With smart metering data starting to be available to end consumers in some markets, Artificial Intelligence (AI) and Machine Learning (ML) techniques enable the creation of new highly tailored energy services.
This innovative type of services increases value not only for the utilities but also to the end consumers.
Most cost benefit analyses of the deployment of smart meters across the world, underline the importance of the end consumers to have access to the data generated by smart metering devices (their load curves), to better understand their consumptions, thus unlocking the potential for improving their energy efficiency. However, neither can they always interpret such data nor are capable of reasoning about what effective measures to adopt that will decrease their energy bill.
While smart meters and, most importantly, the data generated by them (load curves) can act as facilitators to achieve higher levels of energy efficiency, developing an intelligence layer converts energy metering into easily understandable terms is of critical importance. Raw meter data can be a complex undertaking, and data analytics services can help interpret this data to the end consumers, so that they can begin to make smarter choices with this valuable information.
Artificial Intelligence and Machine Learning based algorithms allow a greater insight into the energy use patterns of consumers and provide them opportunities to adjust their energy usage to become more energy efficient.
With a better understanding of end users, it is possible to pinpoint how they can save on their energy bill, recommend them efficient technologies or alert them when changes in the weather are about to occur to prevent spikes in consumption. What is more interesting is that this can all be done in an automated way in a seamless way for the consumer, so a single algorithm can be widely deployed into various end clients and have a significant impact in the energy value-chain.
These new generation of AI-powered services truly offer the opportunities to a triple win situation, where benefits are generated for the consumer, for the utility and for the planet as a whole.
For the household owners, the fact that they may receive, by their energy utility, information such as:
- targeted information about what are the specific measures they should adopt to reduce their energy bill,
- information of how much are they spending on which electrical appliances (Load disaggregation),
- what is the optimal energy tariff and contracted power levels,
- what are the optimally dimensioned distributed renewable energy solutions they should adopt,
creates a new engagement opportunity as well as an increase of the trust levels they may have (or not, as it is commonly the case) with their energy provider.
For the utility, the capability of providing a better service to its clients, helping them in a proactive way to reduce their energy bill and become more energy efficiency, will increase consumer satisfactions and thus reduce churn rates. Additionally, the added knowledge from data analytics will allow utilities to diversify revenue streams by cross-selling new services and products such as:
- solar PV and storage solutions,
- energy management devices and services,
- smart thermostats, substituting obsolete of inefficient appliances,
that will in fact help their clients to become more energy efficient.
Ultimately, the last, but truly first “win”, goes for the planet as a whole as higher levels of energy efficiency will be achieved by household owners, enabled by AI-powered data analytics and contribute towards a sustained reduction of GHG emissions.
We are currently already witnessing some of these solutions and we’ll start to see them even more, as smart metering data becomes more accessible to end consumers in all markets. While it is necessary to ensure that the required privacy and ethical conditions are guaranteed, the availability of such data for end-users coupled with AI-powered data analytics modules will create benefits for the whole energy value-chain and will transform the way consumers (and prosumers) interact and relate with their utilities.