Resource conservation is the key to sustainable business. Data science makes a significant contribution to this. In this article we show how environmental protection and data science go hand in hand.
Basically, the alliance of data science and environmental protection already starts at the origin of climate research. The knowledge we have about the Earth's climate and its development in the future is the result of data science investigations. The better we understand the correlations in the data about the development of the climate in the past, the better we can predict how it will develop in the future. For this purpose Forecast models from the field of data science who can find correlations and causalities in the data. However, data science can do much more in terms of sustainability and resource conservation.
How data science supports the energy transition
If you think of the smart grid, for example, the importance of data science becomes clear. If renewable energy sources are to be integrated into the supply network, security of supply must be guaranteed. Natural fluctuations must be balanced out as best as possible. This is achieved by Anomalies in power consumption detected at an early stage and automatically regulated.
Link tip: Read also our blog article on the topic of "Machine Learning in the Energy Sector.
Here, too, the best possible knowledge about future developments is the decisive advantage. The better the basis for decision-making, the more sustainable energy production and distribution can be. For a customer from the energy sector, Alexander Thamm GmbH has developed a model to optimise the forecast quality. Sustainability in power generation is hardly conceivable without data science.
Resource conservation at the touch of a button
The following example also shows how diverse the starting points are through which data science can contribute to more sustainability: There is enormous potential for optimisation in the area of logistics and warehouse management. It is true that the share of empty runs in freight transport by truck is slowly decreasing. But according to official goods transport statistics, the share is still 59 per cent, measured in terms of the number of load journeys. Optimisation of coordination is possible, for example, with Artificial neural networks and methods from the field of machine learning. These predict empty journeys in advance and automatically show how the journeys can be used more efficiently.
Dealing with storage space and stock is also a starting point for making the best use of available resources. Stocks not only take up valuable storage space - often old stock is also disposed of after a certain period has expired. Responsible, sustainable use of resources can be achieved by accurately determining demand. With the help of Big Data and predictive analytics, inventories can be optimised and data-based demand forecasts can be used to forecast demand can be predicted.
Intelligent handling of data
Greater sustainability and resource conservation are important fields of action that can make a significant contribution to environmental protection. The argument that environmental protection incurs costs is often used in this context. The measures presented here Use Cases and solution approaches show, however, that a sustainable use of available resources can also go hand in hand with economic benefits for companies.
With data science methods and intelligent handling of data, numerous starting points can be found to identify optimisation potential and to know exactly what the future demand will be thanks to data-based forecasts. In this way, we are laying the foundations for the responsible use of resources. We are convinced that technology and science not only play an important role in researching climate change, but can also contribute to its solution.