Multi-objective optimization of latent energy storage in buildings by using phase change materials with different melting temperatures

Autores:
Facundo Bre a b, Roberto Lamberts c, Silvana Flores-Larsen d e, Eduardus A.B. Koenders a
Resumo:

Technologies based on phase change materials (PCMs) are promising solutions to reduce energy consumption in buildings and related greenhouse gas emissions. However, the performance of passive PCMs in buildings is highly dependent on the melting temperatures employed, as well as the climate where the building is located. Therefore, the present contribution describes an optimization-based method to design passive latent energy storage in buildings by using PCMs with different melting temperatures. To achieve this goal, a multi-objective genetic algorithm is coupled with the building energy models developed in EnergyPlus to find the best trade-off between annual heating and cooling loads. A small office is chosen as a case study to evaluate the energy performance of the buildings incorporating the proposed PCM approach. Three different PCM layers are added to the ceilings and the external and internal walls of the building, and their parametric models are developed in EnergyPlus to optimize the melting temperature and thickness of each PCM layer simultaneously. Moreover, a method to select climate-representative locations according to the ASHRAE 169-2020 climate classification and within the WMO Region VI (Europe) is proposed and applied, resulting in eight well-representative locations. An optimization-based design is carried out for each selected location and the performances of the optimized building designs are systematically compared to the ones of the baseline models. The optimization results achieved show that regardless of the climate zone analyzed, using several PCMs with different melting temperatures instead of a single one, is preferred. Moreover, the best performance of PCMs is attained in climate zones where both the heating and cooling loads are present. Thus, the highest saving regarding the annual total loads of 11.7% is achieved in zone 5A (Cold), while the lowest one of 2.3% is obtained in zone 1B (Very hot).

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Developing a surrogate model for naturally ventilated cellular offices in Brazil

Autores:
Marcelo Salles Olinger, Ana Paula Melo, Roberto Lamberts
Resumo:

Demand for artificial cooling in buildings is increasing worldwide, and it is expected to continue to grow in the upcoming decades. To mitigate energy use, the adoption of passive cooling strategies such as natural ventilation is a solution. In this study, a metamodel is developed to estimate thermal performance in naturally ventilated cellular offices in Brazil. Metamodels help to overcome the complexity of building simulation, although some caveats should be considered when developing such models. Simulation parameters were based on a database of cellular office buildings from São Paulo. The output of the simulations was the fraction of hours with operative temperatures above ASHRAE’s Standard 55 adaptive model. Two different modeling approaches were analyzed, a single-zone and a multi-zone. Sensitivity analyses were conducted to identify what parameters are the most influential on simulation results, and what parameters compromise the accuracy of the single-zone approach. Window opening factor, walls’ transmittance and the condition of exposure of walls and windows were the most influential parameters on the simulations. Although the single-zone approach is simpler, it does not consider heat transfer between different offices adequately. From the two modeling approaches, two metamodels were developed. The single-zone metamodel had a more accurate performance when validated on single-zone simulations. However, the errors related to this modeling approach compromises the performance when compared to multi-zone simulations. It is concluded that it is important to understand the limitations and accuracy of a surrogate model before applying it, and that such tool could be helpful for building designers.

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Are years-long field studies about window operation efficient? a data-driven approach based on information theory and deep learning

Autores:
Mateus Bavaresco a, Ioannis Kousis b, Ilaria Pigliautile b c, Anna Laura Pisello b c, Cristina Piselli c d, Enedir Ghisi a
Resumo:

Scientific literature about building occupants’ behaviour and the related energy performance analyses document about several strategies to monitor window operation, including different sensors and data series lengths. In this framework, the primary goal of this study is to propose effective guidelines for minimum experiment durations and their reliability. A six-year-long database from a living laboratory was used as a benchmark; and a recursive strategy enabled to split it into more than 2,500 subsets, supporting two main steps. First, information theory concepts were used to calculate uncertainty and subsets’ divergence were compared to the full database. Second, the subsets were used to train deep neural networks and evaluate the influence of monitoring lengths combined with different kinds of environmental data (i.e. indoor or outdoor). From the information-theoretic metrics, the results support that indoor-related variables can reduce most of the uncertainty related to window operation. Besides, subsets influenced by autumn and winter diverge the most compared to the full database. Considering the modelling approach, the results demonstrated that by including indoor-related variables, higher shares of reliably-performing models were achieved, and smaller subsets were needed. Seasonality has also played a major role along these lines. As a consequence, the conclusions supported the feasibility of nine-month-long field studies, starting in summer or spring, when indoor and outdoor variables are monitored.

