Improve Building Energy Efficiency Research Articles

Carbon reduction potential of housing retrofits: Evidence from China

Carbon emissions have become a major contributor to global climate change, with the building sector representing one-third of the global energy demand, and the residential sector accounting for 70 % of final energy demand worldwide. The energy consumption for building cooling has grown rapidly in recent years. Housing retrofits have shown potential in improving building energy efficiency and thus aligning with objectives for energy-saving and carbon emissions reduction in the residential sector. Based on survey data from 28 provinces in China, this study estimates the impacts of housing retrofits on the building energy consumption for air conditioners, as well as impacts on the resulting carbon emissions. Employing Endogenous switching regression to mitigate self-selection bias and the consequent endogeneity problem, the analysis reveals that housing retrofits can reduce building energy consumption and consequent carbon emissions. Furthermore, factors including household characteristics, building attributes and geographic location can influence both residential energy consumption and carbon emissions. In addition, low-income households, families with children and those living in self-owned houses can benefit the most from housing retrofits. This study also offers recommendations to reducing household energy consumption and carbon emissions through improved renovation practices. These insights are instrumental for policymakers in identifying targeted policy objectives across diverse demographic groups. The findings hold relevance for other developing countries with large amounts of inefficient housing stock during rapid urbanization, and those prone to global warming, aiding their pursuit of Sustainable Development Goals.

Sustainable metal-organic framework based paper sheet as wallpaper for passive indoor moisture control

Passive indoor moisture control is a meaningful way to reduce HVAC operation and improve building energy efficiency. Metal-organic frameworks (MOFs) have been considered one of the most promising sorbent materials for passive indoor moisture control. However, the powder form of MOF is challenging to use in the built environment. Typical shaping methods like pellets, beads, and coating can lead to problems like partial pore blocking, insufficient mechanical stability, amorphization, and low material utilization. Here, we report a new sustainable MOF paper sheet comprising 25% cellulosic fibers and 75% MOF MIL-100(Fe) powder. The porous polymer matrix provides good support and fixation for MOF particles, so it’s more applicable in the built environment. The crystalline and pore structure characteristics of the MOF in the membrane are well preserved. Hydro characteristic test results show that MIL-100(Fe) paper sheet has high water vapor uptake, S-shape isotherm, mild regeneration condition, and high MBV. Then, a numerical model was developed to calculate the relation between a material’s MBV and its thickness. MIL-100(Fe) paper sheet with the most efficient thickness was applied as wallpaper in a building energy simulation model. The simulation result shows that the MIL-100(Fe) paper sheet is promising for passive indoor moisture control. It can remove about 45%-55% of the latent cooling load in most European climate types.

Carbon neutrality in Malaysia and Kuala Lumpur: insights from stakeholder-driven integrated assessment modeling

Introduction: Several cities in Malaysia have established plans to reduce their CO2 emissions, in addition to Malaysia submitting a Nationally Determined Contribution to reduce its carbon intensity (against GDP) by 45% in 2030 compared to 2005. Meeting these emissions reduction goals will require a joint effort between governments, industries, and corporations at different scales and across sectors.Methods: In collaboration with national and sub-national stakeholders, we developed and used a global integrated assessment model to explore emissions mitigation pathways in Malaysia and Kuala Lumpur. Guided by current climate action plans, we created a suite of scenarios to reflect uncertainties in policy ambition, level of adoption, and implementation for reaching carbon neutrality. Through iterative engagement with all parties, we refined the scenarios and focus of the analysis to best meet the stakeholders’ needs.Results: We found that Malaysia can reduce its carbon intensity and reach carbon neutrality by 2050, and that action in Kuala Lumpur can play a significant role. Decarbonization of the power sector paired with extensive electrification, energy efficiency improvements in buildings, transportation, and industry, and the use of advanced technologies such as hydrogen and carbon capture and storage will be major drivers to mitigate emissions, with carbon dioxide removal strategies being key to eliminate residual emissions.Discussion: Our results suggest a hopeful future for Malaysia’s ability to meet its climate goals, recognizing that there may be technological, social, and financial challenges along the way. This study highlights the participatory process in which stakeholders contributed to the development of the model and guided the analysis, as well as insights into Malaysia’s decarbonization potential and the role of multilevel governance.

