If you want to explore this area further, here's everything you need to know about what data and AI can mean for your future energy costs and how these trends will impact your utility bill over time! The average American home uses more than one trillion kilowatt-hours of electricity annually, or enough to power 3 million homes. Nearly half of that is used by appliances. The good news is that there are several ways to save on energy costs without compromising comfort or convenience. For example, LED bulbs can last up to 50 times longer than traditional incandescent bulbs and use 75 percent less electricity; smart thermostats can automatically adjust the temperature settings in your home based on where people are located, saving you an average of 10% on heating and cooling costs. Data and artificial intelligence (AI) fundamentally change how we consume, manage, produce and distribute energy.

Exploding energy costs have been blamed for the closure of more than 60 public swimming pools across Britain over the past four years. And with the bills for some expected to rise by £100,000 this year, it has left leisure centres scrabbling around for ways to keep the facilities running. It may sound far-fetched, but one leisure centre in Devon is using computer power to heat its swimming pool. The idea works by placing 12 computers inside a white box which is then surrounded by oil to capture the waste heat they produce -- in a similar way to another concept that uses computer servers to heat water in people's homes. Innovative: It may sound far-fetched, but Exmouth Leisure Centre in Devon is using computer power to heat its swimming pool.

Public swimming pools facing closure because of soaring energy bills have been offered a lifeline via new technology to heat the water. Mark Bjornsgaard, the chief executive of the tech startup Deep Green, has trialled the idea in Exmouth, Devon. He has put a small computer data processing centre underneath the pool and the energy from it heats the water. The idea has taken off and up to 20 public pools could be upgraded to the heat system this year. "We built a small data centre in Exmouth leisure centre. Most normal data centres waste the heat that the computers generate. We capture ours and we give it for free to the swimming pool to heat the pool," Bjornsgaard told BBC Radio 4's Today programme.

Everyone is looking for a way to slash their heating bills amid soaring energy prices and the deepening cost-of-living crisis. Now, a British start-up has come up with a new way of doing so using a method that may seem a little bizarre to some -- by fitting a computer server to a household's hot water tank. Heata claims its shoebox-sized device could help Britons save around £150 a year on their energy bills, while small companies can also make use of the computer power available on the servers rather than them being in a large data centre. As the computer gets hot, the tank takes waste heat away from it and uses this to warm water for showers, baths and washing up. Each unit can deliver up to 4.8kWh of hot water per day, the company says -- approximately 80 per cent of the hot water required in an average UK household. As many people will know, laptops and computers can get very hot when running for long periods, with internal fans used to cool them down.

Predicting a customer's propensity-to-pay at an early point in the revenue cycle can provide organisations many opportunities to improve the customer experience, reduce hardship and reduce the risk of impaired cash flow and occurrence of bad debt. With the advancements in data science; machine learning techniques can be used to build models to accurately predict a customer's propensity-to-pay. Creating effective machine learning models without access to large and detailed datasets presents some significant challenges. This paper presents a case-study, conducted on a dataset from an energy organisation, to explore the uncertainty around the creation of machine learning models that are able to predict residential customers entering financial hardship which then reduces their ability to pay energy bills. Incorrect predictions can result in inefficient resource allocation and vulnerable customers not being proactively identified. This study investigates machine learning models' ability to consider different contexts and estimate the uncertainty in the prediction. Seven models from four families of machine learning algorithms are investigated for their novel utilisation. A novel concept of utilising a Baysian Neural Network to the binary classification problem of propensity-to-pay energy bills is proposed and explored for deployment.

The concept of smart homes is not something new. In 1975, a Scotland company developed a communication protocol that enables smart home devices to talk to each other with the help of existing electrical wires of a home. Over the years, numerous technological developments made smart homes accessible to a large number of people around the globe. If you have yet to try smart home technology a try, here are some of the main reasons why you should have it. No matter what type of neighborhood you live in, it's of the utmost importance to do everything you can to keep your home secure.

The subject of artificial intelligence (AI) in education often centers around edtech breakthroughs and the ongoing evolution of the learning spaces inside schools. But AI is also experiencing growth in other sectors that have a direct impact on schools, presenting more areas for the education community to study. Take, for instance, construction and green energy resources. As schools look to cut costs, they are also increasing the adoption of sustainability programs. New state-of-the-art energy efficiency technologies may offer cost savings while reducing a school's carbon footprint.

National Grid has turned to artificial intelligence to help it maintain the wires and pylons that transmit electricity from power stations to homes and businesses across the UK. The firm has been using six drones for the past two years to help inspect its 7,200 miles of overhead lines around England and Wales. Equipped with high-res still, video and infrared cameras, the drones are deployed to assess the steelwork, wear and corrosion, and faults such as damaged conductors. Artificial Intelligence has various definitions, but in general it means a program that uses data to build a model of some aspect of the world. This model is then used to make informed decisions and predictions about future events.

New artificial intelligence technology could make buildings smart enough to become their own energy supplier – and even sell back what they don't use. That's the view of Vijay Natarajan, co-founder and marketing director of AI-powered energy analytics business Qbots, which is based at Manchester Science Park. "We enable buildings to actively participate in the energy markets, rather than just passively receive energy bills," Natarajan explained. This way of looking at energy bills is still relatively new. Most buildings purchase electricity from a supplier, which Natarajan says costs twice as much as the energy itself.

Scientists have invented window blinds that store solar energy and are capable of slashing 70 per cent off homeowners' energy bills. The creators of SolarGaps claim it is the world's first'smart blind' that can store solar energy in a battery and is being marketed as an alternative to rooftop solar panels. A set of blinds is expected to sell for around £210 ($270). Scientists have invented window blinds that store energy which they claim can slash an incredible 70 per cent off homeowners' energy bills The blinds have thin solar panels mounted on slats. A motor controls the direction they point in, and makes sure they are facing the sun.