https://www.mdu.se/

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. 

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.

Nosocomial transmission of airborne pathogens remains a persistent threat in multi-bed hospital wards. This study quantifies how personalized exhaust devices, combined with the main two-bed patient room ventilation, can suppress cross-contamination and contributes a configuration-spanning assessment that links personalized exhaust operating set-points to removal efficacy across six layouts, providing design guidance absent from prior single-layout studies. A three-dimensional hospital ward was solved with the Reynolds-Averaged Navier–Stokes (RNG k-ε). At the same time, particle trajectories representing pathogen-laden aerosols were computed by one-way-coupled Discrete Phase Modeling augmented with a Discrete Random Walk stochastic dispersion scheme. Six different ventilation layouts were combined with personalized exhaust flow rates of 0 (off),10, 20, and 40 L/s. Two infection scenarios were simulated: (i) one infectious patient served as the particle source, while a second patient and a healthcare worker were modeled as susceptible targets; (ii) both patients were infectious, with the healthcare worker being the target for exposure. Ventilation geometry strongly governed room airflow and particle transport; without personalized exhaust, inhalation fractions differed by an order of magnitude between layouts. Activating personalized exhaust at 20 L/s reduced inhalation fractions by ≥ 80% in half of the layouts, while 40 L/s achieved complete particle removal in all configurations. In the dual-infection scenario, simultaneous operation of both personalized exhaust units at 20 L/s diminished healthcare-worker exposure by 75%. These results demonstrate that personalized exhaust devices provide robust, configuration-independent mitigation of aerosol transmission and should be considered a complementary strategy to conventional ward ventilation. 

Valve and control faults in building-level radiator systems degrade the efficiency of district heating networks and cause occupant discomfort. Although methods for fault detection and compensation exist independently, integrated systems that diagnose and react to specific faults are a recognized research gap. This study presents an integrated fault detection and compensation framework implemented and evaluated within the EnergyPlus simulation environment. The system uses a machine-learning-informed proxy to trigger fault-specific supervisory control actions. A comparative analysis of control-based (stuck-open) versus physically constrained (stuck-closed) faults through an ablation study revealed fundamentally different operational behaviors. For the stuck-open fault, the fault detection and compensation system was highly effective, reducing the discomfort degree-hours by 68% and the energy consumption by 23%. For the stuck-closed fault, the system's inability to restore comfort confirmed its role as a high-fidelity diagnostic tool, proving that such physical faults require maintenance rather than supervisory control. These findings establish a framework for fault-specific responses in district heating-connected buildings.

Model predictive control enhances building energy performance; however, its reliability is highly dependent on the robustness of internal prediction models under severe operating conditions. To address this, a risk-aware model-then-control (RAMC) training framework is proposed in this study. This approach augments conventional prediction loss with a conditional value-at-risk (CVaR) penalty on operational costs under perturbed inputs, embedding tail-risk awareness directly into the prediction model. The framework is trained via standard backpropagation, avoiding the computational burden of differentiating through the controller. The proposed methodology is evaluated on a simulated commercial building equipped with a hydronic heating system under three weather scenarios. Compared to a standard fidelity-trained baseline, the strongest risk-aware configuration reduced occupied cold degree-hours by 22-26% and peak cold violations by 14-27%, demonstrating the greatest benefit under forecast bias. These comfort improvements were achieved alongside a 17-31% increase in weekly heating energy consumption. The results indicate that embedding tail-risk awareness into model training improves closed-loop comfort robustness relative to standard accuracy-based training. An ablation study attributes this improvement directly to the CVaR tail term, while the risk weight formalizes a tunable energy-comfort trade-off dictated by operational priorities. In this case study, a fixed setpoint-margin baseline reached comparable cold protection at lower energy; the distinct contribution of RAMC is that it relocates a tunable tail-risk preference into the prediction model itself, leaving the downstream controller unchanged.

