If there’s one thing every energy professional knows, it’s that convincing clients or building managers to invest in energy efficiency is hard. Commercial building owners and managers specifically face major points of friction when it comes to implementing meaningful energy saving measures – especially when a large portfolio of buildings is involved. In this post, learn what these barriers are and new tools to overcome them in which artificial intelligence meets energy efficiency.
3 Reasons Why Energy Efficiency is So Hard to Scale
Part of the problem is the persistent issue of energy performance drift — buildings and building systems should be highly efficient, but performance deteriorates rapidly or never matches the model intended by building designers and architects.
Before being able to even begin talking about savings, however, building portfolio owners and operators must first pinpoint which locations or energy sources are the main “energy vampires” or culprits, and then understand the causes behind them such as ineffective controls or a mismatch between design assumptions and building occupant behaviors. Once those steps are done, then it’s time to identify the mitigation actions that will work best for their organisation.
The second barrier that makes energy efficiency so difficult to scale across large building portfolios is the high cost, in terms of both time and resources, of performing on-site energy audits and installing monitoring and control hardware. When you consider how costs can run into the thousands just to get the diagnostics done, it’s no wonder energy efficiency gets pushed to the backburner in commercial buildings.
The third point of friction is flat-out lack of action. Not only do building managers without an energy efficiency background often struggle to see where the savings are in the first place, but it can be difficult for them to understand the nuances of the energy-saving options available to them.
Knowing exactly how their budget will be affected makes it easy for building managers to succumb to decision fatigue, which leads to them putting off selecting the best-suited choice for their needs. Vetting and selecting an energy conservation measure — each with its own set of associated pros and cons — carries very real transaction costs, which is why it’s important to make an informed, meaningful choice.
Introducing EnergyGrader: Where AI Meets Energy Efficiency
Luckily, new software tools have been developed to help do just that. DEXMA, Europe’s leading energy management software provider, has developed an all-new platform called EnergyGrader, designed to help any organisation with an energy bill to identify energy-saving opportunities in a fast and frictionless way.
What do we mean by “frictionless?” Everything is done in the cloud, eliminating the need for any kind of hardware installation or on-site audit. Personalised energy insights and automatic recommendations are made possible thanks to proprietary pattern recognition algorithms that automatically benchmark users’ energy spend and behaviour against a database of 50,000 buildings in order to detect similar patterns.
The EnergyGrader recommendation library is the result of translating this complex AI system into simple energy-saving solutions. This AI platform for energy savings can recommend several different solutions automatically, including:
- which period of the year produces savings
- insights into energy efficiency project CAPEX or OPEX
- when to expect savings
- payback periods and more.
EnergyGrader’s recommendation library will continue to expand and provide increasingly precise recommendations over time, as it learns from users’ energy spending behaviour and building profiles.
Streamlined Energy Efficiency for Everyone
Using energy bill data as its main input, EnergyGrader serves as a way for building operators, owners, facility managers, and even business managers with little to no background in energy efficiency to pinpoint real energy savings opportunities with unprecedented ease and speed. Users can easily compare various personalised recommendations, according to payback period and ROI, in order to catalyse the process of finding a specific solution for their energy efficiency needs.
Even though building operators might be able to visualise their energy usage on their energy bill, they often don’t take action because they don’t understand the effects of various actions might have on their budget, or perceive energy efficiency interventions as too complex or disruptive. EnergyGrader takes the friction out of energy-efficiency decisions by linking them to real consumption data, and putting them in context for each and every user.
The availability of customised recommendations based on real energy spend data can provide the necessary justification and impetus to encourage building owners to actually implement energy efficiency projects. For owners with larger building portfolios, the opportunity to scale projects is enormous, resulting in the potential for huge energy and costs savings.
Curious to see how it works for yourself? Check out this free webinar on scaling energy efficiency with AI and how it can benefit your company, whether you are an energy management pro or totally new to the energy world: