Environmentally Sustainable Green Roof Design for Energy Demand Reduction

Green roofs are artificial ecosystems that provide a nature-based solution to environmental problems such as climate change and the urban heat island effect by absorbing solar radiation and helping to alleviate urban environmental, economic, and social problems. Green roofs offer many benefits in terms of heat and water conservation as well as in terms of energy costs. This work proposes the design of an extensive and environmentally sustainable green roof for the Faculty of Engineering building in Bilbao. The green roof will be made from the composting of food waste generated in the building’s own canteen. Therefore, the main objective of this study is to calculate the solar efficiency of a sustainable green roof, evaluate its thermal performance, and quantify the impact that its implementation would have on energy consumption and the thermal comfort of its users. The results obtained confirm that an environmentally sustainable green roof has a positive effect on summer energy consumption and that this effect is much greater when there is water on the roof, as shown by the difference in energy savings between the dry (−53.7%) and wet (−84.2%) scenarios. The data show that in winter the differences between a green roof and a non-vegetated roof are not significant. In this case, the estimated energy consumption penalty (0.015 kWh/m2) would be 10% of the summer gain

Comparison between Energy Simulation and Monitoring Data in an Office Building

One of the most important steps in the retrofitting process of a building is to understand its pre-retrofitting stage energy performance. The best choice for carrying this out is by means of a calibrated building energy simulation (BES) model. Then, the testing of different retrofitting solutions in the validated model allows for quantifying the improvements that may be obtained, in order to choose the most suitable solution. In this work, based on the available detailed building drawings, constructive details, building operational data and the data sets obtained on a minute basis (for a whole year) from a dedicated energy monitoring system, the calibration of an in-use office building energy model has been carried out. It has been possible to construct a detailed white box model based on Design Builder software. Then, comparing the model output for indoor air temperature, lighting consumption and heating consumption against the monitored data, some of the building envelope parameters and inner building inertia of the model were fine tuned to obtain fits fulfilling the ASHRAE criteria. Problems found during this fitting process and how they are solved are explained in detail. The model calibration is firstly performed on an hourly basis for a typical winter and summer week; then, the whole year results of the simulation are compared against the monitored data. The results show a good agreement for indoor temperature, lighting and heating consumption compared with the ASHRAE criteria for the mean bias error (MBE).This research was supported by the A2PBEER project “Affordable and Adaptable Public Buildings through Energy Efficient Retrofitting” under grant number 609060 funded by the European Commission for providing resources for the monitoring system. The APC was funded by the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund through the project called “Investigation of monitoring techniques of occupied buildings for their thermal characterization and methodology to identify their key performance indicators”, project reference: RTI2018-096296-B-C22 (MCIU/AEI/FEDER, UE)

Energy and Cost Analysis of an Integrated Photovoltaic and Heat Pump Domestic System Considering Heating and Cooling Demands

The integration of photovoltaic panels and heat pumps in domestic environments is a topic that has been studied extensively. Due to their electrical nature and the presence of elements that add thermal inertia to the system (water tanks and the building itself), the functioning of compression heat pumps can be manipulated to try to fulfill a certain objective. In this paper, following a rule-based control concept that has been identified in commercial solutions and whose objective is to improve the self-consumption of the system by actively modulating the heat pump compressor, a parametric analysis is presented. By making use of a lab-tested model, the performance of the implemented control algorithm is analyzed. The main objective of this analysis is to identify and quantify the effects of the main parameters in the performance of the system, namely the climate (conditioning both heating and cooling demands), the photovoltaic installation size, the thermal insulation of the building and the control activation criteria. A total of 168 yearly simulations have been carried out. The results show that the average improvement in self-consumption is around 13%, while the cost is reduced by 2.5%. On the other hand, the heat from the heat pump and the power consumed increase by 3.7% and 5.2%, respectively. Finally, a linear equation to estimate the performance of the controller is proposed.This publication is part of the R+D+i project PID2021-126739OB-C22, financed by MCIN/AEI/10.13039/501100011033/ and “ERDF A way of making Europe”. Also, it has been financed by the Basque Business Development Agency (SPRI) in the 2020–2022 period in the projects ZL-2020-00379, ZL-2021-00225 and ZL-2022-00644 (BEROGRID); and by the Basque Government under the BIKAINTEK 2019 program

Unsupervised recognition and prediction of daily patterns in heating loads in buildings

