# Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems--A Review
## 5 Conclusions  
With the growth of the world's population and an increase in urbanization, district heating and cooling (DHC) has been identified as a promising technology to cover the thermal energy demands in urban areas. Notwithstanding, DHC is evolving towards lower distribution temperatures, the exploitation of decentralized and non-programmable renewable sources and sector-coupling with other energy carriers. However, the lack of optimal control strategies and fault detection tools leads DHC systems to waste energy and resources. Moreover, new challenges such as demand-side management, weather uncertainty and environmental efficiency have emerged. Those have attracted more and more attention to the design of intelligent, robust control platforms as well as diagnostic methods. These advanced solutions, reviewed in this paper with a focus on low-temperature DH studies and EU research projects, must be able to predict events, make real-time decisions and be integrated with SCADA systems in order to push the DHC sector forward in the digital transition. As far as advanced control in DHC is conceived, most of the applications follow purposes such as decreasing fossil fuel consumption, energy-savings maximization, emissions and operating costs reduction. Two main different approaches have been identified that are model predictive control (MPC) and multi-agent systems (MAS) control. The latter has been applied in very few cases but has the potential to reduce the need for extensive data manipulation and enhance the openness of the system to third parts and peer-to-peer markets, but there is still a lack in the standardization of communication and development. MPC has a wider application than MAS and can be considered a cutting-edge technology to improve DHC operation. On one hand, it has the main ability to consider weather forecasts, occupancy profiles, together with stochastic disturbances taking into account system constraints in real-time optimization. On the other hand, it can be time-consuming for the identification of the system model and require high-skilled personnel for development and use. Among the hard control implementation of MPC, mixed-integer linear programming (MILP) is one of the most used thanks to some features such as rigorousness, flexibility, finding the exact optimal solution and extensive modelling capability. With the advance ment in swarm intelligence and machine learning, some soft and hybrid forms of MPC have also appeared in DHC applications. In particular, gradient-free meta-heuristic optimization methods, such as genetic algorithms (GA) and particle swarm optimization (PSO), are becoming more and more popular, since they are capable of quick convergence, flexible control parameters and do not need complex mathematical operations. However, dropping into local optimum, stagnation after some initial stages and reducing the efficiency with increasing dimensions are the principal disadvantages of these methods. Similarly, as in predictive control strategies, in fault detection and diagnosis (FDD), two main approaches emerged: physical-based versus data-driven modelling. Models here are useful in software-based solutions to detect malfunctions from the deviation of the system from standard operation. Alarms can be activated when some calibrated thresholds are exceeded. The use of machine-learning-based FDD tools is growing in DHC, since they can handle complex systems with a large number of variables and have excellent performance in the behavior prediction of non-linear systems and in pattern recognition. However, these solutions have some drawbacks, since large datasets are needed for training over different operating conditions to avoid low performance in the extrapolation in real-time operation. Among hardware-based FDD approaches, infrared thermography found applications for leak detections in DHC pipeline and can be further boosted by means of automatic image recognition algorithms.