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A deep energy retrofit of building envelopes is a vital strategy to reduce final energy use in existing buildings towards their net-zero emissions performance. Building energy modeling is a reliable technique that provides a pathway to analyze and optimize various energy-efficient building envelope measures. However, conventional optimization analyses are time-consuming and computationally expensive, especially for complex buildings and many optimization parameters. Therefore, this paper proposed a novel optimization algorithm, MEVO (metamodel-based evolutionary optimizer), developed to efficiently identify optimal retrofit solutions for building envelopes while minimizing the need for extensive simulations. The key innovation of MEVO lies in its integration of evolutionary techniques with design-of-computer experiments, machine learning, and metaheuristic optimization. This approach continuously refined a machine learning model through metaheuristic optimization, crossover, and mutation operations. Comparative assessments were conducted against four alternative metaheuristic algorithms and Bayesian optimization, demonstrating MEVO's effectiveness in reliably finding the best solution within a reduced computation time. A hypothesis test revealed that the proposed algorithm is significantly better than Bayesian optimization in finding the best cost values. Regarding computation time, the proposed algorithm is 4–7 times faster than the particle swarm optimization algorithm and has a similar computational speed as Bayesian Optimization. [ABSTRACT FROM AUTHOR]

Solar energy is one of the most promising forms of alternative energy because it has no adverse effects on the environment and is entirely free. Converting solar energy into thermal energy is the most common and straightforward method; the efficiency of solar thermal conversion is approximately 70 percent. The intermittent nature of solar energy availability affects the performance of solar water heaters (SWH), which lowers the usefulness of solar energy in residential and commercial settings, particularly for water heating. Even at low temperatures, the performance of a collector can be improved by using low-density polyethylene (LDPE) glazing instead of traditional glass because it is less expensive and lighter than glass. Using a comprehensive experimental-simulative study, the Glass Solar water heater (glass SWH) and the low-density polyethylene solar water heater (LDPE SWH) are analyzed, examined, and compared in this work. These solar water heaters have galvanized iron (GI) as their absorber material. The SWHs were operated in a closed loop at a constant mass flow rate of 0.013 kg/s, and a 4E analysis (which stands for energy, exergy, economics, and efficiency recovery ratio) was carried out. This analysis included a look at the dynamic time, uncertainty, weight reduction, carbon footprint, and series connection. An LDPE SWH has an energy efficiency that is 5.57% and an exergy efficiency that is 3.2% higher than a glass SWH. The weight of the LDPE SWH is 32.56% lower than that of the glass SWH. Compared to the price of a conventional geyser, installing our SWH results in a cost savings of 40.9%, and monthly energy costs are reduced by an average of 25.5%. Compared to October, September has the quickest dynamic time to reach the desired temperature, while October has the most significant dynamic time. The efficiency recovery ratio (ERR) of a glass SWH is 0.0239% lower than that of an LDPE SWH. LDPE SWHs had a carbon credit worth INR 294.44 more than glass SWHs. The findings of these tests demonstrate that the LDPE SWH is a practical replacement for traditional means of heating water, such as SWHs and geysers. [ABSTRACT FROM AUTHOR]

Standard building practices commonly use gypsum-based drywall panels on the interior wall and ceiling applications as a partition to protect the components of a wall assembly from moisture and fire to uphold the building code and ensure safety standards. Unfortunately, gypsum-based drywall panels have poor resistance to water and are susceptible to mold growth in humid climates. Furthermore, the accumulation of drywall in landfills can result in toxic leachate impacting the surrounding environment. A proposed solution to the pitfalls of gypsum-based drywall arises in its substitution with a new lightweight composite honeycomb sandwich panel. This study aimed to develop sandwich panels with improvements in flexural strength and thermal insulating properties through the combined use of cementitious binder mix and kraft-paper honeycomb structures. The proposed alternative is created by following standard practices outlined in ASTM C305 to create cement panels and experimenting with admixtures to improve the material performance in order to cater to a drywall panel application. The kraft-paper honeycomb structure is bonded to cured cementitious panels to create a composite "sandwich panel" assembly. The results indicate that the sample flexural strength performed well after 7 days and exhibited superior flexural strength at 28 days, while providing a substantial increase in R-value of 5.84 m2K/W when compared to gypsum-based panels, with an R-value of 5.41 m2K/W. In addition, the reinforced kraft-paper honeycomb with a thick core and addition of flax fibres to the cementitious boards possesses better thermal conductivity, with a reduction of 42%, a lower density, and a lower water vapour transmission in comparison to the thin kraft-paper honeycomb sandwich panel. [ABSTRACT FROM AUTHOR]