Research on the evaluation of the renovation effect of existing energy-inefficient residential buildings in a severe cold region of China

The renovation of existing energy-inefficient residential buildings is an important measure to improve building energy efficiency and achieve CO2 emission reduction strategic tasks. At present, existing energy-inefficient residential buildings suffer from high energy consumption for heating, low efficiency, and poor indoor thermal comfort. It is necessary to implement energy-saving renovation on existing residential buildings and evaluate the renovation effect. This article took existing energy-inefficient residential buildings as the research object and selected a targeted evaluation system. Based on expert research, the hierarchical-grey correlation degree method was used to determine the weight of indices. Actual testing was conducted on the three nodes of the heat exchange station, secondary network, and the building ontology during the heating seasons of 2015–2016 and 2016–2017 (before and after renovation). Based on measured data, a fuzzy membership degree evaluation model was constructed, and the evaluation results were determined using a linear weighted method to form a complete evaluation system. The evaluation system covered 12 evaluation indices, and the evaluation indices with the largest weight in the criterion and index layer were energy consumption (0.308) and economic contribution rate (0.183), respectively. The average linear weighted evaluation scores before and after the renovation of each node were 14.16 and 29.04, respectively.

Photochromic Thermoelectric Smart Window for Season‐Adaptive Solar Heat and Daylight Management

AbstractPhotochromic smart windows have drawn increasing attention as an approach to improve building energy efficiency and enhance indoor daylight comfort. However, existing photochromic smart windows still block sunlight from entering the room on sunny winter days, causing additional energy consumption for heating. Herein, a dual‐mode smart window is designed with decoupled photo and thermal functions by combining colorless Fe‐doped WO3 photochromic film with window rotation. Based on this, selective heating and cooling of the room between winter and summer is achieved while maintaining the daylight comfort benefits during all seasons. As a proof of concept, the smart window reduces the temperature of a model house by up to 7.9 °C in summer mode, while in winter mode the temperature is only reduced by 0.7 °C. The proposed seasonally adaptive dual‐mode smart window obtains by window rotation overcomes the limitations of conventional photochromic smart windows, which not only achieves better energy efficiency but also retains improved daylight comfort. Furthermore, it demonstrates that the heat absorbed by the smart window can be harnessed to produce electricity through the integration of thermoelectric modules within the glazing, which enhances its impact on reducing energy consumption.

Performance evaluation system of key energy-saving technologies in public institutions based on heat pump system in cold regions

The energy systems of public institutions experience unsatisfactory actual operation and improper operation management, and the level of building energy consumption is increasing annually. Research on the application of key energy-saving technologies in public institutions is an effective method to improve building energy efficiency and reduce energy wastage. In this study, the aperformance of key energy-saving technologies in public institutions were considered as the research object. Combined with the characteristics of different energy-saving technologies and relevant national standards, the evaluation indices were selected from four aspects: comprehensive energy efficiency, economy, environment, and operation management. According to the results of expert investigations and measured data, the index weight was determined using the artificial neural network and expert scoring methods, and a complete evaluation system of indices was built. There were 4 first-level, 10 second-level, and 11 third-level evaluation indices in the evaluation system. The evaluation indices of each level with the largest weights were the comprehensive energy efficiency of the buildings (0.532), technical energy efficiency (0.331), and water pump power consumption to heat (cold) transmission ratio (0.167). The evaluation system for key energy-saving technologies in public institutions is useful for operation managers to identify the weak points of system operations and formulate timely optimisation schemes. This evaluation system can be used as a valuable reference for improving the energy-saving levels of public institutions.

Multi-objective modified satin Bowerbird optimization algorithm used for simulation-based energy consumption optimization of yearly energy demand of lighting and cooling in a test case room

An effective method of optimizing building energy performance through simulation is introduced in this study. The Modified Satin Bowerbird Optimization Algorithm and the DesignBuilder simulation tools are used to find optimal solutions for improving building energy efficiency. Five distinct climatic zones of China are examined in terms of decision variables including window size, overhang characteristics, and building orientation. The objective functions are based on the energy demand for lighting and cooling over a year. Analyses of optimization employing either a single-criterion or a multi-criteria approach delve into the interactions between these functions. According to the results, buildings had a reduction of 24.0 %–42.7 % in their overall yearly energy consumption. 22.9 %, 24.0 %, 35.1 %, 37.2 %, and 42.7 % yearly overall energy consumption reduction are specific to Harbin, Beijing, Guangzhou, Shanghai, and Kunming, respectively. Under varying weather conditions, architectural decision variables have a significant impact on energy consumption.

