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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.

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.

District Heating Networks have evolved in recent years, moving from transporting high-temperature heat produced in Cogeneration Heat and Power Plants, to nowadays using heat from low-temperature renewable heat sources, driving the sustainable transition in the heating sector. The percentage of heat transported by district heating networks varies widely across Europe, from 1% in Ireland to 64% in Denmark. GIS-based algorithms have emerged as design and decision tools to identify renewable heat sources, heat users, and to determine the district heating networks size to connect them. Most of the current methods assume a constant installation cost for the entire network (Single Cost Area). However, this may not hold true for transmission lines crossing Multiple Cost Areas, such as cities, countryside, rivers, and natural reserves, which have significantly different installation costs. This paper presents a GIS-based methodology developed in Python to adapt the existing methods to lines crossing Multiple Cost Areas. The system's profitability is evaluated in terms of Total Investment Cost, Levelized Cost of Heat, and the Maximum Profitable Distance. The result is that using a constant average cost for a Multiple Cost Area region can lead to an error of 11-71 % on the estimation of the maximum profitable distance. To summarize the results a newly developed concept, the Profitability Map, is introduced to highlight the regions where is profitable to transport the heat. Two case studies (100 MW waste heat, 5 MW low temperature geothermal) are presented to demonstrate the use of Profitability Maps, highlighting the advantages of the new methodology as a decision-support tool. 

Construction projects, especially those for commercial purposes, require thorough planning and control to ensure success within predetermined budgets and timelines. This research, conducted in Mashhad, Iran, employs the analytic hierarchy process (AHP) and VIKOR methods to identify and rank factors influencing delays in high-rise projects. The study, based on a sample of 40 projects, emphasizes the comprehensive nature of our research method. The scale for features in project selection includes societal importance (with different applications including cultural hubs, affordable housing initiatives, and urban renewal for social equity), size (less and more than 20 units in residential projects), and diversity (mixed-use development, inclusive infrastructure, and cultural and recreational spaces), contributing to a comprehensive analysis of construction delays. Expert project managers and engineers provided insights through two questionnaires, and their responses underwent thorough analysis. Our findings not only underscore the significance of factors contributing to project success but also rank their impact on the likelihood of delays. The study reveals that the negative effects of these factors on cost, time, and project quality vary. Time emerges as the most influential parameter, with approximately six times more impact on cost and nine times more on quality. Contractor financial weakness, delays in allocating financial and credit resources, insufficient project resource allocation, contractor technical and executive weakness, and a lack of proper implementation and project control are identified as the most important factors contributing to delays.

Towards resilient urban energy systems, prosumers play a crucial role in shaping sustainable energy practices. This study investigates how photovoltaic (PV) systems as the most common energy flexibility measure in context of prosumers can enhance self-sufficiency and contribute to a resilient energy future.This study introduces an integrated modeling approach to evaluate a simplified techno-enviro-economic assessment of photovoltaic (PV) system integration at different types of prosumers by residential buildings (both single and multifamily house), and industrial prosumers, each characterized by distinct load profiles. TRNSYS employed for electricity production modelling in different scenarios to ensure accuracy and reliability in capturing the dynamic interaction between energy consumption and production. Then model outcomes are used in an analytical assessment to find self-consumption efficiencies. This allows us to evaluate consumption levels and displays the ratio of direct solar energy consumption to total solar production over different seasons. In addition, the model quantifies the amount of electricity purchased from the grid to account for own consumption and helps evaluate electricity dependency It also examines excess electricity a is fed into the power grid, providing a detailed overview of the excess electricity to be generated and the feed conditions. Eventually, an environmental analysis is performed to estimate the total CO2-equivalent savings from PV integration.The main expected result in this research includes but not limited to direct self-consumption, storage, and surplus productions; total savings and incomes; internal rate of return; payback time and total CO2 mitigation for all different scenarios and studied cases. Additionally, the research outcomes in this study aid in enhancing energy resilience. These findings collectively emphasize the versatility and positive impact of PV systems in prosumers and contributing to a sustainable urban energy future.

As modern district heating networks integrate buildings with multiple energy sources, fault detection has become increasingly relevant and critical. This study investigates the effectiveness of an unsupervised data-driven fault detection approach to identify stuck valve and faulty thermostatic radiator valve scenarios in the baseboard radiators of an office building. A baseline model for a typical Swedish office building was developed, featuring a ground-source heat pump, solar photovoltaic-thermal panel, water-based radiators, and a connection to the district heating system to support its heating demand. Multiple fault scenarios were considered in the model, involving partially stuck valves and thermostatic radiator valves that deviated from their intended setpoints. Synthetic noise was added to generate faulty scenarios. The model performed well in detecting severe stuck valve faults but showed lower performance on less severe faults and faulty thermostatic radiator valves. The insights gained from this research emphasize the importance of fault monitoring in the context of evolving buildings connected to district heating networks.

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

This paper outlines the obstacles to sustainable construction growth in Iran and thereafter examines the effect and relation between these barriers and the direction of sustainable construction growth as one of the essential objectives for achieving sustainable cities and infrastructure. The study is applied for research purposes that are based on descriptive survey data gathering and correlational data analysis techniques. The statistical population for this study consists of 120 construction-related engineers and university professors who were assessed on a five-point Likert scale. Using SmartPLS software version 4, the responses to the questionnaire were examined. The Kolmogorov–Smirnov assessment was utilized to evaluate the normalcy of the variables, as this assessment is typically employed for this purpose. For data analysis, the PLS (partial least squares) method was used, while SEM (structural equation modeling) methods have been used to assess the study hypotheses. Cronbach’s alpha and the composite reliability coefficient (CR) were applied to determine the instrument’s viability, and the results show that the coefficient connected to all variables is above 7.0, which is an acceptable value. The AVE (average variance extracted) was also used to evaluate the questionnaire’s validity, which was greater than 0.4 and deemed acceptable for coefficients of significance (T-values), coefficient of predictive power (Q2), and coefficient of determination (R2). The obtained results support and confirm all research hypotheses, including that the identified obstacles directly affect the performance of sustainable construction. According to the results of the Friedman test, the legal restrictions variable (CL) is the most significant obstacle to sustainable construction in Iran, with a rank of 4.24. The indicators of political limits (CP) and social and cultural constraints (CSC) came in at second and third, respectively. The results could help government officials make better decisions about where to focus their attention and how to distribute scarce resources. 

Urbanization has created a substantial pressure on the ecological sys-tem where many functions are already suffering from degradation such as the water ecological cycle. On the other hand, many cities around the globe are try-ing to tackle environmental degradation through innovative and green solutions such as parks, green roofs and others. One of the promising methods in this re-gard is planting city streets and parks with different kinds of trees which could contribute to in first-hand the infiltration of storm water and in second hand in reducing the climate change effect through absorption of atmospheric carbon dioxide. In this investigation, we studied the distribution of trees in Kalmar Stadsparken southeastern Sweden. The data for this study was collected by Kalmar municipality and we analyzed the effect of trees on the water infiltra-tion and carbon oxide absorption using an i-Tree program. The results are promising and emphasis the considerable effect of city trees on the urban areas.