•This study develops ensemble machine learning to predict building cooling loads.•Designing energy-efficient buildings with the aid of machine learning.•The model exhibits good agreement with the ...physics-based simulation tool.•The model can predict accurately and quickly cooling loads in early design stage.•The model facilitates designers in designing energy-efficient buildings.
Energy-efficient building design has become imperative for energy conservation, emissions reduction, and life quality enhancement of occupant. Physics-based whole building energy simulation is widely used to access building energy performance, which requires large amount of information to specify values of input parameters and includes underlying assumptions. This study proposed an alternative model based on machine learning (ML) to predict cooling loads of buildings with few common parameters in the design phase. The ML models were developed and evaluated using a dataset of 243 buildings. Predicted cooling loads from these models were compared to those from the physics-based whole building energy simulation. The proposed model exhibits good agreement with the physics-based whole building energy simulation. The analytical results present that the ML models obtained the correlation coefficient (R) of 0.98–0.99, the mean absolute percentage error (MAPE) of 6.17–12.93% which shows the high agreement between observed and predicted values of cooling loads in building. Notably, the ensemble bagging artificial neural networks yielded the highest R of 0.99, the lowest root-mean-square error (RMSE) of 158.77 kW, the lowest MAE of 112.07 kW, and the lowest MAPE of 6.17% among all models in this study. This research contributes to (i) the state of the knowledge by examining various ML models that can predict cooling loads in the early building design stage; and (ii) the state of practice by providing an alternative tool in the design process through better understanding of relationships between building cooling loads and building characteristics for enhancing energy efficiency in buildings.
Buildings must be energy efficient and sustainable because buildings have contributed significantly to world energy consumption and greenhouse gas emission. Predicting energy consumption patterns in ...buildings is beneficial to utility companies, users, and facility managers because it can help to improve energy efficiency. This work proposed a Random Forests (RF) – based prediction model to predict the short-term energy consumption in the hourly resolution in multiple buildings. Five one-year datasets of hourly building energy consumption were used to examine the effectiveness of the RF model throughout the training and test phases. The evaluation results presented that the RF model exhibited a good prediction accuracy in the prediction. In four evaluation scenarios, the mean absolute error (MAE) values ranged from 0.430 to 0.501 kWh for the 1-step-ahead prediction, from 0.612 to 0.940 kWh for the 12-steps-ahead prediction, and from 0.626 to 0.868 kWh for the 24-steps-ahead prediction. The RF model was superior to the M5P and Random Tree (RT) models. The RF was better about 49.21%, 46.93% in the MAE and mean absolute percentage error (MAPE) than the RT model in forecasting 1-step-ahead building energy consumption. The RF model approved the outstanding performance with the improvement of 49.95% and 29.29% in MAE compared to the M5P model in the 12-steps-ahead, and 24-steps-ahead energy use, respectively. Thus, the proposed RF model was an effective prediction model among the investigated machine learning (ML) models. This study contributes to (i) the state of the knowledge by examining the generalization and effectiveness of ML models in predicting building energy consumption patterns; and (ii) the state of practice by proposing an effective tool to help the building owners and facility managers in understanding building energy performance for enhancing the energy efficiency in buildings.
•This study proposes a random forests model to predict short-term building energy use.•Efficient building energy management is aided by machine learning.•Random forests can predict accurately and effectively the hourly building energy consumption.•The model supports facility managers for enhancing energy efficiency in buildings.
Methodology was developed to expand the range of benign alkyl glycoside surfactants to include also anionic types. This was demonstrated possible through conversion of the glycoside to its carboxyl ...derivative. Specifically, octyl β-D-glucopyranoside (OG) was oxidised to the corresponding uronic acid (octyl β-D-glucopyranoside uronic acid, OG-COOH) using the catalyst system
T. versicolor
laccase/2,2,6,6-tetramethylpiperidinyloxy (TEMPO) and oxygen from air as oxidant. The effects of oxygen supply methodology, concentrations of laccase, TEMPO and OG as well as reaction temperature were evaluated. At 10 mM substrate concentration, the substrate was almost quantitatively converted into product, and even at a substrate concentration of 60 mM, 85% conversion was reached within 24 h. The surfactant properties of OG-COOH were markedly dependent on pH. Foaming was only observed at low pH, while no foam was formed at pH values above 5.0. Thus, OG-COOH can be an attractive low-foaming surfactant, for example for cleaning applications and emulsification, in a wide pH range (pH 1.5–10.0).