AutoBPS-Prototype: A web-based toolkit to automatically generate prototype building energy models with customizable efficiency values in China

Prototype building energy models are significant in building energy performance evaluation and energy efficiency potential analysis. However, their establishment process is time-consuming and error-prone. This study aims to develop a user-friendly toolkit, AutoBPS-Prototype, to automatically establish prototype building energy models in China. A web-based graphical user interface of AutoBPS-Prototype was designed and implemented. AutoBPS-Prototype only needs to define the climate zone, building type, and standard vintage to generate a default prototype building energy model based on a pre-built library. In addition, it can generate user-defined models through customized important efficiency values. The OpenStudio Standards used to create the U.S. Department of Energy prototype building models based on the ASHRAE 90.1 standards was extended to cover more prototype building energy models in China, including 20 sub-climate zones, 22 building types and four code and standard vintages. The Chinese building standards were used for the efficiency settings. Besides, case studies were conducted. Firstly, 20 prototype building models of four building types in five cities were generated. The credibility of the model results simulated by EnergyPlus was verified through comparison with measured data. Then, the impact of spatiotemporal differences on building energy consumption was analyzed through simulation of the models using different standard vintages in different climate zones. Finally, a baseline and four upgrade scenarios were designed to explore the impact of various upgrades on building energy consumption. This toolkit aims to assist users in conducting rapid research on building energy efficiency improvement. More toolkit features are under development for retrofit analysis, demand response analysis, and photovoltaics system generation estimation.

Bioclimatic classification for building energy efficiency using hierarchical clustering: A case study for Sri Lanka

Climate zoning is fundamental to improving building energy efficiency through bioclimatic design principles and passive design strategies. In Sri Lanka, buildings consume approximately 37 % of primary energy and about 33 % of total generated electricity. This study aims to carry out bioclimatic zoning and estimate the passive design potential for each bioclimatic zone. Utilising dimensionality reduction through Principal Component Analysis (PCA) and Hierarchical Clustering on 31 years of weather information, Sri Lanka is classified into three distinct bioclimatic zones, namely Z1 (Hot and humid), Z2 (Cool and humid) and Z3 (Warm and humid). An hourly method was used for each zone to determine HDD and CDD. Accordingly, regression models were constructed (R2 > 0.77) to estimate the CDD for each zone for cooling load. Bioclimatic potential analysis is carried out to link climate parameters with building performance. The results demonstrate that natural ventilation is the most effective passive design method with potential 64 ± 13%, 63 ± 10, and 83 ± 4% in a year for hot and humid, cool and humid, and warm and humid zones, respectively. The results emphasise the importance of using bioclimatic classification to design energy-efficient buildings to achieve national energy efficiency goals in low-income countries like Sri Lanka.

Numerical study on the effect of optimizing the Trombe wall structure with built-in fins on improving building energy efficiency in severe cold region

In order to save heating energy and reduce carbon emission intensity, a floor heating room with built-in finned Trombe wall in Xining, Qinghai, a severe cold area, was taken as the research object. And ANSYS 2021 R1 was used to numerically investigate the effects of several major factors, such as fin height, fin transverse distance, fin longitudinal distance, fin inclined angle, fin shape, perforated shape and perforation ratio of perforated fin, on the average heat transfer coefficient (h) between the absorptive surface and the air in the interlayer. The results show that for fins with low heights, the enhancement effect of horizontal fins on the convective heat transfer of the absorptive surface of Trombe wall is greater than that of vertical fins. The enhancement effect of vertical fins on the convective heat transfer of the absorptive surface is more affected by fin transverse distance, while the enhancement effect of horizontal fins on the convective heat transfer of the absorptive surface is more affected by fin longitudinal distance. Besides, h shows a trend of first increasing and then decreasing with the increase of fin inclined angle, and reaches its maximum value of 5.89 W/(m2·K) at 45°. The fins with different shapes are equilateral triangular fins, semicircular fins, isosceles triangular fins and rectangular fins in descending order of their enhancement effect on the heat transfer of wall. Compared with solid fins, fins with circular holes can obtain a better convective heat transfer effect only when the open area ratio is 1:6. While fins with equilateral triangular holes can obtain better convective heat transfer effect as long as the open area ratio is greater than 1:7. Fins with elliptical holes do not significantly improve the heat transfer effect. On the basis of optimizing the fin layout and structure, the thermal performance of the improved Trombe wall during the 8:00∼16:00 period and its impact on indoor thermal comfort of the building were studied, and the energy-saving performance of the plan was discussed. Compared with the Trombe wall room with no fins, the improved Trombe wall can improve the heat teransfer efficiency and the building energy saving rate by 68.50 % and 53.57 %, respectively. The improved Trombe wall can only ensure the indoor thermal comfort in limited several hours, in the rest of time, the residents should add clothing to keep warm. These conclusions have an important effect of engineering guidance and great realistic meanings for promoting the process of solar building integration and achieving the “carbon peak and carbon neutrality” target.

