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Efficiency and Optimization of Buildings Energy Consumption: Volume II

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (20 March 2021) | Viewed by 29048

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Prof. Dr. José A. Orosa

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Department of Navigation Science and Marine Engineering, Universidade da Coruña, Paseo de Ronda, 51, 15011 A Coruña, Spain
Interests: marine engineering; energy; thermodynamics; naval architecture; sustainability
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Dear colleagues,

In 2007, the European commission began a process to define targets to be implemented in the near future and in a long-term period. The objective was to slow down climate change and energy consumption or to reduce the greenhouse gas emissions (GHG) by decreasing the use of fossil fuels, increasing renewables, and improving the efficiency of the systems that employ fossil fuels when they cannot be replaced. In particular, it must be highlighted that 45% of the EU’s GHG are from large-scale facilities in the power and industrial sectors, as well as the aviation sector, and that 55% of the EU’s GHG are from the remaining sectors such as housing, agriculture, waste, and transport (excluding aviation). The first period has recently finished (from 2007 to 2020) and was based on three key targets:

20% cut in greenhouse gas emissions (from 1990 levels);
20% of EU energy from renewables;
20% improvement in energy efficiency.

While most of these targets have not yet been reached at this stage, a clear tendency to reduce GHG can be observed. In consequence, new percentages of these same targets were fixed for the year 2030. It is common sense that short-term targets are not the best solution and only long-term planning will let us improve the actual situation. In consequence, a long-term strategy for 2050 was defined by the EU with the objective to be an economy with net-zero greenhouse gas emissions and take new steps looking for traditional and new ideas to be translated into proposals for all Parties to the Paris Agreement.

As commented before and shown in our previous Special Issue on “Efficiency and Optimization of Buildings Energy Consumption”, housing is one of the biggest energy consumption sectors, and renewable energies and energy efficiency are considered complementary activities to reduce the total energy consumption from fossil fuels. This new Special Issue aims to help EU Member States to develop their national long-term strategies by sharing original ideas and real case studies to reduce their energy consumption in buildings centered on contemporary, new technologies.

Prof. Dr. José A. Orosa
Guest Editor

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Keywords

Building construction
Energy saving
New energy technologies
Optimization
Building design
Energy consumption
Zero-energy buildings
Sustainable buildings

Published Papers (11 papers)

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Editorial

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2 pages, 190 KiB

Open AccessEditorial

Efficiency and Optimization of Buildings Energy Consumption Volume II

by José A. Orosa

Appl. Sci. 2023, 13(1), 361; https://doi.org/10.3390/app13010361 - 27 Dec 2022

Viewed by 757

Abstract

This issue, as a continuation of a previous Special Issue on “Efficiency and Optimization of Buildings Energy Consumption,” gives an up-to-date overview of new technologies based on Machine Learning (ML) and Internet of Things (IoT) procedures to improve the mathematical approach of algorithms [...] Read more.

(This article belongs to the Special Issue Efficiency and Optimization of Buildings Energy Consumption: Volume II)

Research

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21 pages, 5032 KiB

Open AccessArticle

Energy Saving Strategies and On-Site Power Generation in a University Building from a Tropical Climate

by Jaqueline Litardo, Massimo Palme, Rubén Hidalgo-León, Fernando Amoroso and Guillermo Soriano

Appl. Sci. 2021, 11(2), 542; https://doi.org/10.3390/app11020542 - 08 Jan 2021

Cited by 14 | Viewed by 2886

Abstract

This paper compares the potential for building energy saving of various passive and active strategies and on-site power generation through a grid-connected solar photovoltaic system (SPVS). The case study is a student welfare unit from a university campus located in the tropical climate (Aw) of Guayaquil, Ecuador. The proposed approach aims to identify the most effective energy saving strategy for building retrofit in this climate. For this purpose, we modeled the base line of the building and proposed energy saving scenarios that were evaluated independently. All building simulations were done in OpenStudio-EnergyPlus, while the on-site power generation was carried out using the Homer PRO software. Results indicated that the incorporation of daylighting controls accounted for the highest energy savings of around 20% and 14% in total building energy consumption, and cooling loads, respectively. Also, this strategy provided a reduction of about 35% and 43% in total building energy consumption, and cooling loads, respectively, when combined with triple low-e coating glazing and active measures. On the other hand, the total annual electric energy delivered by the SPVS (output power converter) was 66,590 kWh, from where 48,497 kWh was supplied to the building while the remaining electricity was injected into the grid. Full article