The present study proposes a framework for the continuous Bayesian calibration of whole building energy simulation (BES) models utilizing data from building information models (BIM) and building ...energy management systems (BEMS). The ability to import data from BIM and BEMS provides the potential to significantly reduce the time and effort needed for the continuous calibration of BES models. First, five gbXML geometric test cases were used to check the BIM to BES model translation. Translation of the test cases indicates good geometric agreement between the native BIM and the gbXML-based BES model. An actual building calibration case study (with BIM and three years of monthly electrical energy consumption data) was then used to evaluate the proposed continuous calibration method. The results suggest that compared to a non-continuous approach, the continuous Bayesian calibration method showed reduced prediction uncertainty and improved prediction accuracy on a test dataset. The paper also presents information and comparison of the coefficient of variance of the root mean square error (CVRMSE) and the normalized mean biased error (NMBE), recommending looking at their distributions when working with probabilistic BES predictions.
•This study develops a novel time-series sliding window forecast system.•The system integrates metaheuristics, machine learning and time-series models.•Site experiment of smart grid infrastructure is ...installed to retrieve real-time data.•The proposed system accurately predicts energy consumption in residential buildings.•The forecasting system can help users minimize their electricity usage.
Smart grids are a promising solution to the rapidly growing power demand because they can considerably increase building energy efficiency. This study developed a novel time-series sliding window metaheuristic optimization-based machine learning system for predicting real-time building energy consumption data collected by a smart grid. The proposed system integrates a seasonal autoregressive integrated moving average (SARIMA) model and metaheuristic firefly algorithm-based least squares support vector regression (MetaFA-LSSVR) model. Specifically, the proposed system fits the SARIMA model to linear data components in the first stage, and the MetaFA-LSSVR model captures nonlinear data components in the second stage. Real-time data retrieved from an experimental smart grid installed in a building were used to evaluate the efficacy and effectiveness of the proposed system. A k-week sliding window approach is proposed for employing historical data as input for the novel time-series forecasting system. The prediction system yielded high and reliable accuracy rates in 1-day-ahead predictions of building energy consumption, with a total error rate of 1.181% and mean absolute error of 0.026kWh. Notably, the system demonstrates an improved accuracy rate in the range of 36.8–113.2% relative to those of the linear forecasting model (i.e., SARIMA) and nonlinear forecasting models (i.e., LSSVR and MetaFA-LSSVR). Therefore, end users can further apply the forecasted information to enhance efficiency of energy usage in their buildings, especially during peak times. In particular, the system can potentially be scaled up for using big data framework to predict building energy consumption.
Geogrids are commonly used in railway construction for reinforcement and stabilisation. When railway ballast becomes fouled due to ballast breakage, infiltration of coal fines, dust and subgrade soil ...pumping, the reinforcement effect of geogrids decreases significantly. This paper presents results obtained from Discrete Element Method (DEM) to study the interface behaviour of coal-fouled ballast reinforced by geogrid subjected to direct shear testing. In this study, irregularly-shaped aggregates (ballast) were modelled by clumping together 10–20 spheres in appropriate sizes and positions. The geogrid was modelled by bonding a large number of small spheres together to form the desired grid geometry and apertures. Fouled ballast with 40% Void Contaminant Index (VCI) was modelled by injecting a predetermined number of miniature spheres into the voids of fresh ballast. A series of direct shear tests for fresh and fouled ballast reinforced by the geogrid subjected to normal shear stresses varying from 15kPa to 75kPa were then simulated in the DEM. The numerical results showed a good agreement the laboratory data, indicating that the DEM model is able to capture the behaviour of both fresh and coal-fouled ballast reinforced by the geogrid. The advantages of the proposed DEM model in terms of capturing the correct stress–displacement and volumetric behaviour of ballast, as well as the contact forces and strains developed in the geogrids are discussed.
•A computational optimization approach is proposed to support adaptive façade design.•Two case studies are used to validate the capacity of the proposed approach.•The effects of the adaptive façade ...system are analyzed and discussed.•The proposed approach could reduce energy consumption by 14.2–29.0%.•The study facilitates the exploration of next-generation adaptive façade concepts.
The energy consumption in buildings, which accounts for approximately one-third of the total energy used in the world, can be reduced significantly by employing adaptive façades. In this study, a computational optimization approach is proposed to enhance the energy efficiency of buildings based on the design of an adaptive façade system, which can adapt its thermal and visible transmittance for dynamically varying climatic conditions. The engine of the adaptive façade design approach is an automated optimization process, which combines the building energy simulation program (EnergyPlus) with an optimization technique through Eppy, a powerful Python toolkit. The modified firefly algorithm, an in-house optimization tool, is employed to design the adaptive façade system in this study. However, our proposed method is not tied to any particular optimization tool and does not impose any restrictions on a type of building. To this end, the capability of the proposed method for enhancing building energy efficiency is validated by two case studies, namely a typical single office room and a medium office building. We found that the proposed adaptive façade system can reduce the energy consumption by 14.9–29.0% and 14.2–22.3% for the first and second case study, respectively, compared to the static façades. These significant findings demonstrate the potential of adaptive façades to enhance the energy efficiency of buildings.
