Leveraging AI to build hyper-personal energy-saving advice for renewable asset campaigns
Looking at the energy transition landscape, we notice that more and more customers have turned into energy producers and choose electrification to reduce costs and do something beneficial for the world. Electric vehicles, solar panels, and batteries are some of the renewable assets that customers are installing more frequently in their homes. Once the first renewable product is bought, it might even trigger additional purchases to take the next investment step toward electrification. For example, drawing from DELTA-EE research, it has been observed that ⅓ of EV owners have solar PVs in their houses.
The opportunity for energy retailers is that they can build longer and more profitable relationships with these customers by offering them personalised energy advice that caters to their specific needs and characteristics. Advice to these customers can have the form of a call to action to adjust their behaviour in order to claim more energy savings or maximise the benefits they can get from their assets.
The challenge is that energy retailers still lack the behind-the-meter information that could allow them to offer meaningful and personalised advice to these specific clusters of customers. Where smart metering is available, the data from smart meters can help as they contain rich information for the behind-the meter-consumption and behaviour of customers. Analysing the data with trained appliance recognition algorithms and Artificial Intelligence (AI) generated methods offers information regarding the consumers' renewables acquisition or ownership.
Below we analysed 3 types of insights that can become available for an energy retailer, and some examples of advice they could build for customers with renewable assets.
1. Energy insights for EV owners
When a customer gets an electric vehicle, it usually comes with a power socket Level 1 charger. Eventually, they can invest in a stronger Level 2, whose power is greater and charging goes faster. The Level 2 chargers allow for services like scheduling EV charging for specific hours of the day, if dynamic tariffs are in place, via a mobile app. By detecting and analysing the energy demand curve produced from the EV charging and applying to the time series Machine Learning analytics, energy retailers are able to know not only if there is an EV in the house but also what kind of charger the house has (a metric depicted below in Graph 1).
Graph 1 depicts how NET2GRID was able to produce conclusions in regards to whether an EV charging event and thus, an EV, was present in the household, as well as what kind of charger was used (for example, a Level 1 or Level 2). By knowing this specific event (i.e. the charging), and the type of it, the energy retailer can offer a specific EV tariff plan to EV owners to facilitate charging during certain times of the day when prices are low. The energy company can give incentives for charging times when electricity is cheaper or greener, while also minimising the imbalance burden off of the grid. Another opportunity for EV owners to have a Level 1 charger is to offer them a discount to buy a brand new Level 2 smart charger in order to charge their cars faster. Simultaneously, now that the marketing department is aware, they can avoid repeating the same kind of offers to the customers who already have a Level 2 charger.
2. Energy insights for EV owners having solar panels
There is also a relationship between EV owners and solar panel owners. Another piece of data research by DELTA-EE points out that 9-15% of new EV owners purchase PV panels within 6 months to charge their cars via a home charging point while using the electricity generated from their solar panels.
Machine Learning analytics can produce detailed insights from EV owners having solar panels; for example, the numbers of them and their solar panels capacity (Graph 2). The information can be organised into clusters, meaning that a number of users who have an EV and a large solar installation can be approached for different savings advice than the ones who have a smaller installation.
3. Energy insights for solar and battery owners
If some users have batteries in their homes, their energy retailer could offer them a dynamic hourly charging tariff so that they can charge it when the energy prices are lower. With data science, it is possible to determine which customers with solar power are best suited to acquire a home battery (BESS) based on the size of their solar installation (Graph 3) and the energy tariffs they use with their energy supplier. Coupled with information about home energy consumption, an energy retailer could determine, for example, the users that have a big solar installation at home and they can produce energy much more than what they consume, so investing in a battery could be a profitable suggestion to them.
How to incorporate rich energy insights into your organisation
There are multiple ways for marketing departments within utilities to make use of smart insights to start building personalised advice. Utilities are already using SaaS solutions, CRM systems, dashboards, or different marketing tools and engines where they record information from their customers (names, first contract dates, segments, social media, etc). These tools can be enhanced with smart insights and information about the users’ consumption and their household appliance's energy profiles.
What NET2GRID offers
NET2GRID produces energy insights and predictions for leading energy retailers. With its innovative product ‘’Customer Intelligence’’, NET2GRID can provide valuable insights to energy retailers to help them make smarter decisions and offer tailored and hyper-personalised services to their customers. NET2GRID can effectively detect EVs, solar installations and batteries behind the meter, plus more than 14 home appliances and 8 energy activities. NET2GRID analyses this appliance information and provides the output to the energy utility in the form of household consumption insights and profile reports. Major energy retailers and organisations worldwide are trusting NET2GRID’s analytics capabilities like E.ON Germany, EDP, and Rabobank.
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Note: The above graphs are used only as illustrations to showcase how NET2GRID’s energy insights can look when incorporated into a marketing tool or dashboard. NET2GRID doesn’t supply the marketing tool or dashboard.