(This article belongs to the Special Issue Efficiency and Optimization of Buildings Energy Consumption: Volume II)

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17 pages, 1512 KiB

Open AccessArticle

Non-Intrusive Load Disaggregation Based on a Multi-Scale Attention Residual Network

by Liguo Weng, Xiaodong Zhang, Junhao Qian, Min Xia, Yiqing Xu and Ke Wang

Appl. Sci. 2020, 10(24), 9132; https://doi.org/10.3390/app10249132 - 21 Dec 2020

Cited by 3 | Viewed by 2013

Abstract

Non-intrusive load disaggregation (NILD) is of great significance to the development of smart grids. Current energy disaggregation methods extract features from sequences, and this process easily leads to a loss of load features and difficulties in detecting, resulting in a low recognition rate of low-use electrical appliances. To solve this problem, a non-intrusive sequential energy disaggregation method based on a multi-scale attention residual network is proposed. Multi-scale convolutions are used to learn features, and the attention mechanism is used to enhance the learning ability of load features. The residual learning further improves the performance of the algorithm, avoids network degradation, and improves the precision of load decomposition. The experimental results on two benchmark datasets show that the proposed algorithm has more advantages than the existing algorithms in terms of load disaggregation accuracy and judgments of the on/off state, and the attention mechanism can further improve the disaggregation accuracy of low-frequency electrical appliances. Full article

(This article belongs to the Special Issue Efficiency and Optimization of Buildings Energy Consumption: Volume II)

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18 pages, 3197 KiB

Open AccessArticle

Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models

by Miguel Martínez-Comesaña, Lara Febrero-Garrido, Enrique Granada-Álvarez, Javier Martínez-Torres and Sandra Martínez-Mariño

Appl. Sci. 2020, 10(24), 8968; https://doi.org/10.3390/app10248968 - 16 Dec 2020

Cited by 17 | Viewed by 2684

Abstract

The Heat Loss Coefficient (HLC) characterizes the envelope efficiency of a building under in-use conditions, and it represents one of the main causes of the performance gap between the building design and its real operation. Accurate estimations of the HLC contribute to optimizing the energy consumption of a building. In this context, the application of black-box models in building energy analysis has been consolidated in recent years. The aim of this paper is to estimate the HLC of an existing building through the prediction of building thermal demands using a methodology based on Machine Learning (ML) models. Specifically, three different ML methods are applied to a public library in the northwest of Spain and compared; eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR) and Multi-Layer Perceptron (MLP) neural network. Furthermore, the accuracy of the results is measured, on the one hand, using both CV(RMSE) and Normalized Mean Biased Error (NMBE), as advised by AHSRAE, for thermal demand predictions and, on the other, an absolute error for HLC estimations. The main novelty of this paper lies in the estimation of the HLC of a building considering thermal demand predictions reducing the requirement for monitoring. The results show that the most accurate model is capable of estimating the HLC of the building with an absolute error between 4 and 6%. Full article

(This article belongs to the Special Issue Efficiency and Optimization of Buildings Energy Consumption: Volume II)