To ensure energy efficiency and resilience, building prosumers must maintain energy supply during disruptions while minimizing environmental impact. This study examines the resilience of three solar-applied building prosumers: a single-family home, a multi-family building, and a commercial property in Västerås, Sweden. It evaluates (1) the impact of PV and battery integration and load prioritization on outage resilience, (2) seasonal variations in resilience for individual buildings in high-latitude region, and (3) district-level performance to assess aggregation effects on system resilience. One-day power outages are simulated for each season, with loads classified as critical or non-critical. System performance is measured using load coverage rate, which quantifies the robustness of building systems in withstanding disruptions. The results show that load prioritization significantly enhances resilience, particularly for multi-family buildings during heating season, ensuring occupant habitability and survivability. The role of PV and battery sizing in improving resilience varies by season, with battery capacity playing a key role during winter outages. The benefits of energy sharing at the district level are limited, but can be advantageous for multi-family buildings with a lower PV-to-load ratio. This study provides insights into the role of PV and storage systems as resilience strategies for buildings and districts in cold-climate regions.

A predictive control strategy has been developed for photovoltaic-integrated venetian blind systems to balance indoor visual comfort and solar energy generation. The controller has been formulated as a model predictive control problem with an illuminance constraint of 100–200 lux and a four-step prediction horizon. The results have shown that the model predictive control with four-step prediction horizon has maintained indoor illuminance within the comfort range more effectively than both the single-step and fixed-angle configurations. Moreover, the total photovoltaic energy yield has been slightly higher while actuator oscillations have been significantly reduced. These findings have confirmed that extending the prediction horizon enhances both visual comfort stability and energy efficiency, demonstrating the potential of predictive control in smart, energy-positive façade systems.

Renewable Energy Communities (RECs) are evolving as key enablers of climate action and renewable energy integration. However, they face technical and economic challenges, particularly in energy allocation, pricing structures and stakeholder expertise. Existing studies primarily focus on multi-owner RECs, overlooking single-owner models - increasingly relevant in urban settings. This study analyzes a real-world single-owner REC, comparing hourly- and monthly-based pricing models and four energy allocation strategies using techno-economic metrics. Results show that hourly-based pricing reduces costs but lowers revenues. Furthermore, allocation strategies minimally impact technical performance in such REC setting. While the differences between the strategies themselves are negligible from the landlord’s perspective, the impacts on tenants in terms of cost distribution and fairness could vary, requiring further investigation.

This study conducts a techno-environmental assessment of a fully electrified rural single-family house in Västerås, Sweden, functioning as a resilient, sustainable prosumer. It evaluates: (i) grid independence under normal conditions; (ii) thermal resilience during power outages with various enhancement strategies; and (iii) operational COâ‚‚ emission reductions versus combustion-based heating. Using IDA ICE simulations, the building’s energy performance is analyzed with integrated renewable energy (RE) systems: PV, solar thermal, batteries, thermal storage, electric heater, and heat pump. Key performance indicators include self-sufficiency rate (SSR), operational COâ‚‚ emissions equivalent, and thermal resilience metrics. Results show that combining RE technologies significantly improves year-round reliability and emissions reduction. PV-only systems perform well during long outages, while battery-only setups suffice for short ones. Even basic electric heaters maintain habitability during 3-day outages. The findings offer practical insights into enhancing resilience and sustainability in rural prosumer buildings through distributed RE systems.

Positive energy districts (PEDs) generate morerenewable energy than they consume, but annual netpositivebalances often obscure significant hourly supplydemandmismatches, creating a last 20% challenge. Thispaper addresses this critical challenge by proposing aninteroperable digital platform integrating multi-objectiveoptimization, digital twins, and blockchain P2P trading toalign generation, storage, and demand response on anhourly basis. The optimization employs complementarycentralized and iterative decomposed frameworks, cooptimizinggeneration, storage, and loads. Preliminaryanalyses from seven European pilot sites suggest thattransparent coordination and trusted transactions are essentialfor closing the final shortfall. The analyses suggestsignificant improvements in local self-consumption(up to a 15% increase during peak hours) and a reductionin grid reliance. This offers a scalable roadmapfor grid-connected PEDs aiming for optimal local selfconsumption.