Energy Efficiency Examples

Energy Efficiency Examples

Energy Efficiency Examples : Analyzing Energy Efficiency: The U.S. Power Grid, Innovative Niche Community Solutions and the Role AI Will Play Going Forward

Energy Efficiency Examples : With Hurricane Isaias making landfall on parts of the Northeastern United States on August 4th, 2020 and leaving over 1.7 million without power, many Americans are re-examining the US power grid, which is often overlooked. As America’s largest functioning machine is almost always taken for granted by the billions that interact with it each day, both domestically and abroad, this social spotlighting is rare.

It is quite clear, however, that despite the recent advancements in renewable energy, implementation of such technologies across communities globally for sustainable living is still in its early stages.

The US power grid is a remarkable creation: the Department of Energy (DOE) reports that “over 450,000 miles of high-voltage power lines and 160,000 miles of overhead transmission lines” connect electrical plants to homes and businesses across the United States.

It is, however, outdated, and with the threat of the looming climate crisis, energy efficiency is a focal point in emission reduction. As progress continues to be made in the clean energy world, the U.S. power grid has a litany of problems that leave users powerless in driving the future of energy forward.

Around the world, the alternative idea of smaller, smart-grid communities is starting to gain traction for those who think the national grid is not adapting fast enough.

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Since the 1970s, the most extreme off-grid living space has been ‘Earthships.’ Present in six continents, these homes are made of recycled materials (specifically abundantly wasted tires), provide water, food, and sewage treatment, and produce their own energy.

While these homes represent true self-sufficiency and are incredibly distinct in their architecture, they are rarely situated for communal living – a major drawback for the vast majority of people.

Smart-grid communities are a more viable option for those who don’t want to be national grid participants, but still want to be energy independent. Located primarily in the Netherlands and around Europe, these are small communities committed to living with a light environmental footprint and having a say in the type of energy they use.

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Smarthoods is an organization focused on building the modern, energy-decentralized ecovillage. With a mission to “develop the most livable and resilient neighborhoods,” living in a ‘smarthood’ claims to “reduce your ecological footprint by 40%” and “never pay a utility bill again.” The start-up looks to utilize all the best aspects from self-sufficient, sustainable and smart eco-communities already functioning across Europe. Their community plan is below:

The community is meant to operate as a semi-autonomous unit that, if scaled across countries, could decentralize sources of power and make the world a more energy-efficient and resilient place. The project is still in its early stages.

Regardless of where you decide to live, the question of how to intelligently manage energy remains at the center of mitigating climate change. And there are so many developing data points that the problem is now too comprehensive for human intelligence alone.

Enter Artificial Intelligence. The field continues to be among the fastest growing sectors in the tech world, and its applications are seemingly endless with the emergence of big data.

Artificial Intelligence can and will monitor, organize, and efficiently distribute power across America and in microgrid communities across the world. By harnessing the power of robust datasets, AI can analyze data points from every power plant across the country or community and account for every megawatt of energy produced, from wind power to natural gas.

To make efficient predictions of what amount of energy will need to be allocated where, Machine Learning can be applied to come up with applications for supervised, unsupervised, and reinforcement learning that will help revolutionize power efficiency. For example, by programming a reward metric to use as little energy as possible, a machine learning program could reduce societal intake of energy significantly.

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At the household level, ‘smart home’ Internet of Things (IoT) technology is growing in popularity. As these IoT devices collect large amounts of data, AI technology can be implemented to learn consumer preferences. The Artificial Intelligence of Things (AIoT) can help reduce energy consumption in households across the world.

For example, Nest’s Smart Thermostat allows you to only turn on Air Conditioning when you need it, saving consumers money on their energy bill by decreasing overall energy consumption: a win-win. And with the quantity of connected IoT devices expected to grow by over 300% to 64 billion devices by 2025, the energy savings will start to add up.

Whether you are on the power grid, integrate AIoT technology or believe in microgrid energy decentralization, the use of AI – through machine learning or AIoT – is essential to achieving energy efficiency.

The power outage caused by Hurricane Isaias has once again proven the essentiality of energy in our everyday life and implores us to waste as little energy as possible. In the meantime, humans will continue to consider all options – from an individual level to a national stage – for the next bright idea in energy efficiency.

Written by Jason Kauppila

Edited by Alexander Fleiss, Gihyen Eom, Rohan Mehta, Calvin Ma