By Fiona Burlig
It’s fall, which means beautiful leaves, pumpkin-flavored everything, and back-to-school fever. As students and teachers sink into a new school year, the last thing they’re probably thinking about is how much energy they’re using. It turns out, a lot. Nationwide schools spend $8 billion a year on energy – second only to personnel in K-12 budgets. With looming cuts to federal education spending, schools are going to need to cut back. Energy is one line item they can trim through efficiency improvements like new air conditioning systems or LED lighting.
Beyond possible greenhouse gas reductions, many energy efficiency investments are projected to pay for themselves by lowering power bills. Importantly, though, arguments on the cost-effectiveness of these improvements are overwhelmingly based on projection models, rather than real-world data. These models have been shown to be flawed time and again. Yet, getting these measurements right is important for cash-strapped districts trying to prioritize investments in much-needed upgrades.
In a new study, my colleagues at MIT, UC Berkeley, UC Davis and Northwestern University and I devised a brand-new machine learning approach to measure the effectiveness of energy efficiency upgrades in California schools. Armed with a wealth of real-world data – electricity consumption every 15 minutes at all K-12 schools in the Pacific Gas and Electric service territory in California – we measured the impacts of the improvements and compared our findings to the model projections.
The good news: the upgrades did lower energy consumption at the average school by 3%, freeing up real money to pay for textbooks and supplies. The bad news: schools saved only 24% of what was projected. Put another way, if the model estimates suggested the school would save 100 kilowatts an hour per year for a given investment, our estimates suggested that they only saved 24 kilowatts an hour per year. If the school invested $400,000, expecting that they’d recoup their investment in the form of lower energy bills in 4 years, our estimates imply they might never see it pay off.
This does not tell us that energy efficiency investments shouldn’t be made, but instead, that we as researchers need to improve projection models and continue doing real-world evaluations like this one. Additionally, this work shows just how important it is for policymakers to include retrospective studies into governmental programs. Doing so can help building managers determine which investments deliver the greatest savings and optimize their investment dollars. For example, we discovered that lighting upgrades and improvements related to heating, ventilation and cooling (HVAC) appear to do the best, achieving 49% and 42% of expected savings, respectively…