Hydronic radiant floor systems enhanced with phase change materials (PCMs) could achieve significant energy savings while improving the thermal comfort of occupants in lightweight buildings. However, successful integration of PCMs typically requires comprehensive numerical analysis due to their complex nature. This study aims to investigate two scenarios for optimal integration of PCM into the hydronic floor heating system of a highly glazed study room exposed to cold weather conditions. Scenario 1 includes optimization of two design variables, including PCM melting temperature and thickness. Scenario 2 encompasses optimization of seven design variables, including PCM melting temperature and thickness, insulation thickness and thermal conductivity, floor thickness, thermal conductivity, and solar absorbance. Both scenarios are optimized for the six supply water temperatures, ranging from 35.6 °C to 45.6 °C with a 2 °C step. The overall findings suggest that successful integration of PCMs into the hydronic heating system requires a comprehensive solution tailored for the specific application. Thus, scenario 1 and scenario 2 achieved the highest total energy savings of approximately 17.7% and 20.5% for the lowest supply water temperature of 35.6 °C, whereas under both scenarios the supply water temperature of 43.6 °C provided the best thermal comfort. Furthermore, scenario 1 achieved more substantial cooling energy savings over the wider temperature range (41.6-35.6 °C) compared to scenario 2 (39.6-35.6 °C). The findings also suggest that the addition of the insulation layer in the second scenario reduced the thickness of PCM and payback period for more than 70% compared to the first scenario. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) is a recent development that uses modelling and simulation with forecast conditions to exploit building thermal mass in an effort to improve indoor thermal comfort and reduce energy use/cost compared with traditional rule-based control strategies. This paper investigates a range of recent MPC strategies and the direction in which this new technology is trending. The findings indicate that there is little evidence that directly compares the performance of specific optimization algorithms, forecast and simulation parameters (timestep, horizon), and climate forecast accuracy for the same scenario. An analysis of 19 case studies highlights the advantages of MPC compared with conventional control strategies, but also identifies areas that need further improvement. These areas include the optimization strategy, the effects of forecast disturbance assessment, and desirable traits of existing buildings under consideration for MPC implementation. This paper develops a set of target parameters for the types of buildings for which MPC will have the most impact and suggests methods for overcoming shortfalls identified in recently published studies. [ABSTRACT FROM AUTHOR]

Detailed domestic stock energy models can be used to help formulate optimum energy reduction strategies. However, there will always be considerable uncertainty related to their predictions due to the complexity of the housing stock and the many assumptions required to implement the models. This paper presents a simple Monte Carlo (MC) model that can be easily extended and/or transformed in relation to data available for investigating and quantifying uncertainties in both the housing stock model's predictions and scenario assumptions. While 90% of the MC model predictions fell within a range which is ±19% the mean value, 50% of them were within ±8% of the mean. The findings suggest that the uncertainties associated with the model predictions and scenario assumptions need to be acknowledged fully and – where possible – quantified as even fairly small variability in the influential variables may result in rather large uncertainty in the aggregated model's prediction. [ABSTRACT FROM PUBLISHER]

