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The evolution of district heating and cooling systems into sophisticated energy networks is essential for global decarbonization. However, a fundamental tension exists between rapid innovation in advanced control algorithms and the slow replacement cycle of physical infrastructure, making intelligent system-wide control the primary enabler of network modernization. Despite this critical role, the existing literature remains fragmented and lacks a comprehensive synthesis of control strategies across the production, distribution, and end-user levels. This analysis confirmed a definitive shift from isolated, single-level control to holistic frameworks that unlock system-wide flexibility. This review establishes that successful implementation requires addressing distinct objectives at each operational level, from multisource management in production to occupant-centric control at the end-user level. A critical finding is the credibility gap between the demonstrated potential of advanced control and its limited practical application. This disparity is rooted in systemic challenges, including intensive modeling requirements, computational scalability limits, and unresolved human-in-the-loop problems. To bridge this gap, this review presents a structured framework that synthesizes the current state of district heating and cooling control and proposes a forward-looking research roadmap. This roadmap prioritizes the development of hybrid intelligent controllers that integrate learning-based methods with model predictive control, creating persistent digital twins for human-centric applications, and designing secure and decentralized coordination architectures for next-generation thermal networks.

The increasing integration of distributed energy resources in residential buildings necessitates advanced energy management systems for optimal operation. This paper compares a fuzzy logic controller against a rule-based controller for managing energy flows within a prosumer building. The fuzzy logic controller’s adaptability to fluctuating conditions offers potential advantages over the rigid structure of rule-based controllers. The comparison focuses on key performance indicators, including operating costs, self-sufficiency, battery cycle life, and grid interaction. Results highlight the trade-offs between economic performance and battery health, providing insights into the strengths and weaknesses of each control strategy for sustainable and cost-effective energy management. 

Installing solar photovoltaic panels on building rooftops can help property managers generate renewable energy and reduce electricity costs. However, the existence of multiple efficiency indicators and ambiguity in interpreting these metrics limits the comparison of the performance of individual installation projects. This paper presents a methodology using data envelopment analysis to evaluate suitable candidates for rooftop solar panel installation. This approach integrates rooftop area, solar irradiation, temperature, costs, energy yield, and revenue to evaluate the relative efficiency of each building. To demonstrate the methodology, it was applied to rank 22 residential buildings, revealing the top performers for installation in 2022. The approach was subsequently adapted to assess potential outcomes under deferred implementation up to 2030, encompassing a diverse range of climate and pricing scenarios. Five installations were found to be optimal irrespective of the future scenarios. In addition, a super-efficiency approach was applied to overcome the low level of discrimination among the possible installations and to rank each individual unit uniquely. The analysis is designed to guide property owners in identifying favorable solar photovoltaic investments within their portfolios under changing conditions. 

Traditional building automation controllersarehaving low performanceindealing withnon-linearphenomena.In recent years, model predictive control(MPC)has become a notable control algorithmforbuilding automation systemcapable ofhandlingnon-linearprocesses.Performanceof model-basedcontrollers,such as MPC,is depending onreasonablyaccurate process models.For abuildingusingbaseboardradiator heater,anon-linear model isa morereliablerepresentation ofheat distributionsystem.Therefore,thisstudyaims topresentanon-lineargray-box modelfor a residential building connected to the local districtheating networkthatis equipped withradiatorheatemitters.The model is supposed to forecasttheindoorairtemperatureas well as theradiator secondary returntemperature.The modelis validated usingmeasurements collected from a building in Västerås,Sweden.In addition toa betteraccuracy, anothermotivation behind using anon-linearheating circuitmodelis toenhanceits generalizationperformance.With the added benefits ofaccuracy and generalization,this modelis expected toextendpractical MPCimplementation for such buildings