Summary Both district heating and solar collector systems have been known and implemented for many years. However, the combination of the two, with solar collectors supplying heat to the district heating network, is relatively new, and no comprehensive review of scientific publications on this topic could be found. Thus, this paper summarizes the literature available on solar district heating and presents the state of the art and real experiences in this field. Given the lack of a generally accepted convention on the classification of solar district heating systems, this paper distinguishes centralized and decentralized solar district heating as well as block heating. For the different technologies, the paper describes commonly adopted control strategies, system configurations, types of installation, and integration. Real‐world examples are also given to provide a more detailed insight into how solar thermal technology can be integrated with district heating. Solar thermal technology combined with thermally driven chillers to provide cooling for cooling networks is also included in this paper. In order for a technology to spread successfully, not only technical but also economic issues need to be tackled. Hence, the paper identifies and describes different types of ownership and financing schemes currently used in this field.

Hardware implementation of artificial neural networks (ANNs) allows exploiting the inherent parallelism of these systems. Nevertheless, they require a large amount of resources in terms of area and power dissipation. Recently, Reservoir Computing (RC) has arisen as a strategic technique to design recurrent neural networks (RNNs) with simple learning capabilities. In this work, we show a new approach to implement RC systems with digital gates. The proposed method is based on the use of probabilistic computing concepts to reduce the hardware required to implement different arithmetic operations. The result is the development of a highly functional system with low hardware resources. The presented methodology is applied to chaotic time-series forecasting.

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