In the energy sector, accurate demand forecasts are vital but often limited by the available computational power. Reservoir computing (RC) or echo-state networks excel in chaotic time series prediction, with lower computational requirements compared to other recurrent network based methods like LSTMs. Next-generation reservoir computing (NG-RC) is a newer, more efficient variant of classical RC originating from nonlinear vector autoregression and therefore missing the randomness of classical RC. In our study, we evaluate RC and NG-RC for day-ahead energy demand predictions on four data sets and compare it to LSTMs and a naive persistence approach. We find that NG-RC outperforms all other methods when considering the root mean squared error on all data sets but struggles with very small or zero demands. Additionally, it offers a very computationally effective hyperparameter optimization and excels in replicating the inherent volatility and the erratic behavior of energy demands.

In conventional energy systems, the synchronization of power generation and consumption is largely handled by thermal power plants. The increasing share of volatile renewable energy generation which does not follow the demand, makes it necessary to shift the synchronization tasks to the system and demand level. The intended demand side consumption management is usually incentivized via financial mechanisms. However, the optimum design of related incentives is still unclear. It is usually discussed under the term "system friendliness" which in turn still lacks a broadly accepted definition. In this article we introduce a new technical definition of "system friendliness" and develop comprehensive and holistic related indicators. Our findings demonstrate that today's regulatory environments that usually support residential self-consumption maximization are counterproductive. Instead, we show that dynamic end-user prices coupled with dynamic feed in tariffs are able to incentivize an almost 100% system friendly behavior of distributed prosumers.