Using Pearson correlation coefficient as a performance indicator in the compensation algorithm of asynchronous temperature-humidity sensor pair

Artificial Intelligence (AI) based control algorithms for heating, ventilation, and air conditioning (HVAC) equipment have been gradually applied to improve building energy efficiency. Nevertheless, a reliable dataset is certainly a cornerstone for any meaningful AI training. Unfortunately, significant errors exist on humidity records due to asynchronous humidity and temperature sensor time constants, which need to be better compensated. This study aims to verify the general applicability of the previously proposed compensation algorithm and discover a new method to determine essential parameters for the algorithm without lab testing, which makes it possible to apply the compensation algorithm to on-duty sensor pairs. Experiment results from newly tested sensor pairs are found comparable to the previous study's outcome, which confirms the algorithm's general applicability. Meanwhile, the newly proposed performance indicator – the Pearson correlation coefficient (PCC) of humidity ratio and temperature – results in a 64–97 % error reduction on the tested sensor pairs. Despite not being as steady as the original lab method, the PCC proved a possible alternative method worth further investigation due to its accessibility.

Semantic segmentation of longitudinal thermal images for identification of hot and cool spots in urban areas

This work presents the analysis of semantically segmented, longitudinally, and spatially rich thermal images collected at the neighborhood scale to identify hot and cool spots in urban areas. An infrared observatory was operated over a few months to collect thermal images of different types of buildings on the educational campus of the National University of Singapore. A subset of the thermal image dataset was used to train state-of-the-art deep learning models to segment various urban features such as buildings, vegetation, sky, and roads. It was observed that the U-Net segmentation model with ‘resnet34’ CNN backbone has the highest mIoU score of 0.99 on the test dataset, compared to other models such as DeepLabV3, DeeplabV3+, FPN, and PSPnet. The masks generated using the segmentation models were then used to extract the temperature from thermal images and correct for differences in the emissivity of various urban features. Further, various statistical measures of the temperature extracted using the predicted segmentation masks are shown to closely match the temperature extracted using the ground truth masks. Finally, the masks were used to identify hot and cool spots in the urban feature at various instances of time. This forms one of the very few studies demonstrating the automated analysis of thermal images, which can be of potential use to urban planners for devising mitigation strategies for reducing the urban heat island (UHI) effect, improving building energy efficiency, and maximizing outdoor thermal comfort.

A review of the 1-2 apartment residential building stock in Lithuania based on an analysis of Energy Performance Certificates

One of priorities of the EU’s climate change policy is the energy performance of buildings. This is reflected primarily through the series of Directives the EU has enacted along the years, but is most precisely conveyed in the Energy Performance of Buildings Directive (EPBD). As part of the European Union, Lithuania has enacted its own long-term strategy aimed at transforming the current building stock in one which is more energy efficient. Building energy certification, legalized in accordance with the requirements of the directive is the primary tool used to evaluate the energy-efficiency of a building, which includes determination of the energy consumption of a building and subsequently assigning it with an energy performance class. Energy performance certificates (EPC) are obligatory for new and existing buildings in all European Union (EU) member states and provide not only a description of the energy performance characteristics of individual buildings, but are also useful source of legal information for determination of the achievements of building energy efficiency improvement strategy. Despite the current wide spread presence of EPC analyses in other European countries, similar information on the national EPC register of Lithuania is still hardly present in research studies. To this effect this paper presents a detailed overview of the stock of Lithuanian residential 1-2 apartments buildings using data from the national EPC register. The results obtained present the impact of the implementation of EPBD on Lithuanian residential 1-2 apartments buildings, including changes in thermal insulation of building elements, heating system typology, energy consumption and CO2 emissions.

