Regional And Residential Short Term Electric Demand Forecast Using Deep Learning

For optimal power system operations, electric generation must follow load demand. The generation, transmission, and distribution utilities require load forecasting for planning and operating grid infrastructure efficiently, securely, and economically. This thesis work focuses on short-term load forecast (STLF), that concentrates on the time-interval from few hours to few days. An inaccurate short-term load forecast can result in higher cost of generating and delivering power. Hence, accurate short-term load forecasting is essential. Traditionally, short-term load forecasting of electrical demand is typically performed using linear regression, autoregressive integrated moving average models (ARIMA), and artificial neural networks (ANN). These conventional methods are limited in application for big datasets, and often their accuracy is a matter of concern. Recently, deep neural networks (DNNs) have emerged as a powerful tool for machine-learning problems, and known for real-time data processing, parallel computations, and ability to work with a large dataset with higher accuracy. DNNs have been shown to greatly outperform traditional methods in many disciplines, and they have revolutionized data analytics. Aspired from such a success of DNNs in machine learning problems, this thesis investigated the DNNs potential in electrical load forecasting application. Different DNN Types such as multilayer perception model (MLP) and recurrent neural networks (RNN) such as long short-term memory (LSTM), Gated recurrent Unit (GRU) and simple RNNs for different datasets were evaluated for accuracies. This thesis utilized the following data sets: 1) Iberian electric market dataset; 2) NREL residential home dataset; 3) AMPds smart-meter dataset; 4) UMass Smart Home datasets with varying time intervals or data duration for the validating the applicability of DNNs for short-term load forecasting. The Mean absolute percentage error (MAPE) evaluation indicates DNNs outperform conventional method for multiple datasets. In addition, a DNN based smart scheduling of appliances was also studied. This work evaluates MAPE accuracies of clustering-based forecast over non-clustered forecasts

GREEN SYNTHESIS AND CHARACTERIZATION OF SILVER NANOPARTICLES USING CORIANDRUM SATIVUM LEAF EXTRACT

Development of biologically inspired experimental processes for the synthesis of nanoparticles is evolving into an important branch of nanotechnology. To meet the increasing demands for commercial nanoparticles new eco-friendly “green” methods of synthesis are being discovered. In this study, synthesis of stable silver nanoparticles (AgNPs) was done using Coriandrum sativum leaf extract. UV-Vis spectrometer uses to monitor the reduction of Ag ions and formation of AgNPs in medium. XRD and SEM have been used to investigate the morphology of prepared AgNPs. The peaks in XRD pattern are associated with that of face-centered-cubic (FCC) form of metallic silver. The average grain size of silver nanoparticles is found to be 6.45 nm. TGA/DTA result associated with weight loss and exothermic reaction due to desorption of chemisorbed water. FTIR was performed to identify the functional groups of carbonyl, hydroxyl, amine and protein molecule which form a layer covering AgNPs and stabilize the AgNPs in medium

A Reference-Model Strategy for Self-Protective Smart Inverters

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringFariba FatehBehrooz MirafzalThis dissertation presents novel self-protective methods for grid-interactive inverters. The self-protective methods use reference models to inspect the incoming power setpoints, detect unsafe setpoints, and protect the inverter-based system accordingly. By employing these new self-protective methods based on reference models, grid-interactive inverters can effectively contribute to distributed energy generation within the energy infrastructure, where a supervisory control structure is required for energy management and economic dispatch. In a centralized supervisory control structure, the inverters need to be in contact with aggregators, other energy generation units, or the utility operating center. The communication capability makes a grid-interactive inverter a cyber-physical device. However, the connection of inverters to a communication network exposes the inverters to active attackers who can interfere with the control infrastructure and send malicious setpoints to the local controller. Such malicious setpoints can have harmful consequences, such as uncontrolled power oscillations, voltage sags and swells, equipment damage, and blackouts. This dissertation develops the self-protective methods using steady-state and dynamic reference models. The self-protection strategy inspects incoming power setpoints from the utility operator or third-party aggregators using the reference models before engaging the setpoints to the local controllers. At first, a self-learning feature for a self-protective inverter is developed. Self-protective inverters use the self-learning feature to learn their normal operating region by estimating unknown system parameters and employing known parameters from the measurements. The unknown system (grid) parameters are estimated by injecting a current at a different frequency than the fundamental power frequency. Consequently, two distinct real-time grid parameter estimation techniques, namely the model reference estimation and recursive least square method, have been developed and experimentally validated. Then, analytical steady-state and dynamic reference models are developed to inspect the incoming power setpoints. The self-protection method uses these reference models to learn the safe operating region of the inverters. The steady-state reference model is developed based on steady-state linear and nonlinear operating regions, and the dynamic reference model is formed based on the full-order inverter model. Based on the risk of unsafe operation defined by the steady-state model, if the setpoints fall in a high-risk region, the setpoints are rejected by the self-protection method, and the previously accepted setpoints remain in operation. Following the steady-state model check, the accepted setpoints are further examined using the full-order dynamic model to predict the location of dominant eigenvalues of the system using root-locus studies, thus predicting the dynamic response and making the decision whether the commanded setpoints are safe or unsafe. The performance of the self-protection strategy using the analytical reference models is tested using hardware experiments. The steady-state analytical model demonstrates satisfactory performance, characterized by its mathematical simplicity and ease of implementation in real-time applications. However, despite the promising performance and enhanced capabilities of the full-order dynamic model, the complexity of the full-order model can cause a high computational burden on digital signal processors (DSP) for real-time applications. In order to address this challenge, a stability criterion is developed from a simplified inverter model to determine the stability margin, thus predicting the inverter behavior for commanded setpoints. This criterion serves as a predictive tool for ensuring the safe operation of the inverter. One notable advantage of the stability criterion is its ability to quickly estimate the gain margin of the controller without requiring knowledge of pole locations. Experimental tests are conducted on hardware to validate the effectiveness of the developed stability criterion. While the stability criterion successfully reduces the computational burden, it is important to note that the simplified criterion is inherently less accurate compared to the full-order inverter model. This is due to its incapability to capture the intricacies and dynamics of the system fully. To address this limitation, this dissertation proposes novel hybrid dynamic models by combining the data-driven model with the analytical model. This work presents two forms of hybrid dynamic models using a data-driven neural network platform. The first hybrid model uses analytically evaluated risk factors as one of the inputs of the neural network to predict stable and unstable operation as the output of the neural network. The other hybrid model used the data-driven neural network and developed stability criterion in parallel to predict the stable and unstable operation. Furthermore, a standard neural network is implemented as a benchmark that considers all relevant information as input and predicts whether the operation of the inverter is safe or unsafe. This benchmark highlights the merits of the developed hybrid dynamic models. The effectiveness of the hybrid dynamic models is thoroughly evaluated through simulation and hardware tests to ensure their practical applicability

Design of a simplified hierarchical bayesian network for residential energy storage degradation

In this paper a simplified hierarchical Bayesian network (BN) is developed to estimate residential energy storage degradation in terms of capacity fade. The BN is trained using experimental results of lithium iron phosphate batteries. Residential energy storage capacity fade was estimated for multiple cases. These cases originated from a smart home energy management system (SHEMS). The cases reflect that capacity fade of the residential energy storage depends on SHEMS architecture, power consumption limits and electric vehicle schedule

Effectiveness of a hand hygiene training intervention in improving knowledge and compliance rate among healthcare workers in a respiratory disease hospital

Background: Practicing hand hygiene (HH) is a crucial element of infection control, with healthcare workers (HCWs) playing a vital role in preventing the spread of infection. However, inadequate knowledge and non-compliance to HH protocols pose significant challenges in healthcare settings. This study aimed to evaluate the effectiveness of an HH training intervention in enhancing knowledge and staff compliance within a respiratory disease hospital. Method: A pre-and post-training study was conducted among the healthcare workers in a respiratory disease treatment facility. The intervention comprised a series of 3-hour training sessions conducted over five days, focusing on the World Health Organization's (WHO) recommended guideline ''Your Five Moments For Hand Hygiene.'' These sessions covered proper HH techniques and underscored the repercussions of inadequate compliance. Educational materials related to HH were displayed in prominent locations throughout the facility. The knowledge levels and compliance rate were assessed before and after the intervention. Result: The intervention significantly improved HH knowledge levels and compliance rates among the participants. Marking a significant improvement, the compliance rate of HH protocols increased from 66.0% to 88.3% during the pre-to post-training period, with a concurrent increase in the mean knowledge score from 68.6% to 78.9%. Conclusion: This study underscores the potential of training and education in elevating HH compliance and knowledge among healthcare workers. The findings advocate that healthcare facilities routinely incorporate such interventions into their infection control programs, ultimately improving patient and healthcare worker safety

Phytohormone-Mediated Stomatal Response, Escape and Quiescence Strategies in Plants under Flooding Stress

