Here is one possible energy consumer scenario in 2025.
Claire opens its Smart Energy mobile application and checks the power consumption of here apartment. The application informs that its washing machine is down. Then they will see a notification from their electricity supplier. It offers a discount of 80 euros to buy a more energy efficient model. At the same time, it offers funding that is linked to the amount of their regular payments. Its application offers a choice of 3 possible models of washing machines that are cost-effective in terms of cost-effectiveness. At the same time, another option is offered in connection with the new model of the washing machine - delayed start of the machine, which allows the washing machine to run at a time when the energy costs are low. The app will eventually sum up all the information and counts on saving up to 55 euros a year when buying a new scrubber that Claire can choose from. She knows that her washing machine will have to be replaced in the near future, so she will use Smart Energy and buy a new washing machine by clicking the button.
In 2000, it was the reason for introducing clever measurements to improve device reliability and streamline billing processes. At the same time, the goal of optimal utilization of workforce and loss of network losses was also monitored.
The benefits of those projects would be first used by the energy supplier eventually shared with customers. Many network companies have taken the first steps to more consistently use the data that can be obtained today from sophisticated measurements on infrastructure they operate. For example, Enel estimates that smart meter data will allow them to identify unused or over-used transformers to better plan future investment costs. The result should be savings in capital but also a reduction in the operating costs associated with low voltage distribution management.
Many network industries (electricity, water, gas, ...) are today looking for sophisticated applications to find new value by using data management and machine learning. For example, the data obtained may be used for maintenance purposes or for use by predictive tools to achieve additional improvements in distribution planning or to measure the effectiveness and reliability of capital expenditures.
However most utility companies are looking at a huge amount of data that they are not trusting because they can not use them meaningfully. They are trying to create analytical models that use acquired data and turn them into a format that will help them make decisions and consistently use their information value.
The greatest benefit of data acquisition from smart devices and advanced analytics is their potential in comparing and predicting the use of energy for similar customers. This is calculated using machine learning tools and advanced cluster algorithms for selected customer segment. These tools will then enable price optimization, product change and tariff and energy plans.
Energy companies will no longer use location or age to determine which products they offer to their customers. Rather, they will use individual customer profiles for their proper assignment to the corresponding category, including those focusing on green energy.
Companies will also use customer satisfaction data. This transition to the strongest and analytically stricter segmentation will be necessary to enable businesses to achieve the highest revenue from one customer. For example, companies are developing applications for deeper customer engagement. These applications provide not only an overview of energy consumption. They also propose discounts that can be achieved or tips on reducing energy bills.
Energy companies will also be able to predict consumer energy consumption more effectively as a result of a better understanding of the structure and trends of energy consumption. This will allow them, for example, to reduce purchasing and procurement costs in the energy and derivatives markets. It will also open new ways to offer third-party customer data.
At the same time, some regulatory mechanisms are in place to change energy efficiency returns. For example, rules may be adopted to include expenditures incurred in connection with improvements in energy savings in investments under regulated tariffs. This can further emphasize the role of smart metering devices, as the data they generate conceals contain a high potential value that they can use effectively in the regulation. That's why they will use these data faster and more flexible so they can achieve higher operating efficiency and faster return on invested capital. Most importantly, they will finally be able to offer tailor-made services to customers in an increasingly digitized world.