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Seven things you need to know about the future of energy

The energy industry of old just doesn’t cut it anymore, and time is running out to switch to cleaner and smarter ways of providing power before it’s too late. As the Intergovernmental Panel on Climate Change warned in its report released in early October, based on current emissions levels the world will reach 1.5 degrees Celsius degrees of warming, compared to pre-industrial, temperatures, by 2030.

So what can be done? At WIRED Smarter some of the smartest minds in the energy world came together to explore what the future of the energy industry might look like. Here’s the best of what we learned from a packed speaker line-up that included Bulb CEO Hayden Wood, Verv COO Maria McKavanagh and DeepMind’s Sims Witherspoon.

1. Little pushes can add up to big changes in customer behaviour

“What we find is that by having a relationship with customers we can change their behaviour,” says Hayden Wood, CEO of clean energy supplier Bulb. His customers who received annual energy reports ended up reducing their energy usage by two per cent, compared to those who didn’t receive the reports.

That might not sound like much, but it is estimated that if everyone in the UK made the same change that’d save customers £560 million a year, and stop 36m tonnes of CO2 being released into the atmosphere, Wood says.

2. Cutting down CO2 emissions isn’t enough – we have to remove them too

“We will need to reduce CO2 in the atmosphere,” if we’re to stop the most extreme impacts of climate change says Jan Wurzbacher, founder and director of Climeworks, a start-up that builds infrastructure to capture carbon from the atmosphere and lock it deep underground.

Last year in Zurich, Climeworks plants removed 900 tonnes of CO2 that was used to supply greenhouses. But his goal is to eliminate eight billion tonnes from the atmosphere, and to do that he needs policymakers to wake up to the potential benefits of carbon capture. “Changes in policy are much easier if we show there are solutions and we’re not 100 years away,” Wurzbacher says.

3. AI isn’t a fix-all cure, but it is a powerful tool for energy efficiency

“Artificial intelligence is not magic sparkle dust,” says Sims Witherspoon, applied artificial intelligence program manager at DeepMind. But if you’re smart about how you use it, AI could have a huge impact on how we heat and cool our buildings.

“AI can show us creativity but it also has the ability to show us new knowledge,” she says. By using AI to analyse energy use in Google’s data centres, the firm was able to save 30 per cent on energy by switching to an AI system that optimised the cooling system in real-time. And there’s potentially no limit to the gains that these kinds of systems can squeeze out, Witherspoon says. “Rules and heuristics don’t get better – AI does.”

4. Smarter energy forecasts could cut down on the amount of energy used

“Today’s energy system is broken, it has failed to innovate at the same rate as other industries and as a result it is in a race to zero profits,” says Maria McKavanagh, COO at Verv, a company that builds intelligent home hubs that track a home’s energy usage. But by accurately predicting the amount of energy a home will use, she says it could allow people to trade energy with their neighbours.

“We can forecast more accurately than anyone else what the energy consumption of that house is going to be in the next five minutes, hours, or even few months,” McKavanagh says. Verv’s monitoring system samples energy usage millions of times every second and then uses that data to predict future energy usage.

And if energy providers got their hands on this data, they could use it to target energy supply to the right areas at the right times, cutting down on wasted energy product. “If the national grid could forecast with extreme accuracy the energy requirements of every home, we would be able to service that demand,” she says.

5. Energy users might be the people that end up driving change

“Consumers are actually far more forward-thinking than government can sometimes be,” says Juliet Davenport, CEO of renewable energy firm Good Energy. “We’re going to have to make it easier if we’re going to get massive uptake of renewables.”

This means making it easier for people to switch to renewable providers and giving people more options – such as letting them buy excess renewable energy from neighbours over a peer-to-peer marketplace. And at the heart of this all is making products that people want to use, Davenport says. “We have to put ourselves in people’s shoes when we’re designing for people, whether that’s a potato peeler or an energy app.”

6. It’s time to stop thinking about energy supply and start thinking about demand

Deptford power station – then the world’s largest energy facility – first rumbled into action on the south bank of the River Thames in 1891. Since then, the logistics of energy distribution have changed fairly little, says Stephen Fitzpatrick, CEO of the energy firm OVO energy.

“You build a large power station, you have a very long wire and you put customers at the end of is,” Fitzpatrick says, and the only way of meeting demand is raising the output of those vast power stations. But it’s time for that to change. Fitzpatrick says we should be connecting our energy supplies to the internet, so we can better predict and manage demand, flipping the previous way of distributing energy on its head. “We need to control the demand to meet the supply,” he says.

[h2]Smart homes might not be as clever as we might think/h2]

It’s all very well having connected smart devices that monitor our energy usage, let us set alarms through voice control and adjust the thermostat, but is this technology really as futuristic as it seems? Designer and author Alexandra Deschamps-Sonsino isn’t so sure.

“The home is not a system,” Deschamps-Sonsino says – and we should be wary about devices that reduce our living spaces to slickly-oiled, impersonal machines. Why? Well, the impact of smart devices might stretch far beyond our homes and impact how we use public spaces. “Space dictates what people will and won’t do, how much time they’ll spend in places and how they’ll use the rest of the city as well,” she says.

7. It’s time for the green battery revolution

About two-thirds of all of the energy that’s produced in the world ends up being wasted, says Martin Anderlind, chief business development officer at battery firm Northvolt. The problem? There aren’t enough storage options to keep that energy around until its needed.

“We need to be able to store energy in time, not just be able to move it around geographically,” Anderlind says. And this means investing big in battery production. In 2021, Northvolt is planning on opening a 370 megawatt factory in Vasteras, Sweden, that should be able to produce 200 batteries a month.

