•The appliances energy consumption prediction in a low energy house has been studied.•Weather data from a nearby station was found to improve the prediction.•Pressure, air temperature and wind speed ...are important parameters in the prediction.•Data from a WSN that measures temperature and humidity increase the pred. accuracy.•From the WSN, the kitchen, laundry and living room data ranked high in importance.
This paper presents and discusses data-driven predictive models for the energy use of appliances. Data used include measurements of temperature and humidity sensors from a wireless network, weather from a nearby airport station and recorded energy use of lighting fixtures. The paper discusses data filtering to remove non-predictive parameters and feature ranking. Four statistical models were trained with repeated cross validation and evaluated in a testing set: (a) multiple linear regression, (b) support vector machine with radial kernel, (c) random forest and (d) gradient boosting machines (GBM). The best model (GBM) was able to explain 97% of the variance (R2) in the training set and with 57% in the testing set when using all the predictors. From the wireless network, the data from the kitchen, laundry and living room were ranked the highest in importance for the energy prediction. The prediction models with only the weather data, selected the atmospheric pressure (which is correlated to wind speed) as the most relevant weather data variable in the prediction. Therefore, atmospheric pressure may be important to include in energy prediction models and for building performance modeling.
Fig. 6. Occupancy CART model for temperature, humidity, light, CO2 and humidity ratio.
•The accuracy of occupancy detection of a room from sensors data was evaluated.•High accuracies were found when ...using LDA, CART and RF models.•Two features combinations are good enough for high accuracies.•Using all the features may decrease the accuracy of the prediction.•The data sets together with the processing scripts are available for download.
The accuracy of the prediction of occupancy in an office room using data from light, temperature, humidity and CO2 sensors has been evaluated with different statistical classification models using the open source program R. Three data sets were used in this work, one for training, and two for testing the models considering the office door opened and closed during occupancy. Typically the best accuracies (ranging from 95% to 99%) are obtained from training Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART) and Random Forest (RF) models. The results show that a proper selection of features together with an appropriate classification model can have an important impact on the accuracy prediction. Information from the time stamp has been included in the models, and usually it increases the accuracy of the detection. Interestingly, using only one predictor (temperature) the LDA model was able to estimate the occupancy with accuracies of 85% and 83% in the two testing sets.
•A simple methodology based on HMM for unsupervised occupancy detection was presented.•The evaluation of the accuracies of the prediction using different data inputs was presented.•The methodology is ...used to infer average occupancy profiles of different rooms in a residential building.•The inferred schedules based on this methodology can be used for understanding average occupancy schedules.
This paper presents and evaluates a simple methodology based on Hidden Markov models for the problem of unsupervised occupancy detection using the open source program R. The models were created using different environmental parameters such as temperature, humidity, humidity ratio, CO2 and light time series data and were evaluated against ground truth occupancy from a public data set. The accuracies of the models are reported. Also, as a case study, the developed methodology is applied for humidity ratio data calculated from temperature and humidity measured in different rooms (kitchen, living room, office, parents’ room, teenager’s room, laundry room, ironing room and bathroom) in a low energy residential building to infer daily and hourly average occupancy schedules for which there is no ground truth data. The estimated occupancy schedules are commented on by one of the house occupants and discussed. Inferred schedules found with this method could be useful for understanding average occupancy schedules, for detecting regular activities or actions and as an input for residential building energy simulations.
Open-loop building-integrated photovoltaic/thermal (BIPV/T) systems with air as the heat transfer fluid can supply a substantial portion of the space heating and hot water needs of residential and ...commercial buildings in cold climates. Over the last few years, several customized mathematical models for these systems have been developed. This paper presents a more general model useful for design or control purposes which allows for steady-state or transient analysis. Steady state models provide a quick evaluation of the energy balance and system performance useful for design. Transient models provide more insight valuable for development of control algorithms and system design optimization.