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14 pages, 2416 KiB

Open AccessArticle

Evaluation of Energy Efficiency in Thermally Improved Residential Buildings, with a Weather Controlled Central Heating System. A Case Study in Poland

by Krzysztof Cieśliński, Sylwester Tabor and Tomasz Szul

Appl. Sci. 2020, 10(23), 8430; https://doi.org/10.3390/app10238430 - 26 Nov 2020

Cited by 12 | Viewed by 2173

Abstract

Optimization of energy consumption and related energy efficiency can be realized in various ways, both through measures to reduce heat losses through building partitions and the introduction of modern systems of regulation and management of heat distribution. In order to achieve the best possible results, these actions should be interlinked, especially in older buildings that have undergone thermomodernization. Therefore, the aim of the study was to evaluate actions aimed at improving energy efficiency of buildings made in prefabricated technology. These buildings were thermomodernized and then the weather-controlled central heating system was installed. The study assessed whether the application of the change of the method of central heating regulation from the traditional one, taking into account only the change of external temperature to the weather-controlled one, will contribute to the increase of energy efficiency of buildings. The research was carried out in the existing residential buildings, for which data on the actual energy consumption was collected and elaborated and includes periods before modernization, after thermomodernization and the period after the introduction of the central heating system with weather control. The collected data cover an eighteen-year period of buildings’ use. The obtained results indicate that in Polish conditions the introduction of weather-controlled regulation system in buildings made in prefabricated technology (made of large slab) allows to achieve energy savings in the range of 16–23%, it may be related to their high thermal capacity resulting from the use of concrete elements in the building envelope. Full article

(This article belongs to the Special Issue Efficiency and Optimization of Buildings Energy Consumption: Volume II)

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22 pages, 1813 KiB

Open AccessArticle

Design and Application of Cellular Concrete on a Mexican Residential Building and Its Influence on Energy Savings in Hot Climates: Projections to 2050

by Ana C. Borbon-Almada, Jorge Lucero-Alvarez, Norma A. Rodriguez-Muñoz, Manuel Ramirez-Celaya, Samuel Castro-Brockman, Nicolas Sau-Soto and Mario Najera-Trejo

Appl. Sci. 2020, 10(22), 8225; https://doi.org/10.3390/app10228225 - 20 Nov 2020

Cited by 5 | Viewed by 3290

Abstract

The thermal performance of economical housing located in hot climates remains a pending subject, especially in emerging economies. A cellular concrete mixture was designed, considering its thermophysical properties, to apply the new material into building envelopes. The proposed materials have low density and thermal conductivity to be used as a nonstructural lightweight construction element. From the design stage, a series of wall systems based on cellular concrete was proposed. Whereas in the second phase, the materials were analyzed to obtain the potential energy savings using dynamic simulations. It is foreseen that the energy consumption in buildings located in these climates will continue to increase critically due to the temperature increase associated with climate change. The temperatures predicted mean vote (PMV), electric energy consumption, and CO₂ emissions were calculated for three IPCC scenarios. These results will help to identify the impact of climate change on the energy use of the houses built under these weather conditions. The results show that if the conventional concrete blocks continue to be used, the air conditioning energy requirements will increase to 49% for 2030 and 61% by 2050. The proposed cellular concrete could reduce energy consumption between 15% and 28%, and these saving rates would remain in the future. The results indicate that it is necessary to drive the adoption of lightweight materials, so the impact of energy use on climate change can be reduced. Full article

(This article belongs to the Special Issue Efficiency and Optimization of Buildings Energy Consumption: Volume II)

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24 pages, 5727 KiB

Open AccessArticle

Artificial Intelligence, Accelerated in Parallel Computing and Applied to Nonintrusive Appliance Load Monitoring for Residential Demand-Side Management in a Smart Grid: A Comparative Study

