Analyst Report: Smart Algorithms: The Future of the Energy Industry


Internet companies have made an art out of creating algorithms that can mine data and unleash innovation and opportunities. Picture the famous recommendation engines behind Netflix and Amazon that drive their businesses and have launched new markets for niche content. Google has forever changed advertising and the consumption of information with its sophisticated algorithms. These same types of smart analytics will create the next-generation of innovation around energy, form the foundation of new business opportunities for greentech entrepreneurs, and help both individuals and organizations finally reduce their energy consumption.

“Utilities will have to embrace energy information analytics if they want to reach their energy efficiency targets,” says Tom Scaramellino, the founder and CEO of startup Efficiency 2.0, which has spent the last 5 years developing energy management algorithms that can offer utility customers highly personalized energy-efficiency recommendations. Put another way, Scaramellino added, compared to the majority of other industries, “It’s actually pretty sad that utilities haven’t picked up on this yet.”

Efficiency 2.0 is using its algorithms — developed by researchers that came from Yale, Harvard and Columbia — to get utility customers to reduce their energy consumption in various ways. Scaramellino says that when Efficiency 2.0 launched its tool, it was the first to be based on statistical models and incorporate the wealth of publicly-available data, like information from real estate companies about home size, estimated income level, and predictions like whether or not a home has a natural gas stove. Using this type of data the service makes a profile of the customer and can make highly-accurate educated guesses on how the customer would or would not reduce their energy consumption. For example, Efficiency 2.0’s service would not recommend suggestions for a home energy retrofit that would cost five-figures to a student living in an apartment building (as if!).

Energy algorithms will be crucial for not just changing consumer behavior, but also for automatically reducing energy consumption. EcoFactor, a startup which sells a service for connected thermostats, uses its algorithms and publicly-available information to maintain a comfortable temperature in homes, while shaving off energy consumption. Like Efficiency 2.0, EcoFactor’s tool uses data like weather conditions and real estate info as well as manually entered consumer data to tweak a home’s thermostat every minute. EcoFactor says over a month it can save 20-30 percent on the heating and cooling costs in an energy bill.

Startups are using smart analytics to automatically reduce the energy consumption of commercial and industrial buildings, too. Toronto-based company Regen Energy developed an algorithm based on “swarm logic” — in which each individual makes a decision based on the actions of the group — with the idea that if energy-consuming devices in buildings were made to act like a swarm of bees, a school of fish or a flock of birds, it could significantly cut energy consumption. Regen Energy’s wireless nodes bolt onto HVAC systems in large buildings and its software uses swarm logic to lower and raise the temperature of the HVAC systems in succession.

Smart Energy Opportunity

So why have all these startups emerged with smart energy algorithms to address energy efficiency? Because the market opportunity could be massive. Utilities are expected to spend $210 billion globally on digital power grid infrastructure upgrades between 2010 and 2015, according to Pike Research. All of that IT investment is expected to unleash — on a daily basis — 3,000 times more data than utilities currently have to manage, according to Foundation Capital partner Warren Weiss, who made the comments at our Smart Grid bunker event earlier this year. Some estimates suggest that if 140 million smart meters, which update energy info every 15 minutes, are installed over the next 10 years in the U.S., they could produce a massive 100 petabytes. A single petabyte — or 1 quadrillion bytes of information — is equivalent to the amount of data contained in 20 million four-drawer filing cabinets filled with text.

Utilities, which historically tend to be conservative and not very tech-savvy, will soon be overwhelmed with this energy data and will be looking for tools to use to manage and mine it. Software firms Ecologic Analytics and eMeter have been innovating around the software link that connects smart meter data to a utilities’ back office (commonly called meter data management systems, or MDMS). Utilities will need smart algorithms to help them better predict how much energy certain types of customers will use at different times of day — better “educated guesses” can save utilities a lot of money in terms of knowing when to generate more or less power. IBM has created weather prediction algorithms for a utility-focused service called Deep Thunder that alerts utilities when they need to tailor energy services or tweak their generation plans based on upcoming weather.

The more algorithms can rely on a wealth of information, and reduce the need for additional hardware, the better the opportunity for the startup. Energy sensor and monitoring startp Zensi has developed algorithms around listening to the voltage noise of different appliances via home circuitry, and — similar to photo recognition software — can make accurate educated guesses about what devices are consuming how much energy. Removing the hardware needed by similar energy management systems for each appliance could significantly cut down the cost of an energy management service. If you’re not convinced of the opportunity by now, maybe this will convince you: Belkin recently bought up Zensi before it raised any venture capital financing.

Investors are also seeing the opportunity — EcoFactor recently closed $5.9 million in funding from RockPort Capital Partners and Claremont Creek Ventures. Foundation Capital’s Weiss has said he is looking at the next-generation of customer information management systems, outage management systems, billing systems — including the prepaid cellular-style model — and data analytics that can mine all of the subsequent energy information and better predict how energy usage happens.

All of this potential opportunity to build energy information algorithms means that there will likely be parallels between, and lessons learned from, the Internet industry and companies that are mining energy data. Efficiency 2.0’s CEO Scaramellino says he sees his service as the for home energy use. Energy management companies like Lucid Design Group have recently developed Facebook-style software for social networking around building energy use. Now who’s going to look to the next-generation of web firms, like music discovery site Pandora and Twitter influence site Klout for energy software innovation?

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