Statistical, dynamical, integral: the three waves of ecohacking

Statistical, dynamical, integral: the three waves of ecohacking

John McKibbin Follow

One of the most exciting developments in the energy sector in recent years has been the emergence of 'ecohacking'. Beginning around 5 years ago, we've witnessed the emergence of a swathe of companies applying the latest web technology to drive the uptake of energy efficiency and renewables.

Here we characterise the emergence of ecohacking as three waves to paint a picture of the broad waves of innovation that have occured while offering our vision for the sector.

Dynamical: bottom-up simulation

Traditional ecohacking, which we call dynamical ecohacking, involves sending an expert into buildings to gather facility data including the building features, equipment and appliances, and then running bottom-up simulations to provide a blueprint for action.

The strength of the dynamical approach is the deep insights it offers into the underlying mechanics of energy consumption and generation, and its ability to provide a bankable business case to target and justify deep energy savings.

The limitation of the approach is that it is expensive and time-consuming. A conventional building energy audit takes at least a day to perform by a trained energy auditor, and much more if a detailed business case is desired. As a result conventional energy audits haven't been applied in the vast majority of households and small businesses.

Statistical: top-down analytics

The second wave of ecohacking, which we call statistical ecohacking, involves collecting large datasets describing billed consumption, sociodemographics, and climate and developing statistical insights using big data analytics and cloud-based computation.

The strength of the statistical approach is its relatively low cost and high scalability, meaning it can be applied to very large numbers of households and businesses.

The limitation of the approach is the limited insights and savings that can be obtained. In the absence of facility data, it is very difficult to, for instance, identify whether a building is under-insulated or provide bankable estimates of the bill savings arising from energy saving measures. As a result, the recommendations arising from statistical approach are more of a general list of suggested actions than a targeted blueprint, limiting the savings that can be feasibly achieved.

Integral: analytics-informed simulation and simulation-informed analytics

We believe a new wave of innovation is currently emerging, which we call integrated ecohacking, defined by the increasing convergence of bottom-up simulation with top-down analytics.

We think broad-based datasets and big data analytics will increasingly be applied to bottom-up simulations. For instance, at Ecologic Apps we're accumulating very large datasets of climate, appliance ownership, behavioural and tariff data from around the world. We're then applying a combination of geospatial and statistical algorithms to generate smart defaults for our bottom up simulations. The power of this approach is its ability to deliver deeper energy savings than offered by conventional analytics at a considerably reduced time and cost than conventional bottom-up audits.

We also see a corresponding role for bottom-up simulations in informing top-down forecasting and investment planning. For instance, we're exploring the application of our datasets and tools to simulating the energy consumed by entire cities - providing a much more informed basis for network investment. We're also exploring partnerships with a range of utilities, councils and government agencies to use our data and tools to target large scale energy efficiency and renewable energy investments.

We believe an integrated approach will be key to combining the depth of energy savings achieved by bottom-up simulations with the broad reach of top-down analytics. Watch this space.