People around the globe have grown increasingly reliant on energy to power smartphones, computer systems, and their homes. At the same time, the amount of energy produced is expanding, reports CB Insights. In the U.S., the energy storage market was capable of deploying over 1,000 MWh of storage over the last four years, and projections for energy storage is expected to double in 2018. Even though the amount of energy produced and stored is increasing, the demand for energy efficiency is rising in tandem. Education Facilities Managers need to understand how energy efficiency and renewable energy will affect school systems and how artificial intelligence and machine learning can positively impact goals of reducing their carbon footprints.

Energy Efficiency Programs Under Perform Regularly in Schools

A leading problem with energy efficiency programs in the K-12 sector goes back to basic problems of lack of strategy and calculating the cost savings of energy efficiency improvements against realized savings. On average, schools are capable of reducing energy use by 3 percent, but as little as 24 percent of projections are realized. In other words, the biggest energy efficiency programs are still falling short of expected savings. Unfortunately, this can lead Education Facilities Managers to forgo energy efficiency and renewable energy improvements to enhance school building conditions. However, artificial intelligence is changing the game.

Artificial intelligence and Machine Learning Improve Energy Efficiency Savings Realization

The use of artificial intelligence and machine learning have been shown to improve the accuracy in determining actual cost savings after implementation of energy efficiency upgrades. The best energy efficiency and renewable energy resources on the planet are incapable of overcoming one fundamental problem, human error. People make mistakes, and while this is avoidable, artificial intelligence and machine learning can be leveraged to consider how human actions and interactions within their own environments in K-12 schools, adversely affect energy savings realized through investment in a new energy efficiency program.

Additional improvements through artificial intelligence, such as an autonomous grid, have the potential to make transitions and responses to major events involving the power grid seamless. These so-called “smart grids” could be used to divert energy between facility assets, manage multiple incoming sources of energy and adjust energy output to increase efficiency.

How to Leverage Artificial Intelligence for Energy Efficiency and Renewable Energy

Artificial intelligence and machine learning involve a basic concept: systems that have the capacity to learn from their own actions and the interactions with other systems and people enabling an unmatched level of optimization and prediction capability, reports WePower. In the coming years, upgrades to improve energy efficiency and renewable energy use will become more integrated with off-the-shelf components and solutions. Essentially, the use of AI and machine learning will leverage energy efficient devices and systems, like Google’s DeepMind Technology. DeepMind is already working to develop innovative ways to reduce energy consumption. In fact, Google DeepMind successfully reduced electricity consumption in Google's data centers by up to 15 percent, but the savings are realized over longer durations, reflecting an overall decrease in total consumption of energy usage by 40 percent.

Obviously, Facilities Managers do not have Google DeepMind at their fingertips, but those that understand how to leverage artificial intelligence for energy efficiency and renewable energy, such as the Cenergistic Executive® platform which provides big-data, analytics and machine learning (ML) techniques to embolden facilities’ team to accelerate change and maximize savings, can dramatically reduce energy use and save money. To achieve this feat, Education Facilities Managers should follow these steps:

1. Conduct a comprehensive energy audit to determine areas of weaknesses and strengths in energy use and efficiency in the facility.
2. Use the stored data and projections for energy kilowatt hour cost to determine the future cost of energy for the facility.
3. Review how energy and efficiency and renewable energy upgrades to current facilities would affect overall energy costs in the future.
4. Leverage advanced technologies, especially artificial intelligence, and machine learning, to reduce confounding factors and improve accuracy in making savings' projections.
5. Take advantage of external resources to understand and leverage data, like an on-site data specialist provided by Cenergistic.

Take Advantage of New Technologies and Experience in Making Energy Efficiency Upgrades