This paper presents a multistep methodology combining unsupervised and supervised learning techniques for the identification of the daily heating energy consumption patterns in buildings. The relevant number of typical profiles is obtained through unsupervised clustering processes. Then Classification and Regression Trees are used to predict the profile type corresponding to external variables, including calendar and climatic variables, from any given day. The methodology is tested with a variety of datasets for three different buildings with different uses connected to the district heating network in Tartu (Estonia). The three buildings under analysis present different energy behaviors (residential, kindergarten and commercial buildings). The paper shows that unsupervised clustering is effective for pattern recognition since the results from the classification and regression trees match the results from the unsupervised clustering. Three main patterns have been identified in each building, seasonality and daily mean temperature being the variables that have the greatest effect. The results concluded that the best classification accuracy is obtained with a small number of clusters with a classification accuracy from 0.7 to 0.85, approximately.The authors would like to thank GREN Eesti [44] for providing data from the substations for academic purposes. The authors would like to acknowledge the Spanish Ministry of Science and Innovation (MICINN) for funding through the Sweet-TES research project (RTI2018-099557-B-C22). This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 768567

Thermal characterization of a modular living wall for improved energy performance in buildings

Vertical vegetation systems are an innovative passive method for decreasing the thermal energy demand of buildings while increasing the quality of urban life. The main objective of this work is to calculate the effectiveness of vegetation in reducing thermal loads analytically. For this purpose, the thermal energy performance of the modular living wall was compared with a traditional double façade construction system to evaluate the influence of vegetation using Stochastic Differential Equations models. The research was carried out experimentally using a real-scale PASLINK test cell. The thermal behaviour of a double leaf bare wall and the same double leaf wall converted into a modular living wall were calculated for different summertime and wintertime periods. In both studied cases, the temperature of the exterior surface of the bare wall is taken at the same place regardless of whether or not there is greenery system in the energy balance. With this simplification, the effect of the modular living wall can be identified within the estimated coefficients. The thermal resistance of the conventional double façade increased 0.74 (m2 K)/W over the non-greened wall, which represents a weighted increase of 49%. Additionally, the experimental results showed that the evapotraspiration processes that take place in the living wall lead to an increase in the combined convection-radiation coefficient, which reduces the overheating of the façade. Moreover, the effective solar absorptivity value of the outermost surface of the bare wall has been reduced an 85% thanks to the living wall, which confirms the high capacity of the living wall to reduce solar heat gains.This publication is part of the R+D+i project PID2021-126739OB-C22, financed by MCIN/AEI/10.13039/501100011033/ and “ERDF A way of making Europe”. This project has been made possible thanks to the agreement between the Basque Government and the University of the Basque Country UPV/EHU through of the ENEDI research group for the management and development of the Thermal Area of the Buildings Quality Control Laboratory of the Basque Government (ATLCCE). Open Access funding provided by University of Basque Country

Evaluation of the Thermal Performance of Two Passive Facade System Solutions for Sustainable Development

Sustainable development is essential for the future of the planet. Using passive elements, like ventilated facades based on insulation and air chambers, or living walls, which are solutions based on nature, is a powerful strategy for cities to improve their thermal environment, reduce energy consumption, and mitigate the effects of climate change. This approach allows for the quantification of the influence of passive surfaces on energy fluxes compared to bare surfaces. In addition, it delves into understanding how the incorporation of vegetation on building facades alters surface energy fluxes, involving a combination of physical and biochemical processes. This comprehensive investigation seeks to harness the potential of passive and natural solutions to address the pressing challenges of urban sustainability and climate resilience. This research uses a surface energy balance model to analyze the thermal performance of two facades using experimental data from a PASLINK test cell. This study uses the grey box RC model, which links continuous-time ordinary differential equations with discrete measurement data points. This model provides insight into the complex interplay among factors that influence the thermal behavior of building facades, with the goal of comprehensively understanding how ventilated and green facades affect the dynamics of energy flow compared to conventional facades. The initial thermal resistance of the bare facade was 0.75 (°C m2)/W. The introduction of a ventilated facade significantly increased this thermal resistance to 2.47 (°C m2)/W due to the insulating capacity of the air chamber and its insulating layer (1.70 (°C m2)/W). Regarding the modular living wall, it obtained a thermal resistance value of 1.22 (°C m2)/W (this vegetated facade does not have an insulating layer). In this context, the modular living wall proved to be effective in reducing convective energy by 68% compared with the non-green facade. It is crucial to highlight that evapotranspiration was the primary mechanism for energy dissipation in the green facade. The experiments conclusively show that both the modular living wall and open-ventilated facade significantly reduce solar heat loads compared with non-passive bare wall facades, demonstrating their effectiveness in enhancing thermal performance and minimizing heat absorption

Estimation of the Heat Loss Coefficient of Two Occupied Residential Buildings through an Average Method