Phase change material (PCM)-enhanced building envelopes can control indoor temperatures and save energy. However, PCM needs to undergo a phase change transition from solid to liquid and back to be fully effective. Furthermore, most previous research integrated PCM with high embodied energy materials. This study aims to advance the existing research on integrating PCM into carbon-negative wall assemblies composed of hempcrete and applying temperature control strategies to improve wall systems' performance while considering the hysteresis phenomenon. Four hempcrete and hempcrete-PCM (HPCM) wall design configurations were simulated and compared under different control strategies designed to reduce energy demand while enhancing the phase change transition of the microencapsulated PCM. The HPCM wall types outperformed the hempcrete wall assembly through heating (~3–7%) and cooling (~7.8–20.7%) energy savings. HPCM walls also maintained higher wall surface temperatures during the coldest days, lower during the warmest days, and within a tighter range than hempcrete assembly, thus improving the thermal comfort. However, the results also show that the optimal performance of thermal energy storage materials requires temperature controls that facilitate their charge and discharge. Hence, applied control strategies reduced heating and cooling energy demand in the range of ~4.4–21.5% and ~14.5–55%, respectively. [ABSTRACT FROM AUTHOR]

The paper provides a methodology for the optimal control of heating, ventilation, and air conditioning (HVAC) systems used in public buildings, with the purpose of obtaining high comfort and safety standards along with energy efficiency. The combination of the two concurrent objectives of minimizing energy use and guaranteeing high standards of occupant comfort is obtained by means of multi-objective optimization, in which a comfort model is combined along with a dynamic energy model of the building. The use of dynamic setpoints for the HVAC and the inclusion of comfort indicators represent a step forward, compared to the current design and operation procedures suggested by technical standards. The utilization of the proposed methodology is tested with reference to a case study, represented by an academic building used by the University of Pisa for educational purposes, whose extensive and variable occupancy can help to emphasize the importance of comfort in the operation of HVAC systems in different climatic conditions and with different occupancy profiles. We show how this optimization brings interesting results in terms of energy-saving (up to 30%), obtaining an increased comfort level (of more than 25%) compared to the operating conditions suggested by technical standards. [ABSTRACT FROM AUTHOR]

A model predictive control (MPC) system with an adaptive building model based on thermal-electrical analogy for the hybrid air conditioning system using the radiant floor and all-air system for heating is proposed in this paper to solve the heating supply control difficulties of the railway station on Tibetan Plateau. The MPC controller applies an off-line method of updating the building model to improve the accuracy of predicting indoor conditions. The control performance of the adaptive MPC is compared with the proportional-integral-derivative (PID) control, as well as an MPC without adaptive model through simulation constructed based on a TRNSYS-MATLAB co-simulation testbed. The results show that the implementation of the adaptive MPC can improve indoor thermal comfort and reduce 22.2% energy consumption compared to the PID control. Compared to the MPC without adaptive model, the adaptive MPC achieves fewer violations of constraints and reduces energy consumption by 11.5% through periodic model updating. This study focuses on the design of a control system to maintain indoor thermal comfort and improve system efficiency. The proposed method could also be applied in other public buildings. [ABSTRACT FROM AUTHOR]

Hempcrete is a sustainable biocomposite that can reduce buildings' embodied energy while improving energy performance and indoor environmental quality. This research aims to develop novel insulating hemp-lime composites using innovative binder mixes made of recycled and low-embodied energy pozzolans. The characterization of composites' mechanical and hygrothermal properties includes measuring compressive strength, splitting tensile strength, thermal conductivity, specific heat capacity, and moisture buffer capacities. This study also investigates the impact of sample densities and water content on compressive strength at different ages. The findings suggest that mixes with a 1:1 binder to hemp ratio and 300−400 kg/m3 density have hygrothermal and mechanical properties suitable for insulating infill wall applications. Hence, compressive strengths, thermal conductivity, and specific heat capacity values range from 0.09 to 0.57 MPa, 0.087 to 0.10 W/m K, and 1250 to 1557 J/kg K, respectively. The average moisture buffer value for all hempcrete samples of 2.78 (gm/m2 RH%) indicates excellent moisture buffering capacity. Recycled crushed brick pozzolan can enhance the hygrothermal performance of the hemp-lime composites. Thus, samples with 10% crushed brick have the lowest thermal conductivity considering their density and the highest moisture buffer capacity. The new formulas of hydrated lime and crushed brick have mechanical properties comparable to metakaolin and hydraulic lime formulas. [ABSTRACT FROM AUTHOR]