How AI and NLP will affect the media industry

One of the most visible of these technologies has been the use of artificial intelligence (AI) as well as Natural Language Processing (NLP), in process of collecting data and analysing it.

Publishing industry across the world is going through challenges. The dynamic nature of technology trends demands its continuous evolution from publishing to a digital media company. Progress has been made in terms of both content platforms i.e. the move from purely print to a variety of audio-visual avenues (such as television and online news portals, among others), as well as in terms of technology used to gather and publish information. One of the most visible of these technologies has been the use of artificial intelligence (AI) as well as Natural Language Processing (NLP), in process of collecting data and analysing it.

NLP is an area of AI which aims to produce effective human-computer dialogue by teaching computers to understand, organise, analyse and reproduce coherent sentences. NLP software uses a variety of models to achieve this – such as analysing large amounts of data to ‘learn’ a language and its rules as well as relying on manual input of the same rules. Currently, NLP algorithms enable large amounts of data to be absorbed and processed in order to give users the latest stories before they are discovered manually. Mobile news apps like Inshorts, NDTV and others use this process to display snippets in the notification bars, allowing users to be informed of trending stories with minimal effort. The ultimate aim is to allow computers to not only create sentences but to understand the direct and indirect meaning of those generated sentences. This would mean that the articles would contain perspectives too, rather than just facts, showing that the computer was smart enough to understand the language.

In the media industry, the concept of language can be extended not only to written words but to images and videos, as content types have become more diverse over the years. AI and NLP, hence, now possess a larger pool of data to work with and automate so as to generate news reports and other forms of required content.

They can do so in a variety of ways:

Data collection & Analysis

For a media company, there are two facets of data collection. Content in media industry is generated in all possible media formats (viz. text/image/video and audio). Text mining and NLP plays an important role in gathering semantic information about text, audio and video content. Relevant keywords, sentiments, and entities, along with topic classification is computed through NLP. Image classification is used for face detection or finding important parts of an image. All this data is then used to build a knowledge graph for ease of content search, recommendations, and relevant content syndication. The second part of data collection is around collecting time series data on what content is consumed by the end user. This helps the algorithm match user interest with content meta data to serve relevant content.

Real time serving data of web and mobile content helps the publisher to figure out trending content, which is attractive from advertising perspective. Additionally, it enables investigative reporters to obtain all known mentions in and around a topic they are working on within a span of minutes. The data obtained from text mining also serves as a learning process for the computer in order to develop its language skills for future endeavours. For a media company this requires significant investment in developing the infrastructure to collect and analyse large volume of data.

Insights Utilisation

The insights obtained from text mining allow for trends to be identified and leveraged in order to solidify a target audience and appeal to their interests. While this is applicable to new content, feedback from already published content allows for its improvement by using adaptable keywords. Historical analysis of trending content helps the editors focus on the topics they would want to write and distribute across social media. This means that when journalists are assigned specific beats, the algorithms showcase all the stories that relate to the beat immediately based on past data. Social listening tools imbued with NLP algorithms are the preferred mode of gathering the data necessary to achieve this as they can also gather insights into advertising along with long-form content. By serving relevant ads for a target audience, the ROI for the advertisers can be improved resulting in increased revenue for the publisher. High performing advertising campaigns, for example, then become templates for future campaigns and low-performing ones can be analysed for their defects.

Customer Feedback

Along with improving organisational content, NLP algorithms can also help formulate responses to questions posed by both customers as well as journalists. This would enable the system to also track the effects of the response across media platforms to determine the industry scenario. A direct outcome of this report would be the ability to weed out incorrect or ‘fake’ news that may be published by backlinking their sources and exposing their inaccuracies. Websites that publish spoofs and parodies, for example, can be identified and the information they disseminate fact-checked before it goes viral. Smart AI programs would be able to perform these semiotic analyses to determine accuracy.

The media industry today is becoming more dynamic and embracing technology at a faster rate to ensure that its progress can be harnessed. AI and NLP enable computers to understand semantics and determine the best course of action to be taken with respect to content dissemination based on the analysis of industry feedback.

Bill Gates predicted this in 1999

Back in 1999 Bill Gates was all about the hustle, penning a number of books about business and the future. One such book, called Business @ The Speed of Thought, released in 1999, included a number of predictions about the future of business and technology, many of them were spot on.

However, the most notable prediction was about something business owners take for granted: mobile phones and other smart devices.

“People will carry around small devices that allow them to constantly stay in touch and do electronic business from wherever they are,” Gates wrote in 1999.

“They will be able to check the news, see flights they have booked, get information from financial markets, and do just about anything else on these devices.”

“Just about anything” proved to be spot on, as today even the most basic Android device can do all of the above and much more, with some entrepreneurs running virtually their entire business from just one small screen.

However, despite Gates’ premonitions, the billionaire tech genius has his reservations about the next wave of computing, namely AI and robotics. Gates revealed in February he thinks companies should be taxed for introducing robots into their workforce.

“You can’t just give up that income tax, because that’s part of how you’ve been funding that level of human workers. There are many ways to take that extra productivity and generate more taxes. Exactly how you’d do it, measure it, you know, it’s interesting for people to start talking about now,” he said.

“People should be figuring it out. It is really bad if people overall have more fear about what innovation is going to do than they have enthusiasm. That means they won’t shape it for the positive things it can do.”

Bill Gates is an American business magnate, software developer, investor, and philanthropist. He is best known as the co-founder of Microsoft Corporation.