Generally, flooding causes waterlogging or submergence stress which is considered as one of the most important abiotic factors that severely hinders plant growth and development. Plants might not complete their life cycle even in short duration of flooding. As biologically intelligent organisms, plants always try to resist or survive under such adverse circumstances by adapting a wide array of mechanisms including hormonal homeostasis. Under this mechanism, plants try to adapt through diverse morphological, physiological and molecular changes, including the closing of stomata, elongating of petioles, hollow stems or internodes, or maintaining minimum physiological activity to store energy to combat post-flooding stress and to continue normal growth and development. Mainly, ethylene, gibberellins (GA) and abscisic acid (ABA) are directly and/or indirectly involved in hormonal homeostasis mechanisms. Responses of specific genes or transcription factors or reactive oxygen species (ROS) maintain the equilibrium between stomatal opening and closing, which is one of the fastest responses in plants when encountering flooding stress conditions. In this review paper, the sequential steps of some of the hormone-dependent survival mechanisms of plants under flooding stress conditions have been critically discussed

ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application

Chronic kidney diseases (CKDs) are a significant public health issue with potential for severe complications such as hypertension, anemia, and renal failure. Timely diagnosis is crucial for effective management. Leveraging machine learning within healthcare offers promising advancements in predictive diagnostics. In this paper, we developed a machine learning-based kidney diseases prediction (ML‐CKDP) model with dual objectives: to enhance dataset preprocessing for CKD classification and to develop a web-based application for CKD prediction. The proposed model involves a comprehensive data preprocessing protocol, converting categorical variables to numerical values, imputing missing data, and normalizing via Min-Max scaling. Feature selection is executed using a variety of techniques including Correlation, Chi-Square, Variance Threshold, Recursive Feature Elimination, Sequential Forward Selection, Lasso Regression, and Ridge Regression to refine the datasets. The model employs seven classifiers: Random Forest (RF), AdaBoost (AdaB), Gradient Boosting (GB), XgBoost (XgB), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT), to predict CKDs. The effectiveness of the models is assessed by measuring their accuracy, analyzing confusion matrix statistics, and calculating the Area Under the Curve (AUC) specifically for the classification of positive cases. Random Forest (RF) and AdaBoost (AdaB) achieve a 100% accuracy rate, evident across various validation methods including data splits of 70:30, 80:20, and K-Fold set to 10 and 15. RF and AdaB consistently reach perfect AUC scores of 100% across multiple datasets, under different splitting ratios. Moreover, Naive Bayes (NB) stands out for its efficiency, recording the lowest training and testing times across all datasets and split ratios. Additionally, we present a real-time web-based application to operationalize the model, enhancing accessibility for healthcare practitioners and stakeholders.Web app link: https://rajib-research-kedney-diseases-prediction.onrender.com

Antioxidant potential, subacute toxicity, and beneficiary effects of methanolic extract of pomelo (Citrus grandis L. Osbeck) in Long Evan rats

The aim of this study was to investigate the antioxidant potentials, subacute toxicity, and beneficiary effects of methanolic extract of pomelo ( L. Osbeck) in rats. Long Evans rats were divided into four groups of eight animals each. The rats were orally treated with three doses of pomelo (250, 500, and 1000 mg/kg) once daily for 21 days. Pomelo extract contained high concentrations of polyphenols, flavonoids, and ascorbic acid while exhibiting high 1,1-diphenyl-2-picrylhydrazyl radical scavenging activity and ferric reducing antioxidant power values. There was no significant change in the body weight, percentage water content, and relative organ weight at any administered doses. In addition, no significant alterations in the hematological parameters were also observed. However, rats which received 1000 mg/kg dose had a significant reduction in some serum parameters, including alanine transaminase (15.29%), alkaline phosphatase (2.5%), lactate dehydrogenase (15.5%), -glutamyltransferase (20%), creatinine (14.47%), urea (18.50%), uric acid (27.14%), total cholesterol (5.78%), triglyceride (21.44%), low-density lipoprotein cholesterol (40.74%), glucose (2.48%), and all atherogenic indices including cardiac risk ratio (24.30%), Castelli's risk index-2 (45.71%), atherogenic coefficient (42%), and atherogenic index of plasma (25%) compared to control. In addition, the highest dose (1000 mg/kg) caused a significant increase in iron (12.07%) and high-density lipoprotein cholesterol (8.87%) levels. Histopathological findings of the vital organs did not indicate any pathological changes indicating that pomelo is nontoxic, safe, and serves as an important source of natural antioxidants. In addition, the fruit extract has the potential to ameliorate hepato- and nephrotoxicities and cardiovascular diseases as well as iron deficiency anemia