The building sector is the largest energy consumer accounting for 40% of global energy usage. An energy forecast model supports decision-makers to manage electric utility management. Identifying ...optimal values of hyperparameters of prediction models is challenging. Therefore, this study develops a novel time-series Wolf-Inspired Optimized Support Vector Regression (WIO-SVR) model to predict 48-step-ahead energy consumption in buildings. The proposed model integrates the support vector regression (SVR) and the grey wolf optimizer (GWO) in which the SVR model serves as a prediction engine while the GWO is used to optimize the hyperparameters of the SVR model. The 30-min energy data from various buildings in Vietnam were adopted to validate model performance. Buildings include one commercial building, one hospital building, three authority buildings, three university buildings, and four office buildings. The dataset is divided into the learning data and the test data. The performance of the WIO-SVR was superior to baseline models including the SVR, random forests (RF), M5P, and decision tree learner (REPTree). The WIO-SVR model obtained the highest value of correlation coefficient (R) with 0.90. The average root-mean-square error (RMSE) of the WIO-SVR was 2.02 kWh which was more accurate than those of the SVR model with 10.95 kWh, the RF model with 16.27 kWh, the M5P model with 17.73 kWh, and the REPTree model with 26.44 kWh. The proposed model improved 442.0-1207.9% of the predictive accuracy in RMSE. The reliable WIO-SVR model provides building managers with useful references in efficient energy management.
Fruit orchards in the Vietnamese Mekong Delta (VMD) are severely degraded due to many factors, such as low organic matter content, soil acidification, and poor soil management. Organic manures are ...considered to be a soil conservation measure that decreases soil degradation and acidity. This study aimed to evaluate the impacts of soil organic amendments on the improvement of soil fertility and pomelo productivity. Two soil amendments, namely, chicken manure (CM) and cow dung (CD), were investigated for a period of three years at three pomelo orchards. The soil quality was assessed in two depths (0–20 and 20–50 cm), including the soil pH, electrical conductivity (EC), total nitrogen (Ntot), available phosphorus (Pavail), soil organic matter (SOM), bulk density (BD), and exchangeable cations (Ca, Mg, and K). The results indicated that CD and CM improved soil fertility in topsoil layer (0–20 cm) due to an increase in soil pH, SOM, exchangeable Ca, Ntot, and Pavail. In addition, soil BD significantly reduced after CD and CM were supplied in the three consecutive years of study. The soil quality properties that significantly affected pomelo yield were SOM, Ntot, Pavail, and soil BD. Thus, these soil qualities may be considered as key factors for determining and assessing soil quality in fruit orchards in the VMD. More studies on the influence of organic manures on nutrient uptake and pomelo fruit quality are warranted.
Corrosion is a common deterioration that reduces the service life of concrete structures and steels. Particularly, corrosion behavior is a highly nonlinear problem influenced by complex ...characteristics. This study used advanced artificial intelligence (AI) techniques to predict pitting corrosion risk of steel reinforced concrete and marine corrosion rate of carbon steel. The AI-based models used for prediction included single and ensemble models constructed from four well-known machine learners including artificial neural networks (ANNs), support vector regression/machines (SVR/SVMs), classification and regression tree (CART), and linear regression (LR). Notably, a hybrid metaheuristic regression model was implemented by integrating a smart nature-inspired metaheuristic optimization algorithm (i.e., smart firefly algorithm) with a least squares SVR. Prediction accuracy was evaluated using two real-world datasets. According to the comparison results, the hybrid metaheuristic regression model was better than the single and ensemble models in predicting the pitting corrosion risk (mean absolute percentage error=5.6%) and the marine corrosion rate (mean absolute percentage error = 1.26%). The hybrid metaheuristic regression model is a promising and practical methodology for real-time tracking of corrosion in steel rebar. Civil engineers can use the hybrid model to schedule maintenance process that leads to risk reduction of structure failure and maintenance cost.
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•Artificial intelligence is used in modeling the pitting risk and corrosion rate of steel.•This study examines diverse novel prediction methods.•The hybrid metaheuristic regression model has superior prediction accuracy.•A scientific methodology for predicting pitting risk/corrosion rate in steel rebar.
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