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Bottom-up modelling of electricity end-use consumption of the residential sector in Brazil

Autores:
Cristiano André Teixeira, Ana Paula Melo, Michele Fossati, Roberto Lamberts
Resumo:

Electricity consumption in the residential sector in Brazil has been increasing annually despite efforts to promote the energy efficiency of household appliances. One of the main goals for achieving more energy efficiency in dwellings is understanding its energy end uses. In this context, this paper presents a bottom-up model developed to analyse regional and national electricity end uses in the residential sector in Brazil based on a recent survey on Ownership of Appliances and Consumption Habits. The percentages of total electricity consumption associated with nine appliances (light bulbs, refrigerators, freezers, televisions, showers, microwaves, washing machines, fans, and air conditioners) were estimated. The values were obtained using the software EnergyPlus for air conditioners and electricity consumption equations for the other eight appliances. Results show that the proposed model gives reasonable estimates of electricity consumption, which were close to the values expected for most appliances. Regionally, the appliances for which ownership and pattern of use are influenced by the climate (electric showers, fans, and air conditioners) obtained the most significant variation in the percentage of electricity consumption.

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A Global Building Occupant Behavior Database

Autores:
Bing Dong, Yapan Liu, Wei Mu, Zixin Jiang, Pratik Pandey, Tianzhen Hong, Bjarne Olesen, Thomas Lawrence, Zheng O’Neil, Clinton Andrews, Elie Azar, Karol Bandurski, Ronita Bardhan, Mateus Bavaresco, Christiane Berger, Jane Burry, Salvatore Carlucci, Karin
Resumo:

This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.

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A holistic approach for assessing architectural integration quality of solar photovoltaic rooftops and shading devices

Autores:
I. Custódio a b, T. Quevedo c, A.P. Melo c, R. Rüther a b
Resumo:

When used as building construction elements, photovoltaic (PV) systems can be multifunctional. Therefore, they can provide innovative envelope designs that impact energy consumption. PV integration can radically change the form of buildings and cities; thus, their architectural quality must be protected. This work evaluates the integration quality of PV systems, considering constructive, functional, and aesthetical (formal) aspects, contemplating performance criteria set by different stakeholders. The influences of PV integration on the energy performance of buildings, its energy generation, energy yield, Performance Ratio, economic viability, and aesthetical features are assessed. Widely used types of integration are contemplated: PV rooftops and PV shading devices. The approach is described in detail and uses the internationally adopted softwares EnergyPlus®, Rhino®, and PVsyst® to be broadly used by designers. Six case studies of a commercial building in low-latitude sites showed that PV building integration can be used as a strategy to reduce energy consumption, while keeping high energy generation performance, being economically viable, and respecting architecture. Results have emphasized the importance of assessing PV architectural integration quality, showing that a detailed analysis is essential to identify more advantageous situations.

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Comparison between national and local benchmarking models: The case of public nursery schools in Southern Brazil

Autores:
Veronica Martins Gnecco, Matheus Soares Geraldi, Michele Fossati, Maria Andrea Triana
Resumo:

Energy benchmarking models assist the decision-making process for urban planners, architects, engineers and stakeholders. However, specialists diverge about the accuracy of benchmarking models used to represent a location. The aim of this study is to compare two benchmarking models of nursery schools: first, considering the national building stock for Brazil; second, using local data for a Southern Brazilian city. The national benchmarking model was obtained from a country-level benchmarking policy. The local benchmarking model was developed in four steps: data collection, archetype modeling, energy simulations and multiple linear regression to formulate the benchmark equation. Then, both national and local models were compared using data of 12 actual nursery schools, through their energy performance classification and an ANOVA test. Results showed that there are no significant differences between the models. Conclusion points out that a country-level benchmarking can adequately represent a city-level benchmarking; however, a late clustering process might be indicated for cases in locations with particularities apart from the national building stock. This paper provides an opportunity to discuss if benchmarking models, created with large stock-level analysis, indeed can evaluate the energy performance of specific buildings. The authors recommend future research comprising other regions to enhance applicability of the results.

