If there’s one thing every energy professional knows, it’s thatconvincing clients or building managers to invest in energy efficiency is hard．Commercial building owners and managers specifically face major points offrictionwhen 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 whichartificial 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 firstpinpoint 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 thehigh cost, in terms of both time and resources, of performing on-site energy audits and installing monitoring and control hardware．当你考虑成本如何达到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 isflat-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 todecision 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 (Now called DEXMA Detect): Where AI Meets Energy Efficiency
Luckily,new software toolshave been developed to help do just that. DEXMA, Europe’s leading energy management software provider, has developed an all-new platform calledEnergyGrader (Now called DEXMA Detect), designed to helpany organisation with an energy billto identify energy-saving opportunities in afastandfrictionlessway.
What do we mean by “frictionless?” Everything is donein the cloud, eliminating the need for any kind ofhardware installationoron-site audit．Personalised energy insights and automatic recommendations are made possible thanks to proprietarypattern recognition algorithmsthat automatically benchmark users’ energy spend and behaviour against a database of 50,000 buildings in order to detect similar patterns.
The EnergyGrader (Now called DEXMA Detect) 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 projectCAPEXorOPEX
- when to expect savings
- payback periods and more.
EnergyGrader’s (Now called DEXMA Detect) 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
Usingenergy bill dataas its main input, EnergyGrader (Now called DEXMA Detect) serves as a way for building operators, owners, facility managers, and even business managerswith little to no background in energy efficiencyto 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 tocatalyse the process of finding a specific solutionfor 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 (Now called DEXMA Detect) takes the friction out of energy-efficiency decisions bylinking them to real consumption data, andputting them in contextfor each and every user.
The availability ofcustomised recommendations based on real energy spend datacan 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 thisfreewebinaron 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:
