Increasing the share of renewables is crucial for accelerating the sustainable transitions of modern building and district heating systems. This study develops a hybrid co-simulation framework, integrating a Python-based model with an established district energy system (DES) TRNSYS model, to optimize the design and control of on-site renewables such as photovoltaic panels (PV), solar thermal collectors, a water-to-water heat pump, seasonal thermal storage, a domestic hot water tank, and auxiliary heaters. The methodology combines diverse simulation tools and data-driven control sequences, enabling interaction across system components for enhanced energy efficiency and performance. The findings indicate that the optimized framework reduces net present cost by approximately 14 % and environmental impacts by 11 %. The data-driven controls further minimized temperature deviations significantly better than traditional Rule-Based Controls, achieving nearly optimal comfort levels with minimal environmental impact. The developed co-simulation enhances energy efficiency and intelligent controls in building applications, minimizes environmental impacts, and effectively covers the energy demand in building and districts (building clusters). These findings highlight the essential role of advanced hybrid co-simulation frameworks in improving DH system design and control, emphasizing their potential for sustainable urban energy transitions.

This research addresses the critical challenge of digital integration and exchange of data and information from the building side towards 4-5th generation district heating and cooling (4-5GDHC) systems, where heterogeneous data and information from distributed components hinders integration and deployment of data-driven services at scale. The study conducts a critical evaluation of six major ontologies (Brick Schema, RealEstateCore, Project Haystack, SAREF, Flow Systems Ontology, and ASHRAE Standard 223P) and performs semantic modeling experiments on key building-side components including buildings in thermal networks, thermal energy storages, heat pumps, photovoltaic-thermal systems, and waste heat recovery systems. The analysis reveals significant gaps in current ontologies for representing district-level interactions, bidirectional energy flows, and thermal storage dynamics. While existing frameworks effectively model basic building components and sensors, they lack DHC-specific terminology and cannot adequately represent prosumer relationships or complex system topologies. The paper positions ontology-based semantic models as one layer of a broader digital information infrastructure and explores how they can interface with large language models (LLMs) to streamline information interaction across building and district energy systems. This work contributes to three key advances: a comprehensive critical evaluation of existing ontologies for DHC applications, practical semantic modeling experiments demonstrating real-world applicability and limitations, and forward-looking integration frameworks combining knowledge graphs with LLMs and design metadata. The findings highlight the need for DHC-specific ontology extensions and multi-ontology integration to address the unique challenges of 4-5GDHC systems. By bridging semantic technologies and AI. 

Climate crisis mitigation roadmaps, policies and directives have increasingly declared that a key element for the facilitation of sustainable urban development is on-site decentralized renewable energy generation. A technology with enhanced capabilities, able of promoting the integration of renewable energy into buildings, for energy independent and resilient communities, is Photovoltaic Thermal (PVT) systems. Ongoing research has potential yet displays a lack in unified methodology. This limits its influence on future decision-making in building and city planning levels. In this investigation, the often overlooked air-based PVT technology is put on the spotlight and their suitability for integration with energy systems of buildings is assessed. The aim of this study is to highlight vital performance and integration roadblocks in PVT research and offer suggestions for overcoming them. The methodology of reviewed literature is examined in detail with the goal of contributing to a unified approach for more impactful research.

The roadmap for urban sustainability involves the transition to reliable and decarbonised energy networks. In this regard, business concepts based on sector coupling through the use of Thermal Energy Storage (TES) systems can play a key role. This research is placed in this context, with the aim of evaluating the flexibility potential of novel TES in order to provide load shifting services to the electricity grid and improve the renewables penetration. The idea involves the modelling of the TES upscaling scenarios on the national territory and the simulation of energy demand starting from real data on the electricity grid from European TSOs. For this purpose, a Model Predictive Control Framework (MPC) is developed and implemented in Python environment and the results for the case study of Italy are presented. Starting from the time-series data of energy production and consumption at national level, the actual fraction of electricity used for heating and cooling is calculated and the potential of using short-term and mid-term thermal energy storage for minimizing the surplus from renewable energy sources (RES) in the grid is evaluated. As a result, alternative hourly load profiles based on load shifting are proposed and the flexibility potential and sustainability impact of such systems is discussed. The findings show a reduction of 57 % per year of the RES surplus with values close to 100 % during winter days under the considered thermal energy storage capacity scenario. In addition, at least 10 % load shifting potential is achieved. The research provides a contribution to the demonstration and optimization of sector coupling concepts and discusses future outlooks and directions. Lessons learned can constitute insights for policy makers and technology providers, boosting research and diffusion of thermal energy storage technologies as an alternative to batteries and hydrogen systems for unlocking the flexibility potential of electric grids.

The efficiency and lifetime of Photovoltaic cells degrade with elevated temperature levels over time. Cooling the cells contributes positively to their performance and their lifespan. Heat transfer enhancement techniques using thermal inserts, such as baffles, have been investigated widely within Solar Air Heater research. However, these strategies have not yet been applied to Photovoltaic Thermal technology for such cooling purposes, despite their potential benefits. In this study, V-shaped baffles inspired from Solar Air Heaters are evaluated in the context of Air-Based Photovoltaic Thermal for the first time. A prototype was experimentally tested to validate a Computational Fluid Dynamics model. To further improve the thermohydraulic performance of baffles, a novel design was developed, that of smooth V-baffles. In general, a decrease of 8 C° on average was achieved by the cooling baffles. The new design exhibited a higher Thermal Enhancement Factor than that of the straight edge equivalents, up to 22% higher. Additionally, it was indicated that the use of baffles can be beneficial for Photovoltaic Thermal systems, by achieving a more uniform temperature distribution of the photovoltaic cells, up to 47%. This minimizes the formation of hot zones along the photovoltaic surface.

Demand side management (DSM) is a strategy for district heating (DH) networks to reduce peak demand and energy costs. Traditional DSM methods apply fixed temperature reductions, assuming uniform occupant tolerance, which can limit effectiveness or cause discomfort. This paper presents findings from a longitudinal field study (2023–2024 heating season, Stockholm, Sweden) evaluating a personalized DSM approach. Using the ComfortID mobile application, approximately 70 users could accept, or abort DSM events based on individual thermal comfort preference. The results showed that about 25% of events were cancelled. Accepted events averaged a 0.8-K reduction over 22 hours and 54 minutes; cancelled events showed a 1.1-°C reduction over 76 minutes. Additionally, the study found that participants’ thermal sensations significantly deviated from the ISO 7730 standard, highlighting the limitations of generic models. Incorporating personalized thermal models doubled occupant flexibility for DSM compared to a population-based approach. The results demonstrate that integrating personalization into DSM programs can enhance flexibility and energy savings up to 28% without compromising occupant's comfort.

Recently, phase change material (PCM) has been seen as a promising thermal energy storage (TES) technology for providing energy storage and operation flexibility in buildings. Despite its various applications, there has been a lack of real-time tracking capability of the PCM performance in real-deployed systems due to its complex physics. PCM state of charge (SoC) is a key indicator required for quantifying the remaining energy at any operation condition. Since SoC is not a direct measurement, there is a need for highly accurate prediction models. In this article, we propose solving this challenge by employing sparse identification of nonlinear dynamics (SINDy) to unlock the nonlinear dynamic complexity of PCM-TES. The minor utilization of temperature sensors in real-life applications is mitigated by using an extended Kalman filter (EKF) estimator that tunes, in real-time, any faced model inaccuracy. This framework will make it possible to provide highly accurate estimations for the spatial PCM temperatures based on limited noisy measurements. The proposed approach was successfully applied to experimental data recorded from the operation of a prototypical PCM storage for Domestic Hot water generation. The results show how efficient the proposed EKF-SINDy is in SoC estimation compared to the measurement-only approach.

The increasing integration of renewable energy sources (RES) and the transition towards a decarbonized energy sector present significant challenges, particularly in demand-side management. Thermal energy storage (TES) systems offer a cost-effective solution for enhancing energy flexibility in building heating systems. However, improper sizing and operation of TES systems can lead to increased investment costs and energy losses. To bridge this gap, this study proposes a novel, optimization-based framework for the systematic sizing and operation of TES systems. The methodology encompasses two key components: (1) an innovative TES sizing framework that integrates system modelling and optimization-based sizing leveraging historical thermal load data; (2) validation and performance evaluation of the sizing outputs through building energy simulations across three diverse building types and climatic conditions. Key findings demonstrate the framework's ability to adapt to various scenarios, achieving operational cost reductions of up to 35 % and significantly enhancing the energy flexibility in terms of flexibility factor by up to 1.03. Furthermore, the proposed framework is shown to effectively optimize TES capacities to unique building load patterns. These results highlight the framework's potential as a robust tool for optimizing TES in buildings, contributing to flexible and cost-efficient energy systems.

Air-handling units (AHUs) have become indispensable parts of heating, ventilation, and air conditioning (HVAC) systems. AHUs are also significant energy consumers due to the function of their several actuators. Many recent works focus on improving the control techniques of AHUs to provide better indoor comfort with lower energy consumption. However, due to its inherent structure, it is complex to design an optimal and adaptive control for AHU that fulfills this mission in all operation conditions. Model predictive control (MPC), in this context, has been in focus in many contributions recently. However, designing a multi-input multi-output (MIMO) MPC for AHU optimal control is not a trivial task due to the difficulty of having a high-fidelity mathematical model. This study proposes and validates a data-driven nonlinear MPC with MIMO architecture. The proposed MPC is based on the sparse nonlinear dynamic of AHU built upon operation data of a real AHU installed in the KTH live-in lab. In contrast to the classical approaches, the proposed MPC adjusts simultaneously five different actuators to control the supply temperature. This article presents a simulation study for the performance of the proposed MPC framework under different control configurations.

With the rapid development of information technology, energy consumption in data centers has become increasingly prominent. As a core component, cooling systems account for substantial energy use while offering significant energy-saving potential, making them crucial for energy efficiency optimization. To address energy conservation in cooling systems, a free cooling system integrated with cold thermal energy storage is investigated in this study. Using typical meteorological parameters of Wuhan as a case study, a genetic algorithm (GA)-based model predictive control (MPC) strategy is employed to optimize system performance, and its adaptability across different climatic zones in China is evaluated. The results demonstrate that optimizing with power usage effectiveness (PUE) minimization as the objective function reduces the PUE value by 0.018 compared to the baseline system. When applied nationwide, lower PUE values are observed in regions with more abundant free cooling resources. After MPC optimization, the most significant improvements are exhibited in the mild climate zone, where a maximum PUE reduction of 0.0185 is achieved compared to pre-optimized systems.