This paper analyses the performance of the Xbee-PRO IEEE 802.15.4 (ZigBee protocol) based Wireless Area Network transmission component for urban environment, as part of the Smart City concept. This ...embedded solution was designed to provide a wireless networking layer for Machine-2-Machine (M2M) communication. A series of carefully designed field comparison tests in a “0” obstruction environment and a real urban area was conducted using three-dimensional positioned nodes. The objective of the study was to simulate the potential applications and expose the cases where this technology cannot be applied. The obtained results reveal significant deviation from the technical manufacturer specifications when applied in the actual field measurement environment.
Photovoltaic–thermal (PV/T) collectors provide renewable energy, and they are instrumental to achieve grid independency. A subset of these collectors is collectors that are integrated into building ...envelope. The so-called “building-integrated” PV/T collectors have seen a dramatic rise in popularity recently. This recent popularity has necessitated systematic design optimization. To benefit design optimization, a review of computer models of these collectors was performed. This review was performed by objectively assessing international findings on roof-integrated and curtain-wall integrated PV/T collectors. The scope of this review was thermal collection efficiency. The significance of this review was to identify the weakest link in computer models that should one day lead to more accurate computer models. This weak link is the internal heat transfer rate. To overcome this weakness, a model calibration method was proposed that is based on case-by-case parameter identification. In addition, a detailed dimensional analysis was performed that allowed a new Π group to be introduced to Nusselt (Nu) number correlations of developing, turbulent parallel-plate flow. This Π group is the Stanton number as applied to the inter-channel radiative heat transfer coefficient (Str). Commonly implemented Nu number correlations do not account for this heat transfer rate. They only account for collector geometry, collector air flow inertia and collector air viscosity.
Lynch syndrome (LS) is the most frequent cancer predisposition syndrome affecting the colon and rectum. A pathogenic variant (PV) disrupting one of the mismatch repair (MMR) genes is responsible for ...the disease. The spectrum of tumors in LS is heterogeneous and includes cancer of the colon and rectum (CRC), endometrium, ovaries, stomach, small bowel, urinary tract, bladder, pancreas, and skin. Knowledge of the phenotypic variation of patients with LS, the type and frequency of PVs, and cascade testing studies in the Latin American population is limited. The present study aims to recognize the PVs in MMR genes, describe the phenotype in Mexican-Mestizo patients and their relatives, and identify the acceptance rate of cascade testing of relatives at risk. We included 40 carriers of a MMR gene PV and 142 relatives that developed a LS-related neoplasm. Patients’ clinical data, number, and type of malignancies were obtained from their medical records. Amsterdam I-II, Bethesda criteria, and PREMM5® predictive model score were estimated. Available immunohistochemistry (IHC) reports were analyzed. Relatives at risk were determined from index cases pedigrees. The distribution of MMR gene mutations among 40 probands was: MLH1 (67.5 %), MSH2 (22.5 %), MSH6 (7.5 %), and PMS2 (2.5 %). Out of the 182 LS cases, 58 % exhibited the LS phenotype before age 50. The most common tumor was CRC, followed by endometrial cancer in women and gastric cancer in males. We found a 90.0 % concordance between the IHC and germline PV. The most frequent PV in our sample was MLH1 c.676C > T, occurring in 1/6 index cases. All probands disclosed their molecular test result to their family. Out of the 451 asymptomatic relatives at risk, 28.2 % underwent germline testing. Our results highlight the importance of conducting germline genetic studies in LS since it allows the establishment of appropriate cancer screening, risk-reducing measures, and genetic cascade testing among relatives at risk. Interestingly, we observed a significantly higher prevalence of the c.676C > T variant in MLH1, probably a singular characteristic of the Mexican-Mestizo population. New strategies to facilitate accurate communication between index cases and relatives should be implemented to improve the cascade testing acceptance rate.
Abstract Gastrointestinal stromal tumors are a heterogeneous group with a wide spectrum of histologic features. We describe the first case of 61-year-old woman who presented gastrointestinal stromal ...tumors of the ampulla of Vater with osteoclast-like giant cells surrounding osteoid-like material and aneurismal bone cyst-like areas. The phenotype was supported by light microscopy and corroborated by immunohistochemistry analysis. Because of the presence of osteoid-like and aneurismal bone cyst-like components, it is first necessary to make differential diagnosis with other entities such as metastatic osteosarcoma. Our case shows another form of differentiation that has not previously been reported.
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