The existing performance gap between the design and the real energy consumption of a building could have three main origins: the occupants’ behaviour, the performance of the energy systems and the performance of the building envelope. Through the estimation of the in-use Heat Loss Coefficient (HLC), it is possible to characterise the building’s envelope energy performance under occupied conditions. In this research, the estimation of the HLC of two individual residential buildings located in Gainsborough and Loughborough (UK) was carried out using an average method. This average method was developed and successfully tested in previous research for an occupied four-story office building with very different characteristics to individual residential buildings. Furthermore, one of the analysed residential buildings is a new, well-insulated building, while the other represents the old, poorly insulated semidetached residential building typology. Thus, the monitored data provided were filtered in order to apply the abovementioned average method. Even without fulfilling all the average method requirements for these two residential buildings, the method provides reliable HLC values for both residential buildings. For the house in Gainsborough, the best estimated HLC value was 60.2 W/K, while the best approach for Loughborough was 366.6 W/K. Thus, despite the uncertainty sources found during the analysis, the method seems promising for its application to residential buildings.This work was supported by the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund through the MONITHERM project “Investigation of monitoring techniques of occupied buildings for their thermal characterization and methodology to identify their key performance indicators”, project reference: RTI2018-096296-B-C22 and -C21 (MCIU/AEI/FEDER, UE)

Simulation and Thermo-Energy Analysis of Building Types in the Dominican Republic to Evaluate and Introduce Energy Efficiency in the Envelope

The improvement of the energy performance in buildings is key for sustainable development, even more so in the case of the Dominican Republic (DR), which is committed to this goal but which has neither regulation nor specific social behavior in this field. The main goal of this work is double; on one hand it is aimed at providing useable information for those who have the responsibly of making regulation norms and on the other, it is desirable to give an essential, technically proven and handy tool to those involved in the construction sector in improving the envelopes of buildings and to introduce good practices into the management of the energy systems of buildings. A case study of eight administrative buildings located in different climatic zones of the DR was carried out. A simulation tool was used for the study, and one of the buildings was monitored to verify the simulation work. Those factors that affect the development of the buildings in relation to thermo-energy consumption have been detailed. The large-scale heat gains resulting from the common glazing used by the tertiary sector in the Dominican Republic (including office buildings, hospitals and shops among others) illustrate the need for economically viable solutions in this sector. As a conclusion, it has been proved that the incidental thermal load of buildings could be reduced by up to 40%, thus in turn reducing the costs associated with the electricity needed to maintain the users’ desired thermal comfort level, as their influence in this sector is significant

Design of a Microscale Refrigeration System for Optimizing the Usable Space in Compact Refrigerators

This research aims to enter the miniature refrigeration machine sector with the objective of designing a small scale unit while maintaining a competitive coefficient of performance (COP), comparing with a Peltier plates system. To this end, a research of the current technology was carried out in order to obtain indicative values on the scales that were being worked on and their application. After the previous research, a refrigeration cycle was designed in EES (engineering equation solver). From this design, different conclusions were obtained: (1) The correct sizing of the compressor revolutions together with its displacement is crucial for the equipment to be able to provide the desired cooling capacity. (2) In order to obtain the desired cooling capacity in the microscale refrigeration system, the heat exchangers must have fins. (3) Of the analysed refrigerants, R600a is the best choice, as it shows favourable characteristics (high COP and low compression ratio) when working in this type of cycle.This work was supported by the Basque Government in the Elkartek call through the SOLRUC project “Knowledge acquisition for the design of new ultra-compact cooling solutions”, project reference: KK-2020/00115

Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters

[EN] An accurate characterization and prediction of heat loads in buildings connected to a District Heating (DH) network is crucial for the effective operation of these systems. The high variability of the heat production process of DH networks with low supply temperatures and derived from the incorporation of different heat sources increases the need for heat demand prediction models. This paper presents a novel data-driven model for the characterization and prediction of heating demand in buildings connected to a DH network. This model is built on the so-called Q-algorithm and fed with real data from 42 smart energy meters located in 42 buildings connected to the DH in Tartu (Estonia). These meters deliver heat consumption data with a 1-h frequency. Heat load profiles are analysed, and a model based on supervised clustering methods in combination with multiple variable regression is proposed. The model makes use of four climatic variables, including outdoor ambient temperature, global solar radiation and wind speed and direction, combined with time factors and data from smart meters. The model is designed for deployment over large sets of the building stock, and thus aims to forecast heat load regardless of the construction characteristics or final use of the building. The low computational cost required by this algorithm enables its integration into machines with no special requirements due to the equations governing the model. The data-driven model is evaluated both statistically and from an engineering or energetic point of view. R-2 values from 0.70 to 0.99 are obtained for daily data resolution and R-2 values up to 0.95 for hourly data resolution. Hourly results are very promising for more than 90% of the buildings under study. (This study has been carried out in the context of RELaTED project. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 768567. This publication reflects only the authors' views and neither the Agency nor the Commission are responsible for any use that may be made of the information contained therein