# Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems--A Review
## 4 Discussion
### Overview of Commercial Platforms Capabilities Available in the Market  
Conversely from HVAC systems in buildings, in DHC applications, there are more chances to find a high-level supervisory control and data acquisition (SCADA) infrastructure in place that allows taking advantage of innovative platforms available in the market for load forecasting, operational planning and anomalies detection. Even if it is out of the scope of this work to perform a detailed technical review of the platforms available in the market, the authors performed a high-level analysis based on a few publicly available information gathered and without pretending to be exhaustive. The capabilities analyzed and the commercial platforms surveyed have been listed in Table 12 of Appendix B. It has been considered the possibility for the exploitation of a physical thermal-hydraulic model of the DH network for design purposes, the possibility to run an advanced optimization algorithm for optimal production scheduling, supply temperature optimization, local weather forecast, DH load forecast, demand response operation and fault detection capabilities. Only for a few platforms has some detailed information been found. Termis Engineering by Schneider Electric [142] seems to be one of the most complete and can be applied to both DH and DC systems. It has also dedicated modules for supply temperature, pressures, pumping and TES optimization. The physical model of the DH network in Termis is based on the quasi-dynamic assumption with a static model for the evaluation of pressures and flows, and a fully dynamic temperature assessment [143]. It can be used offline for scenario analysis, but it can also run online predicting the impact of some control inputs in the DH system in real-time applications. Dantoss after the acquisition of the companies OE3i [144] and Leanheat [145] is capable of providing an advanced modular-based software solution covering almost all the capabilities investigated. * Overall, from the analysis performed on main European players it emerges that:
* Several platforms are conceived in hybrid solution by implementing a digital twin of the network based on physical models but also exploiting artificial intelligence algorithms (Danfoss, DCbrain, Gradyent). * More and more platforms are using data-driven machine learning models for load forecasting and in some cases also at the building level (NODA, Danfoss). * Termis, Danfoss and Gradyent have the capability to use the thermo-hydraulic model of the network (in some cases simplified) on-line as an operational support tool calculating optimal hydraulic parameters of the DH network (temperatures, flows and pressures) according to the forecasted boundary conditions. * Some companies, even if they have developed thermo-hydraulic models for DH design and planning, they do not integrate them with their optimal dispatching tools. * Stochastic optimization is handled by the dispatch optimization engines provided by Artelys and ENFOR. * In [146], the developers of OptiEPM highlighted their choice in implementing the Matheuristics algorithms to achieve a better performance than a direct MILP approach from the computational time point of view that can be relevant in complex problems with a large number of control variables. * Almost all DH production optimization tools surveyed are used to solve the unit commitment problem considering both long-term and short-term planning of the generation plants and considering the participation to the electricity markets (e.g., with CHP units). * Few companies such as NODA and Danfoss focus on the demand side of the DH system for the implementation of demand-side management solutions for peak shaving. * Some companies such as DCbrain, NODA, Danfoss and Gradyent apply artificial intelligence algorithms to provide predictive or condition-based maintenance and fault detection services. * Termis Temperature Optimization is capable to transform the non-linear dynamic optimization problem in a linear programming one and to solve it [147].