by Yu-Chen Hu, Yu-Hsiu Lin and Chi-Hung Lin

Appl. Sci. 2020, 10(22), 8114; https://doi.org/10.3390/app10228114 - 16 Nov 2020

Cited by 9 | Viewed by 2786

Abstract

A smart grid is a promising use-case of AIoT (AI (artificial intelligence) across IoT (internet of things)) that enables bidirectional communication among utilities that arises with demand response (DR) schemes for demand-side management (DSM) and consumers that manage their power demands according to received DR signals. Disaggregating composite electric energy consumption data from a single minimal set of plug-panel current and voltage sensors installed at the electric panel in a practical field of interest, nonintrusive appliance load monitoring (NIALM), a cost-effective load disaggregation approach for (residential) DSM, is able to discern individual electrical appliances concerned without accessing each of them by individual plug-load power meters (smart plugs) deployed intrusively. The most common load disaggregation approaches are based on machine learning algorithms such as artificial neural networks, while approaches based on evolutionary computing, metaheuristic algorithms considered as global optimization and search techniques, have recently caught the attention of researchers. This paper presents a genetic algorithm, developed in consideration of parallel evolutionary computing, and aims to address NIALM, whereby load disaggregation from composite electric energy consumption data is declared as a combinatorial optimization problem and is solved by the algorithm. The algorithm is accelerated in parallel, as it would involve large amounts of NIALM data disaggregated through evolutionary computing, chromosomes, and/or evolutionary cycles to dominate its performance in load disaggregation and excessively cost its execution time. Moreover, the evolutionary computing implementation based on parallel computing, a feed-forward, multilayer artificial neural network that can learn from training data across all available workers of a parallel pool on a machine (in parallel computing) addresses the same NIALM/load disaggregation. Where, a comparative study is made in this paper. The presented methodology is experimentally validated by and applied on a publicly available reference dataset. Full article

(This article belongs to the Special Issue Efficiency and Optimization of Buildings Energy Consumption: Volume II)

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16 pages, 2509 KiB

Open AccessArticle

Prediction of Building’s Thermal Performance Using LSTM and MLP Neural Networks

by Miguel Martínez Comesaña, Lara Febrero-Garrido, Francisco Troncoso-Pastoriza and Javier Martínez-Torres

Appl. Sci. 2020, 10(21), 7439; https://doi.org/10.3390/app10217439 - 23 Oct 2020

Cited by 27 | Viewed by 3131

Abstract

Accurate prediction of building indoor temperatures and thermal demand is of great help to control and optimize the energy performance of a building. However, building thermal inertia and lag lead to complex nonlinear systems is difficult to model. In this context, the application of artificial neural networks (ANNs) in buildings has grown considerably in recent years. The aim of this work is to study the thermal inertia of a building by developing an innovative methodology using multi-layered perceptron (MLP) and long short-term memory (LSTM) neural networks. This approach was applied to a public library building located in the north of Spain. A comparison between the prediction errors according to the number of time lags introduced in the models has been carried out. Moreover, the accuracy of the models was measured using the CV(RMSE) as advised by AHSRAE. The main novelty of this work lies in the analysis of the building inertia, through machine learning algorithms, observing the information provided by the input of time lags in the models. The results of the study prove that the best models are those that consider the thermal lag. Errors below 15% for thermal demand and below 2% for indoor temperatures were achieved with the proposed methodology. Full article

(This article belongs to the Special Issue Efficiency and Optimization of Buildings Energy Consumption: Volume II)

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25 pages, 15774 KiB

Open AccessArticle

Affordable, Energy-Efficient Housing Design for Chile: Achieving Passivhaus Standard with the Chilean State Housing Subsidy

by Aner Martinez-Soto, Yarela Saldias-Lagos, Valentina Marincioni and Emily Nix

Appl. Sci. 2020, 10(21), 7390; https://doi.org/10.3390/app10217390 - 22 Oct 2020

Cited by 6 | Viewed by 2877

Abstract

In Chile, it is estimated that the energy demand will continue to increase if substantial energy efficiency measures in housing are not taken. These measures are generally associated with technical and mainly economic difficulties. This paper aims to show the technical and economic feasibility of achieving Passivhaus standard house in Chile, considering the budget of the maximum state subsidy currently available (Chilean Unidad de Fomento (CLF) 2000 ≈ 81,000 USD). The design was simulated in the Passive House Planning Package software to determine if the house could be certified with the selected standard. At the same time, the value of all the items was quantified in order not to exceed the stipulated maximum budget for a house considered as affordable. It was shown that in terms of design it is possible to implement the Passivhaus standard given the current housing subsidy. The designed housing ensures a reduction of 85% in heating demand and a 60% reduction in CO₂ emissions during the operation, compared to an average typical Chilean house. Full article

