolanda Padron: The way that it would work a lot of times in the US is, I mean, it’s, you pay, the company itself pays a lot less than they would’ve over time, right? Just by pure, the, the regular kind of tax laws. Um, but the community, there’d be just direct donations to the community, so then they’d get, uh, like money would actually come into the community where the turbines were being built instead of just distributed around the state, which I mean, in a state as big as Texas, it gets, um, but easier for that c- um, that county to get a lot more, uh, funding than they would typically get if it was [00:26:00] through a big enough area.

Um, but yeah, no, I agr- I completely agree with you guys that, that this should be a movable good. I mean, how many times have we seen, uh, even just a blade, um, that it looks like it’s, uh, just a, a failed blade that they have to go in and replace, and then they take it out, fix it, and then just bring it back to the same site or take it to another site across the country.

And, and to that point, like if you were to h- judge it as something that’s immovable, would then any blade replacement just not be taxed? Because then it’s, you’re moving that one component and two, but it’s essentially the same turbine. Like, I don’t know how that all would make sense.

Allen Hall: I think the Uptime Supreme Court agrees with the Indian Supreme Court that wind turbines are movable, and that’s good.

Well, that wraps up another episode of the Uptime Wind Energy podcast. If today’s discussion sparked any questions or ideas, we’d love to hear from you. [00:27:00] Reach out to us on LinkedIn. And if you found value in today’s conversation, please leave us a review. It really helps other wind energy professionals discover the show.

And don’t forget to subscribe so you never miss an episode. For Rosa, Yolanda, and Matthew, I’m Allen Hall. We’ll see you here next week on the Uptime Wind Energy podcast.

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Vermont and Florida: A Key Difference

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Can’t swear that the story here is authentic, but it sure rings true.

Vermont is a somewhat quirky state, but it protects its citizens very well. FWIW, this is where I want MY tax dollars going too.

Florida is a deeply red state that, true to form, wants as much ignorance as it can possibly produce. Educated people aren’t voting for people like Ron Desantis.

Vermont and Florida: A Key Difference

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