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Users’ assessment of personal fans in a warm office space in brazil

Autores:
Maíra André, Carolina Buonocore, Luiza Castro, Roberto Lamberts
Resumo:

The use of personal fans allows improving thermal comfort and energy savings in warm office spaces. This is due to individual adjustment and extended indoor temperature acceptability. However, to achieve that, the usability of fans must be assured. Therefore, an experiment with 40 people of various age groups was carried out to assess four types of fans, one of which is an evaporative cooling device. The goal was to find out which criteria should be used for selecting a fan to implement in an office space. Results show that airflow sensation and speed adjustment are considered the most important, although, noise is also very important, and cost can be an eliminatory criterion. The evaporative device was the best rated even in a space with 70 to 80% relative humidity, as users considered it to have a smooth controllable airflow. The results highlight these aspects should be considered in the selection of a personal fan and could also drive the industry to improve fans design for increasing usability and expanding the use of these systems.

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Assessment of solar radiation data quality in typical meteorological years and its influence on the building performance simulation

Autores:
Facundo Bre a, Rayner M. e Silva Machado b, Linda K. Lawrie c, Drury B. Crawley d, Roberto Lamberts b
Resumo:

Solar radiation along with other weather variables are commonly processed on typical meteorological years (TMYs) to be applied in the design of various energy systems. However, in several regions of the world, solar radiation data usually lacks a suitable and/or representative measurement, which leads to its modeling and prediction to properly fill this information in the databases. Consequently, the accuracy of these models can influence the viability and proper design of such energy systems. Within this context, the present contribution aims to assess the quality of solar radiation data included in the most recent TMY databases with Brazilian data and how that quality can influence the selection of months that create TMYs as well as the building performance simulation (BPS) results. Because two different approaches to generate the solar radiation data are used, we evaluate the global horizontal irradiation data in the two latest versions of recent Brazilian TMY databases against the corresponding satellite-derived ones obtained from the POWER database (NASA). Simultaneously, as another alternative approach, global solar radiation data are calculated for the same studied locations and period through the modeling method used to generate the current version of the International Weather for Energy Calculations (IWEC2), and its performance is also compared against the corresponding reanalysis data (POWER). Finally, a set of case studies applying the local building performance regulations are exhaustively analyzed to quantify the impact of the uncertainty of solar radiation models on BPS results throughout Brazil. The results indicate that the accuracy of solar radiation models can highly influence the resulting TMY configurations. These changes can drive differences up to 40% on the prediction of the ideal annual loads of the residential buildings while, regardless of design performance, differences lower than 10% are found for the commercial case studies in most locations. Conversely, the prediction of peak loads for cooling shows to be more sensitive to the climate data changes in the commercial buildings than in the residential ones.

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Application of machine learning to estimate building energy use intensities

Autores:
R.K. Veiga a, A.C. Veloso b, A.P. Melo a, R. Lamberts a
Resumo:

Information on building energy consumption and its characteristics is essential for carrying out benchmarking processes. However, currently a lack of data acts as a major barrier in this regard. To address this issue, the purpose of this study is to demonstrate the application of machine learning to estimate building energy use intensities of bank branches buildings located in Brazil. The methodology proposed in this study completed a data collection regarding the bank branch typology. Then, the archetype model and its fixed and variables inputs variables were defined to generate 48,000 samples that were simulated using EnergyPlus program. A Sobol sensitivity analysis was performed, showing that the lighting power density followed by the weather variable were found to be the most influential variables when estimating the energy consumption of the bank branches. Finally, a comparison between machine learning techniques were applied to train the predictive model. The Support Vector Machine achieved MAE and RMSE values of 3.16 and 4.45 kWh/m2.year, respectively, representing the most accurate model for benchmarking purposes. Due to the non-linearity among the variable parameters, optimizing sophisticated machine learning techniques significantly improved the model accuracy. The results are of great value, since the model developed can be used in future benchmarking throughout the country. The methodology showed high accuracy and could be extended to other typologies.

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