Energy disruption comes through two planks. First, people using more renewables (and less dirty energy), which, in turn, comes through lowering the costs of renewables and decreasing the use of dirty energy. Second, energy disruption—and a cleaner world—comes through collating the millions of sensors and Internet of Things (IoT) devices installed each year and managing them on one pane of glass.
AI achieves both objectives through its capabilities that allow consumers, retailers, and businesses to predict, analyze, and classify energy data to control them. AIOps helps engineers collate all energy-released data on one pane of glass and monitor, predict, and implement energy controls in real-time through AI functionalities.
AI Enables Adaptive Controls
AI makes our world more energy efficient through three of its capabilities: prediction, classification, and insight generation.
Prediction problems include questions like: Can I predict whether this will or will not occur, given the available data? In the retail and commercial space, fault prediction and dynamic maintenance are among the most straightforward uses of AI and helps operators predict equipment failures. It does this by using sensor data from various units and significantly reduces downtime and maintenance costs.
For example, DeepMind, a subset of Google, uses reinforcement learning to reduce energy use in its data centers by 15%. In Cambridge, Origami Energy uses ML to predict asset availability and market prices in near real-time.
Then, there is insight generation, where I can get information from the available big data collated by AI. In a commercial and retail setting, AI models learn from individual data and then issue changes to individual units.
For example, U.K.-based electricity and gas utility company National Grid uses AI learning tools to better forecast demand to the system, resolving to reduce Britain’s energy usage by 10%.
As a practical example, I’ve seen that this massive surge in energy comes from using my washing machine on certain days. I use this data to problem-solve ways to slash the energy increase. I group incoming data from my energy devices into categories, enabling me to deduce where I can reduce energy usage.
For Devs and Architects
For IT developers and AI engineers, AI achieves efficient energy management by helping engineers collate and manage all AI-driven assets on one piece of glass. These internet-connected devices include generation assets, smart buildings, IoT and smart meters, and EV and mobility. This smart microgrid helps engineers do the following:
Monitor all energy assets—for example, solar, EV, smart buildings, or distributed energy—regardless of form.
Control and optimize energy assets in real-time, trying to regulate ecosystems at the lowest possible cost. This is done through ML, gleaning insights from the mass of accumulated data on energy generation and consumption usage (for example, if the solar system will generate solar energy in the next 15 minutes). Engineers manage these assets based on the ML algorithm.
Collect insight from aggregated big data—for example, from renewable energy assets and electricity tariffs.
Analyze and predict how the energy assets perform, adjusting algorithms based on generation and consumption patterns, like providing scheduling information for energy providers.
Through these controls, engineers can forecast and prevent potential problems.
Our world would be a less healthy, more energy-dense place if it weren’t for AI.
From platforms like your Google Fuchsia OS, you can supervise, regulate, and control your energy assets to cut costs, predict how they will work in the future, and introduce innovation, among other items. For customers, business owners, and developers, AI helps them see in real-time how their energy assets perform and decide how to recalibrate to cut energy usage, use more renewable energy, or more wisely manage their energy devices.
Leah Zitter, Ph.D., has a master’s in philosophy, epistemology, and logic and a Ph.D. in research psychology.
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