Cracking the code: mapping residential building energy performance in rural Central Asia through building typologies

Buildings in rural Central Asia have unique characteristics as they were built during the Soviet era (during 1950–1960) without any energy efficiency measures. The special and aged building stock pose a crucial challenge on energy security and energy supply. However, accurate scientific data on their energy usage is lacking, highlighting a research gap about robust and validated methodology to determine the energy use of rural Central Asian buildings. In response to this need, this research paper proposes a set of generalised residential building typologies for rural Central Asia, with a focus on Kyrgyzstan. The study combines quantitative and qualitative methods, including household surveys, energy demand modelling, EnergyPlus simulations, and result validation. It derives energy-based building typologies from construction year and building envelope characteristics. Through numerous validated simulations, it was identified that the heat demand for rural Kyrgyz houses is 3–5 times here compared to European houses (250–400 kWh/m2). The study revealed the estimated heat demand for homes in rural Kyrgyzstan, indicating a significant potential for improving building energy efficiency in the region. These results can inform specific approaches, such as building renovation plans, energy certification, and renovation strategies. Furthermore, the validated methodology introduced offers opportunities for researchers in the field of building energy efficiency and can be applied to similar regions in Central Asia with comparable building stocks and climates.

Exploring the impact of additional sunspace on the total annual energy consumption of rural residences in cold region of China: a Case study in the Xuzhou region

ABSTRACT Sunspace is a popular strategy for enhancing the energy efficiency of buildings. This study aims to investigate the impact of sunspace on a rural residence located in a cold region of China on its total annual energy consumption. To do so, the researchers developed a benchmark model of a rural residence in Xuzhou and simulated six types of additional sunspaces using Honeybee to estimate energy consumption. The results clearly demonstrated that incorporating sunspaces into rural residences had a significant impact on reducing annual energy consumption, leading to energy savings ranging from 4.7% to 10.8% compared to the benchmark rural residence. Additionally, the study analyzed the relationship between the energy consumption of the rural residence and the location and depth of the sunspace. The findings revealed that the location of the sunspace had a greater impact on annual energy consumption than its depth. Lastly, the study also analyzed the economic viability of sunspace application in XuZhou rural residences, demonstrating that the utilization of sunspaces in XuZhou rural residences is economically feasible. The study provides valuable empirical and theoretical support for the design of sunspaces in rural residences located in cold regions of China and underscores the potential benefits of incorporating sunspaces to improve building energy efficiency.

Improving building energy efficiency and thermal comfort with natural fibre insulation

As part of the energy transition and global environmental problems, improving the energy efficiency of buildings has become an imperative aspect for the architecture, engineering and construction industries. Furthermore, the energy performance of a building depends directly on the quality of its thermal envelope and the insulation material applied. Therefore, the main aim of this study is to assess by means of simulations the impact of ecological insulation materials on thermal comfort and energy efficiency in the case of a residential building in a Mediterranean climate. The results of the simulation show that optimising envelope design parameters can reduce annual energy loads for heating and for cooling. The most efficient strategies for achieving this reduction involve the use of ecological thermal insulation of external walls, optimising glazing settings and use of efficient shading devices.

Enhancing solar façade thermal performance with PCM spheres: A CFD investigation

To improve building energy efficiency and address thermal storage challenges during periods without a heat source, such as cloudy weather or night-time, a range of solutions is required. Innovative technologies and sustainable practices are essential for combating climate change and reducing carbon emissions. This study primarily focuses on Thermal Energy Storage (TES) systems, specifically those using Phase Change Materials (PCMs), to increase energy efficiency for Transpired Solar Collectors used in buildings applications. During the last 30 years Transpired Solar Collectors (TSC) have been extensively investigated. However, a primary concern still exists regarding thermal storage when the heat source is unavailable, such as during periods of cloudy weather or at night. Thus, a Thermal Energy Storage (TES) system coupled with the TSC is a potential solution. In this study we are investigating using numerical simulation the arrangement of encapsulation for TES, integrating phase change materials (PCM) in spherical elements when compared with plate encapsulation elements. The model reproduces a part of a real scale thermal energy storage inserted in a Double Skin TSC. The model consists of a Plexiglas duct in which four different arrangements for the spherical encapsulated PCM were studied. For each of the arrangements the heat transfer between the TES elements and the air passing through the collector was analyzed. The primary finding of the study indicates that the hexagonal arrangement offers better passive airflow control, thus enhancing the heat transfer up to 12.3% compared to the rectangular arrangements

