CLUE - The next step to a smarter EnergyLink

› predictive analytics

› data science and computing

› behavioural monitoring

› machine learning

CLUE THE NEXT STEP TO A SMARTER ENERGYLINK Machine Learning and Data Mining EnergyLink is changing the way businesses operate.

COzero Smart Energy for Smart Business COzero offers smarter energy options for Australian businesses of all shapes and sizes.

cozero.com.au

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COzero – Smart Energy for Smart Business

The Next Step to a Smarter EnergyLink

THE FUTURE OF PREDICTIVE ANALYTICS ON ENERGY CONSUMPTION The ability of machines to make inferences without human interaction has changed the scope of data analysis in customer service.

Businesses of all sizes can now leverage the power of modern-day computing to better utilise the ever-increasing availability of big data.

Consumer behaviour can then be predicted with incredible accuracy for a tailored customer experience to the benefit of all involved.

This white paper uses contemporary analysis techniques to demonstrate how various Australian businesses consume power, and to determine the relationships between those usage characteristics.

Authors Benjamin McCoy Jeremy Bell Youhan Cheery Luke Fries Carlos Aya

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COzero – Smart Energy for Smart Business

INTRODUCTION Clustering is an unsupervised machine learning method which uses distinguishing features to group a set of objects based on their data with no assistance or training, causing objects with similar properties to be clustered together. Clustering provides a way by which hidden patterns present in data can be uncovered by relating like objects, forming a mine from which a number of insights about those objects can be exploited.

There are a variety of algorithms that can be applied to a given dataset, and an endless number of features that can be explored.

Though patterns in the data may be abstract, minute or beyond 3 dimensions, with careful feature analysis practitioners are able to provide context-specific knowledge that differentiates one situation from another. For this reason the quality of the clustering is usually far beyond that which would be achievable by performing the task manually.

One of the primary uses of clustering is making recommendations to customers. Spotify, Amazon, Netflix, IMDB and Facebook are but a few of the companies which thrive on the application of cutting edge machine learning. Early adoption of these algorithms has contributed to them overtaking market competition to become some of the most successful and well known businesses today.

The Next Step to a Smarter EnergyLink

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COzero – Smart Energy for Smart Business

CHALLENGE According to the Council of Australian Governments (COAG), space conditioning accounts for an average of 43% of energy usage across offices in Australia (Figure 1) and an average of 52% of energy usage in hotels (Figure 2). For this reason, accounting for climate is one of the first steps in improving recommendations to better help clients manage their consumption.

20%

Total Equipment

2%

Domestic hot water

9%

Other electrical process

26% Lighting

43%

Heating, ventilation and air conditioning (HVAC)

Figure 1: Offices (All), Electricity End Use Shares, 1999 – 2012 / Average all periods, n=1150 / Original source: pitt&sherry

The Next Step to a Smarter EnergyLink

1%

6%

Domestic hot water (electric)

Pool heating (electric)

10%

11%

Other electrical process

Total Equipment

20%

52%

Lighting

Heating, ventilation and air conditioning (HVAC)

Figure 2: Hotels, Electricity End Use Shares, 1999 – 2012 / Original source: pitt&sherry

The expansion of COzero’s client base, particularly the recent partnership with Ennet, necessitates the development of a more intelligent EnergyLink platform. This development complements the manual recommendations system for a solution that is sustainable, personalised, and can take advantage of the new client data.

Ultimately, this project will allow for a better customer experience, and more savings for COzero’s rapidly growing client base.

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COzero – Smart Energy for Smart Business

SOLUTION The solution to this challenge is Clue, COzero’s clustering and recommendations algorithm.

Clue uses machine learning to automate the process of detecting anomalies in site operation. It provides insights into the relationships between similar businesses, and can estimate energy usage for a site based on the weather forecast.

Employing machine learning techniques, Clue can cluster thousands of client’s energy data, identifying sites that operate similarly, and constructing models for removing temperature dependence.

Sites with similar energy profiles often have the same energy management requirements, so similar recommendations will be appropriate for a majority of members in a cluster. By monitoring the behaviour of comparable sites, Clue is able to determine recommendations that are most effective for each type of site and result in fiscal benefit for the customer.

The Next Step to a Smarter EnergyLink

BENEFITS Clue generates collections of similarly operating sites. By combining information about sites within a cluster, the expected performance of individual sites can be determined. This allows COzero to present customers with achievable goals. Sites with drastically poor performance can be prioritised, and sites who are performing well can be analysed to identify energy reduction strategies.

By automating the detection of site irregularities, the speed and responsiveness of the COzero recommendation system will be dramatically improved. This allows clients to more quickly address issues with their business’ energy consumption, saving time and money. The speed of the machine learning algorithm allows thousands of sites to be continuously monitored and analysed non-stop, 24 hours a day.

As part of the clustering process, a temperature independent energy profile of any site can be generated. Without the large variability induced by temperature, energy usage within a site can be compared on a week to week basis, allowing analysis to be performed without constant manual correction for temperature.

The onboarding process allows instant integration of past energy data into the algorithm.

This provides clients with statistics on their energy usage as soon as they join COzero, meaning almost-instantaneous savings.

Clue learns from new data, meaning as more data is fed into the system, it becomes more accurate and reliable. This makes Clue scalable, robust, and futureproof.

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COzero – Smart Energy for Smart Business

CONCLUSION Machine Learning and data mining are changing the way businesses operate.

COzero has embraced this change, incorporating state of the art machine learning algorithms into the core of EnergyLink. Machine learning is a constantly evolving field, and COzero has the expertise required to adapt and take advantage of the latest cutting edge technology.

Clue is the first step in progressing COzero to the forefront of technology in the energy saving space. The ability to make fast and accurate recommendations at a scalable level is what will set COzero apart from competitors.

The Next Step to a Smarter EnergyLink

ABOUT COZERO COzero is a business to business energy management firm which provides valuable insights into electricity consumption via the EnergyLink platform. Through cutting edge data science and computing, COzero is able to monitor and understand usage patterns, and recommend strategies to reduce energy consumption. Clients can remove the stress of energy management and focus on the more fulfilling aspects of business.

Founded in 2007 and based in Sydney, Australia, COzero is a leader in the smart business energy space. EnergyLink, our flagship energy management software provides our clients smarter ways to consume and track their energy.

To find out more about COzero and EnergyLink, visit www.cozero.com.au

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