Performance Evaluation of Three Virtual Metering Methods to Estimate Zone-level Perimeter Heater Energy Requirement

Virtual metering provides a cost-effective alternative to physical meters to monitor building energy performance and capture unmetered energy flows at the zone-level. Virtual metering accuracy depends on the modelling method and its ability to represent the heating and cooling processes at a building thermal zone. This paper employs three virtual metering methods to estimate the heating energy of zone-level perimeter heaters: a steady-state modelling method, a transient modelling method, and a load disaggregation modelling method. Inverse models representing these three virtual metering methods are trained using data obtained from seven perimeter offices in an academic building in Ottawa, Canada. Model parameters are identified using the genetic algorithm and used for creating virtual meters that estimate the energy requirement of zone-level perimeter heaters. The virtual meters\u27 accuracy is assessed by comparing the results to measured heating energy obtained from physical meters installed in the seven offices. The three virtual metering methods\u27 performance is evaluated through illustrative examples in terms of modelling assumptions, data requirements, and virtual metering accuracy. The results indicate that the three virtual metering methods can estimate the daily heating energy supplied by perimeter heaters at a normalized root-mean-square error between 13% and 23%

Generating design-sensitive occupant-related schedules for building performance simulations

Despite the benefits of occupant behavior (OB) models in simulating the effect of design factors on OB, there are challenges associated with their use in the building simulation industry due to extensive time and computational requirements. To this end, we present a novel method to incorporate these models in building performance simulations (BPS) as design-sensitive schedules. Over 2,900 design alternatives of an office were generated by varying orientation, window to wall ratio (WWR), the optical characteristics of windows and blinds, as well as indoor surfaces’ reflectance. By using daylight simulations and stochastic OB modeling, unique light use schedules were generated for each design alternative. A decision tree was then developed to be used by building designers to select light use schedules based on design parameters. These findings are relevant for building energy codes as they provide an approach to incorporate design-sensitive operational schedules for use as BPS inputs by practitioners. These design-sensitive schedules are expected to be superior to default ones currently specified in codes and standards, which ignore the effect of design factors on OB, and ultimately on energy consumption

Exploring occupants\u27 impact at different spatial scales

Buildings\u27 users have widely been accepted as a source of uncertainty in building energy performance predictions. However, it is not evident that the diversity of occupants\u27 presence and behavior at the building level is as important as at the room level. The questions are: How should occupants be modeled at different spatial scales? At the various scales of interest, how much difference does it make if: (1) industry standard assumptions or a dynamic occupant modeling approach is used in a simulation-based analysis, and (2) probabilistic or deterministic models are used for the dynamic modeling of occupants? This paper explores the reliability of building energy predictions and the ability to quantify uncertainty associated with occupant modeling at different scales. To this end, the impacts of occupancy and occupants\u27 use of lighting and window shades on the predicted building lighting energy performance at the room and building level are studied. The simulation results showed that the inter-occupant variation at larger scales is not as important as at the room level. At larger scales (about 100 offices), the rule-base model, custom schedule model, and stochastic lighting use model compared closely for predicting mean annual lighting energy use

Data-driven short-term load forecasting for heating and cooling demand in office buildings

Short-term forecasts of energy demand in buildings serve as key information for various operational schemes such as predictive control and demand response programs. Despite this, developing forecast models for heating and cooling loads has received little attention in the literature compared to models for electricity load. In this paper, we present data-driven approaches to forecast hourly heating and cooling energy use in office buildings based on temporal, autoregressive, and exogenous variables. The proposed models calculate hourly loads for a horizon between one hour and 12 hours ahead. Individual models based on artificial neural networks (ANN) and change-point models (CPM) as well as a hybrid of the two methods are developed. A case study is conducted based on hourly thermal load data collected from several office buildings located on the same campus in Ottawa, Canada. The models are trained with more than two years of hourly energy-use data and tested on a separate part of the dataset to enable unbiased validation. The results show that the ANN model can achieve higher forecasting accuracy for the longest forecast horizon and outperforms the results obtained by a Naïve approach and the CPM. However, the performance of the hybrid CPM-ANN method is superior compared to individual models for all studied buildings

Retrospectively Analysis of Clinical/Pathological and Prognostic Features of Subtypes of Breast Cancer

DergiPark: 379007tmsjAims: Breast cancer is the most common type of cancer among all women across the world, with an incidence of 25.2%. Of all the cancer cases, breast cancer comes second in line after lung cancer. By 6.4% it marks fifth place as the reason for cancer-related-deaths. Therefore new studies on breast cancer are required. We aimed to retrospectively analyze clinical, pathological and prognostic features of cases that were divided into four subgroups based on their hormone receptor and HER-2 conditions. Method: Records of GATA-Oncology Clinic patients who have been diagnosed with breast cancer within years of 2008-2014, were inspected retrospectively. Cases were divided into four subgroups based on their hormone receptor and HER-2 conditions. Missing records were primarily gathered by electronic recording system, also still-missing-information about the patients were provided via phone calls. Collected data has been evaluated with SPSS 15,0. Results: While demographics such as family history and menopausal state were not different among 4 subgroups, triple negative patients tended to have a lower body-mass index and mean age (p=009, p=0.041, respectively). Only 12 patients had advanced disease at diagnosis. A total of 168 patients received chemotherapy. Progression occurred in 41 patients (21.9%) from early phase breast cancer cases that were taken to adjuvant chemotherapy program. Family history had a significant association with recurrence in breast cancer patients (p=0.026). Menopausal state, lymphovascular invasion, lymph node state and stage were not associated with progression. Independent prognostic factors were not obtained with multivariate analysis for disease-free survival. Advanced stage breast cancer patients had a higher tendency to metastasis. Triple negative patients had more drug resistance towards systemic treatment than other subgroups (p lt;0.001). It has been found that full response to anthracycline + taxane regime was less in triple negative patients. Conclusion: In conclusion, there were some differences within our subgroups. Patients of these subgroups should be followed up and treated with different strategies. All subgroups, especially triple negative group, were in need of new effective therapy strategies