(This article belongs to the Special Issue Efficiency and Optimization of Buildings Energy Consumption: Volume II)

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13 pages, 1480 KiB

Open AccessArticle

Air Changes for Healthy Indoor Ambiences under Pandemic Conditions and Its Energetic Implications: A Galician Case Study

by José A. Orosa, Modeste Kameni Nematchoua and Sigrid Reiter

Appl. Sci. 2020, 10(20), 7169; https://doi.org/10.3390/app10207169 - 14 Oct 2020

Cited by 8 | Viewed by 1911

Abstract

The present paper aims to show a mathematical understanding of the effect of ventilation rate over building energy consumption. Moreover, as a case study to show this methodology, a proposal was analyzed of modifying the teaching period to reach a maximum increase of air changes in school buildings, to allow adherence to the COVID-19 pandemic requirements in the Galicia region, with lower energy consumption. In this sense, to analyze the energetic implication of this proposal, the building construction was defined, modeled in accordance with the ISO Standard 13790 and implemented in accordance with the Monte Carlo method. Results showed the probability of energy consumption as a Weibull model. Furthermore, a map of different Weibull models in accordance with different ventilation rates was developed. The constants of the Weibull models allow to identify normal distributions of the probability density functions of energy consumption, especially the ones with lower energy consumption. As a consequence, these constants are a better parameter to identify the optimal ventilation rate for each season in search of a healthy indoor ambience, which is of interest for a future design guide. Finally, the main results showed a reduction of energy consumption at a higher ventilation rate in the summer season. As a consequence, the necessity of modifying teachings periods, as an adequate procedure to prevent more COVID infections, is concluded. Full article

(This article belongs to the Special Issue Efficiency and Optimization of Buildings Energy Consumption: Volume II)

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Review

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17 pages, 591 KiB

Open AccessReview

Hybrid Techniques to Predict Solar Radiation Using Support Vector Machine and Search Optimization Algorithms: A Review

by José Manuel Álvarez-Alvarado, José Gabriel Ríos-Moreno, Saul Antonio Obregón-Biosca, Guillermo Ronquillo-Lomelí, Eusebio Ventura-Ramos, Jr. and Mario Trejo-Perea

Appl. Sci. 2021, 11(3), 1044; https://doi.org/10.3390/app11031044 - 24 Jan 2021

Cited by 46 | Viewed by 3579

Abstract

The use of intelligent algorithms for global solar prediction is an ideal tool for research focused on the use of solar energy. Forecasting solar radiation supports different applications focused on the generation and transport of energy in places where there are no meteorological stations. Different solar radiation prediction techniques have been applied in different time horizons, such as neural networks (ANN) or machine learning (ML), with the latter being the most important. The support vector machine (SVM) is a classification method of the ML that is used to predict solar radiation. To obtain a better accuracy of prediction data, search optimization algorithms (SOA) such as genetic algorithms (GA) and the particle swarm optimization algorithm (PSO) were used to optimize the prediction accuracy by searching the model parameters. This article presents a review of different hybrid SVM models with SOA applied to obtain the best parameters to reduce the prediction error of solar radiation using meteorological variables. Research articles from the last 5 years on solar radiation prediction using SVM models and hybrid SMV optimized models with SOA were studied. The results show that SVM with GA presents a better performance than the classical SVM models using the Radial basis kernel function for prediction parameters. Full article

(This article belongs to the Special Issue Efficiency and Optimization of Buildings Energy Consumption: Volume II)

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