A personalized regression model for predicting thermal sensation based on local skin temperature in moderate summer conditions

Personalized thermal sensation models play a crucial role in ensuring occupant's thermal comfort satisfaction and improving building energy efficiency. However, an adaptive and accurate personalized model that can be easily implemented in real life is still challenging. This paper investigates the influencing factors of thermal sensation vote (TSV) and proposes a personalized regression model that only uses a single local skin temperature as the key indicator. A survey is conducted with forty subjects aging from 20 to 59 years old. The relationship among ambient temperature, skin temperatures, and subjective TSV is analyzed. The forehead temperature is recommended as the key indicator for prediction because it exhibits a strong correlation with ambient temperature and TSV, and it is easy to capture. Furthermore, the impact of individual characteristics on TSV is investigated. The proposed model effectively captures and compensates for individual differences by incorporating subjects' set point skin temperature and body fat percentage (BF%). The proposed model can be readily applied in real-life scenarios due to its minimal requirement for occupant’s feedback and its higher accuracy compared to other models. Specifically, it exhibits a significantly lower Root Mean Square Error (RMSE) of 15.8 %, 9.4 %, and 65.2 % compared to the Support Vector Regression (SVR) model Zhang's model and Zhou's model. Moreover, the proposed model showcases the lowest mean absolute error among the compared models. This approach of developing a personalized regression model based on local body temperature holds promise for future international ergonomic standard development.

SmartBuild RecSys: A Recommendation System Based on the Smart Readiness Indicator for Energy Efficiency in Buildings

The Smart Readiness Indicator (SRI) is a newly developed framework that measures a building’s technological readiness to improve its energy efficiency. The integration of data obtained from this framework with data derived from Building Information Modeling (BIM) has the potential to yield compelling results. This research proposes an algorithm for a Recommendation System (RS) that uses SRI and BIM data to advise on building energy-efficiency improvements. Following a modular programming approach, the proposed system is split into two algorithmic approaches linked with two distinct use cases. In the first use case, BIM data are utilized to provide thermal envelope enhancement recommendations. A hybrid Machine Learning (ML) (Random Forest–Decision Tree) algorithm is trained using an Industry Foundation Class (IFC) BIM model of CERTH’S nZEB Smart Home in Greece and Passive House database data. In the second use case, SRI data are utilized to develop an RS for Heating, Ventilation, and Air Conditioning (HVAC) system improvement, in which a process utilizes a filtering function and KNN algorithm to suggest automation levels for building service improvements. Considering the results from both use cases, this paper provides a solid framework that exploits more possibilities for coupling SRI with BIM data. It presents a novel algorithm that exploits these data to facilitate the development of an RS system for increasing building energy efficiency.

Comparative analysis of metaheuristic optimization approaches for multisource heat pump operation

Through the use of effectively unlimited energy from the environment, contemporary heat pump installations have proven to be a highly efficient means for satisfying both heating and cooling needs. As such, they are often regarded as a key driver in the electrification process of the thermal domain. Keeping in mind the significant share of the thermal demand in building energy requirements, this aspect in particular is projected to have a great impact on the overall energy efficiency improvement of buildings and increase of demand-side flexibility. The latter point is considered crucial for improving the precarious balance against the contemporary intermittent power supply obtained from a variety of renewable sources. However, in the process of increasing their efficiency, heat pump systems are becoming significantly more complex with combinations of various different sources of energy from the ground, air and the Sun. By extension, the complexity of managing their operation has also increased and proves to be a challenge even for experienced plant operators. In order to aid in this regard, relevant literature suggests that optimization algorithms can be considered as a viable tool. This paper explores the limited extent of existing state-of-the-art approaches for hybrid heat pump control via metaheuristic optimization and broadens the subject matter with a comparative study that analyzes and benchmarks several metaheuristic approaches found in the literature on similar problems. A real-world multisource heat pump installation in Ferrara is utilized as the basis for the study.