Demand for digital energy optimization on the rise.

Electricity prices are exorbitantly higher than in the previous year. Currently, the price of electricity on the Swiss market is up to 100 centimes per kilowatt hour (kWh). In 2021, the price was still an average of 6 centimes. The price has therefore increased more than tenfold, and the situation is no different on the European electricity markets.

Things are getting bleak around electricity: Prices for electricity have risen sharply on the free market, putting energy-intensive companies in particular in a bind.


The price of electricity traded on derivatives markets has risen particularly sharply. Since the war in Ukraine and the Nord Stream 1 gas curtailment, the price of electricity on derivatives markets has risen immensely. While it cost around 200 euros per kilowatt hour in Switzerland, Germany and France last year, the price has risen to between 1,200 and 1,900 euros by the end of the winter. In economic circles, this is interpreted as a signal of scarcity. The increase in price reflects fears of a shortage among electricity consumers.

The high prices are there to prevent the deficit of electricity, to prevent electricity rationing, in which there would be power cuts in large parts of Switzerland. It is of immense importance for the coming winter to avoid differently distributed overloads.

Market participants fear that there will no longer be enough electricity to meet all demand towards the end of the winter. To prevent this, consumers should take advantage of the financial benefit that can be achieved through targeted electricity savings.


An environmentally friendly and energy-independent solution for the future is offered by the company Lynus with its machine learning software. The solution from Lynus is used in private households as well as in large companies so that electricity and gas are used more efficiently and economically. It networks all the building’s energy generators and consumers, analyzes the building’s and devices’ consumption, issues forecasts for the future, and then optimizes them using machine learning.

It is clear that in the future there will have to be a rethink on the subject of electricity and gas use. One possibility would be to become more independent of the big electricity and gas players by means of smart home technologies. Another possibility is to reduce one’s own consumption and use less electricity and gas on one’s own initiative. The optimal path probably lies in a balanced combination of these two options.