Monitoring and modeling of household air quality related to use of different Cookfuels in Paraguay.

In Paraguay, 49% of the population depends on biomass (wood and charcoal) for cooking. Residential biomass burning is a major source of fine particulate matter (PM2.5 ) and carbon monoxide (CO) in and around the household environment. In July 2016, cross-sectional household air pollution sampling was conducted in 80 households in rural Paraguay. Time-integrated samples (24 hours) of PM2.5 and continuous CO concentrations were measured in kitchens that used wood, charcoal, liquefied petroleum gas (LPG), or electricity to cook. Qualitative and quantitative household-level variables were captured using questionnaires. The average PM2.5 concentration (μg/m3 ) was higher in kitchens that burned wood (741.7 ± 546.4) and charcoal (107.0 ± 68.6) than in kitchens where LPG (52.3 ± 18.9) or electricity (52.0 ± 14.8) was used. Likewise, the average CO concentration (ppm) was higher in kitchens that used wood (19.4 ± 12.6) and charcoal (7.6 ± 6.5) than in those that used LPG (0.5 ± 0.6) or electricity (0.4 ± 0.6). Multivariable linear regression was conducted to generate predictive models for indoor PM2.5 and CO concentrations (predicted R2  = 0.837 and 0.822, respectively). This study provides baseline indoor air quality data for Paraguay and presents a multivariate statistical approach that could be used in future research and intervention programs

Machine learning-based estimation of buildings' characteristics employing electrical and chilled water consumption data: Pipeline optimization

Smart meter-driven remote auditing of buildings, as an alternative to the labor-intensive on-site visits, permits large-scale and rapid identification of buildings with low energy performance. The existing literature has mainly focused on electricity meters' data from a rather small set of buildings and efforts have often not been made to facilitate the models' physical interpretability. Accordingly, the present work focuses on the implementation and optimization of ML-based pipelines for building characterization (by use type (A), performance class (B), and operation group (C)) employing hourly electrical and chilled-water consumption data. Utilizing the Building Data Genome Project II dataset (with data from 1636 buildings), feature generation, feature selection, and pipeline optimization steps are performed for each pipeline. Results demonstrate that performing the latter two steps improves the model's accuracy (5.3%, 2.9%, and 3.9% for pipelines A, B, and C compared to a benchmark model), while notably reduces the number of utilized features (94.7%, 88.3%, 89.4%), enhancing the models' interpretability. Furthermore, adding features extracted from chilled-water consumption data boosts the accuracy (with respect to baseline) for the second subset by 12.4%, 13.5%, and 7.2%, while decreasing the feature count by 97.2%, 96.4%, and 96.5%, respectively.publishedVersio

Data Driven Building Electricity Consumption Model Using Support Vector Regression

Every building has certain electricity consumption patterns that depend on its usage. Building electricity budget planning requires a consumption forecast to determine the baseline electricity load and to support energy management decisions. In this study, an algorithm to model building electricity consumption was developed. The algorithm is based on the support vector regression (SVR) method. Data of electricity consumption from the past five years from a selected building object in ITB campus were used. The dataset unexpectedly exhibited a large number of anomalous points. Therefore, a tolerance limit of hourly average energy consumption was defined to obtain good quality training data. Various tolerance limits were investigated, that is 15% (Type 1), 30% (Type 2), and 0% (Type 0). The optimal model was selected based on the criteria of mean absolute percentage error (MAPE) < 20% and root mean square error (RMSE) < 10 kWh. Type 1 data was selected based on its performance compared to the other two. In a real implementation, the model yielded a MAPE value of 14.79% and an RMSE value of 7.48 kWh when predicting weekly electricity consumption. Therefore, the Type 1 data-based model could satisfactorily forecast building electricity consumption