The energy transition is correcting the lack of sustainability in the energy sector. In this article, we show how the energy sector can make a significant contribution to resource conservation thanks to data science.
There are numerous factors that need to be taken into account in power generation. On the one hand, this is due to the complexity of the electricity grid. On the other hand, the system has become even more complex since renewable energy sources have shaped the energy mix in generation. Fluctuations in the power grid are part of the daily routineas certain energy sources are dependent on the weather.
The question that arises in view of the supply network is one of optimisation on the one hand and forecasting quality on the other. Only with a correspondingly reliable knowledge base can the processes be optimally controlled. How these two goals can be achieved with the help of data science will be demonstrated here using two practical use cases.
Visualisation of electricity consumption
The analysis of data creates a new knowledge base and thus leads to better decisions. Also the Automation of processes can be driven forward with it. Visualisations are an ideal means of representing Data analysesbecause graphical representations can provide the most important information at a glance.
Link tip: Are you interested in the power of the visual? Then also read our blog article on the topic Data visualisations.
For energy service providers, it is becoming even more important to understand customers' electricity consumption in detail. In particular, consumption in relation to types of use, such as in schools, hotels and supermarkets, is an important constant in demand. In order to better control production and marketing, the consumption data is enriched with other data on year of construction, floor area and number of storeys.
Optimisation of the load control of power plants
Transparency and knowledge about future developments put decision-makers in a better position. By having data-based forecasts at their disposal, they can prepare for future events and likely demand. Better planning security means both more sustainable use of available resources and improved economic value creation.
It is essential for energy producers to know as precisely as possible about the amount of electricity they need in order to control electricity production as accurately as possible. With a model for the optimisation of load control (Load Forecasting), considerable improvements can be achieved compared to previous methods. In addition, the process can be fully automated and the forecasting quality can be increased at the same time.
Successful energy transition: Data Science combines economy and ecology
The goal of data-driven business models is to generate added value from data. This can be both an economic added value and an ecological added value. The two can go hand in hand: Because if resources are used responsibly in the course of greater sustainability, this also entails an economic benefit.
Link tip: We have shown how these data-driven business models look in practice in our article on "Machine Learning in the energy sector" depicted.
Security of supply is one of the primary goals in energy supply. The success of the energy transition also depends on whether availability is guaranteed in the future. The more renewable energies are used, the better forecasting models must be able to predict the demand and the amount of electricity produced.
In the energy transition, economy and ecology must be balanced. This creates an incentive for all stakeholders to take the necessary measures. Sustainability thus has two dimensions. Both the business models must be sustainable in the age of the energy transition as well as the handling of the available resources. We are therefore convinced that the future of energy supply must be data-based.