Extreme Calvarial and Upper Cervical Hyperpneumatization: A Case Report

The pneumatization of bones of cranial base other than the mastoid process and temporal bone is a pathologic and rare condition, and it may cause some serious complications. Extension of the pneumatization to the cranial vault and upper cervical bones is extremely rare. A 67 year-old man was admitted with complaint of chronic nonspecific headache for a long time. He had no history of head trauma or otologic infection. Physical examination not revealed fever, any palpable swelling, rhinorrhea or otorrhea. There was only a slight right sensorineural hearing loss. Brain computerized tomography (CT) revealed hyperpneumatization in the right mastoid process and right temporal bone, bilateral occipital, parietal and frontal bones, and right side of the atlas. There was no pneumocephalus, but there was free air under the scalp of the right suboccipital region and around the right condyle, right transverse process of the atlas and right paravertebral region of the upper cervical vertebrae. Extrathecal cerebrospinal fluid (CSF) leakage was not detected by CT cisternography with intrathecal contrast administration and by the radionuclide cisternogram

Detailed Case Studies

Wireless body area networks (WBANs) are one of the key technologies that support the development of pervasive health monitoring (remote patient monitoring systems), which has attracted more attention in recent years. These WBAN applications requires stringent security requirements as they are concerned with human lives. In the recent scenario of the corona pandemic, where most of the healthcare providers are giving online services for treatment, DDoS attacks become the major threats over the internet. This chapter particularly focusses on detection of DDoS attack using machine learning algorithms over the healthcare environment. In the process of attack detection, the dataset is preprocessed. After preprocessing the dataset, the cleaned dataset is given to the popular classification algorithms in the area of machine learning namely, AdaBoost, J48, k-NN, JRip, Random Committee and Random Forest classifiers. Those algorithms are evaluated independently and the results are recorded. Results concluded that J48 outperform with accuracy of 99.98% with CICIDS dataset and random forest outperform with accuracy of 99.917, but it takes the longest model building time. Depending on the evaluation performance the appropriate classifier is selected for further DDoS detection at real-time

Development and validation of an educational robot attitude scale (ERAS) for secondary school students

Humanoid robots equipped with social skills have come to be used increasingly in the field of education across various subfields such as science education, special education, and foreign language education. In order to enhance the use of humanoid robots in educational settings, and to comprehensively evaluate its impact on the transformation of the class, understanding students' attitudes towards the use of robots for educational purposes plays a critical role. This paper outlines the implementation and validation procedures of an educational robot attitude scale (ERAS) developed to measure the attitudes of secondary school students towards the use of humanoid robots in educational settings. The sample of the study comprised of 232 secondary school students. The development and validation process consisted of exploratory factor analysis and convergent validity. The developed scale consists of 17 items and represents four factors of students' attitude: engagement, enjoyment, anxiety and intention. These four factors accounted for 66% of the total variance of the scale. Internal consistency coefficient for the whole scale was found .90 according to the reliability analysis. The results of the study suggest that the scale is a valid, reliable, and efficient tool for measuring the dimensions of students' attitudes towards humanoid robots in educational settings

The contextual factors contributing to occupants' adaptive comfort behaviors in offices - A review and proposed modeling framework

Occupants play an unprecedented role on energy use of office buildings and they are often perceived as one of the main causes of underperforming buildings. It is therefore necessary to capture the factors influencing these energy intensive occupant behaviors and to incorporate them in building design. This review-based article puts forward a framework to represent occupant behavior in buildings by arguing: occupants are not illogical and irrational but rather that they attempt to restore their comfort in the easiest way possible, but are influenced by many contextual factors. This framework synthesizes statistical and anecdotal findings of the occupant behavior literature. Furthermore, it lends itself to occupant behavior researchers to form a systematic way to report the influential contextual factors such as ease of control, freedom to reposition, and social constra ints

Cluster analysis-based anomaly detection in building automation systems

Faults in heating, ventilation, and air-conditioning control networks substantially affect energy and comfort performance in commercial buildings. As these control networks are comprised of many sensors and actuators, it is challenging to identify, often subtle, anomalies caused by these faults. In this paper, we develop a cluster analysis method for anomaly detection. The proposed method consolidates the building automation system data into a small number of distinct patterns of operation. These distinct patterns help energy managers discover and interpret anomalies through visualization of these patterns. The method was demonstrated with a year's worth of building automation system data from 247 thermal zones and an air handling unit. Anomalies associated with zone temperature and airflow control were identified in about one-third of these zones. At the air handling unit-level, we identified anomalies related with three different faults: the use of economizer mode with perimeter heating, and leaky outdoor and return air dampers. The use of economizer mode with perimeter heating affected 39% to 52% of the total operation period and caused the outdoor air damper to remain fully open and the heat recovery unit to remain off during most of the heating season