Wavelet-based short-term load forecasting using optimized anfis

This paper focuses on forecasting electric load consumption using optimized Adaptive Neuro-Fuzzy inference System (ANFIS). It employs the use of Particle Swarm Optimization (PSO) to optimize ANFIS, with aim of improving its speed and accuracy. It determines the minimum error from the ANFIS error function and thus propagates it to the premise part. Wavelet transform was used to decompose the input variables using Daubechies 2 (db2). The purpose is to reduce outliers as small as possible in the forecasting data. The data was decomposed in to one approximation coefficients and three details coefficients. The combined Wavelet-PSO-ANFIS model was tested using weather and load data of Nova Scotia province. It was found that the model can perform more than Genetic Algorithm (GA) optimized ANFIS and traditional ANFIS, which is been optimized by Gradient Decent (GD). Mean Absolute Percentage Error (MAPE) was used to measure the accuracy of the model. The model gives lower MAPE than the other two models, and is faster in terms of speed of convergence

Calibration of ZMPT101B voltage sensor module using polynomial regression for accurate load monitoring

Smart Electricity is quickly developing as the results of advancements in sensor technology. The accuracy of a sensing device is the backbone of every measurement and the fundamental of every electrical quantity measurement is the voltage and current sensing. The sensor calibration in the context of this research means the marking or scaling of the voltage sensor so that it can present accurate sampled voltage from the ADC output using appropriate algorithm. The peakpeak input voltage (measured with a standard FLUKE 115 meter) to the sensor is correlated with the peak-peak ADC output of the sensor using 1 to 5th order polynomial regression, in order to determine the best fitting relationship between them. The arduino microcontroller is used to receive the ADC conversion and is also programmed to calculate the root mean square value of the supply voltage. The analysis of the polynomials shows that the third order polynomial gives the best relationship between the analog input and ADC output. The accuracy of the algorithm is tested in measuring the root mean square values of the supply voltage using instantaneous voltage calculation and peak-peak voltage methods. The error in the measurement is less than 1% in the peak-peak method and less than 2.5% in the instantaneous method for voltage measurements above 50V AC, which is very good for measurements in utility. Therefore, the proposed calibration method will facilitate more accurate voltage and power computing for researchers and designers especially in load monitoring where the applied voltage is 240V or 120V ranges

Capacity building in Ocean Bathymetry: The Nippon Foundation GEBCO Training Programme at the University of New Hampshire

A successful Capacity Building project in hydrography is underway at the University of New Hampshire. Organised by the General Bathymetric Chart of the Oceans and sponsored by the Nippon Foundation, the programme trains hydrographers and other marine scientists in bathymetric mapping. Participants are formally prepared to produce bathymetric maps when they return to their home countries through a combination of graduate level courses and workshops, practical field training, participation in deep ocean research cruises, working visits to other laboratories and institutions, focused lectures from visiting experts, and the preparation of a bathymetry map of their area from public domain data. Intangible but necessary preparation includes the networking with professionals in bathymetry and related fields within Ocean Mapping, and the building of a cadre of graduates who will form the basis of international bathymetric mapping in the future

Recent approaches and applications of non-intrusive load monitoring

The Appliance Load Monitoring is vital in every energy consuming system be it commercial, residential or industrial in nature. Traditional load monitoring system, which used to be intrusive in nature require the installation of sensors to every load of interest which makes the system to be costly, time consuming and complex. Nonintrusive load monitoring (NILM) system uses the aggregated measurement at the utility service entry to identify and disaggregate the appliances connected in the building, which means only one set of sensors is required and it does not require entrance into the consumer premises. We presented a study in this paper providing a comprehensive review of the state of art of NILM, the different methods applied by researchers so far, before concluding with the future research direction, which include automatic home energy saving using NILM. The study also found that more efforts are needed from the researchers to apply NILM in appliance energy management, for example a Home Energy Management System (HEMS)

Frontonasal dysplasia Sequence : A case report

Frontonasal dysplasia (FND) is a very rare congenital abnormality in which the mid face does not develop normally. It affects mainly the head and face. Cause is unknown but may be sporadic or familial. We report a rare case of a full term baby who presented with classical features of FND in Maiduguri, Nigeria. Management difficulty in resource limited setting is highlighted.Key words: Dysmorphism, Frontonasaldysplasia, Neonate

A statistical data selection approach for short-term load forecasting using optimized ANFIS

Volume of the forecasting data and good data analysis are the key factors that influence the accuracy of forecasting algorithm because it depends on data identification and model parameters. This paper focuses on data selection approach for short-term load forecasting. It involves formulating data selection algorithm to identify factors (variables) that influence energy demand at utility level. Correlation Analysis (CA) and Hypothesis Test (HT) are used in the selection, where Wavelet Transform (WT) is applied to bridge the gap between the forecasting variables. This results to three groups of data; data without CA, HT and WT, data with CA, HT but without WT and data with CA, HT and WT. An optimized adaptive neuro-fuzzy inference system (ANFIS) using Cuckoo Search Algorithm (CS) is used to conduct the forecasting. The essence is to reduce the computational difficulty associated with the gradient descent (GD) algorithm in traditional ANFIS. With the three data groups, it is observed that CHW data can give satisfactory results more than the NCNHNW and NCNHW data. Also the numerical results shows that CHW data selection approach can give a MAPE of 0.63 against the bench-mark approach with MAPE of 3.55. This indicates that it is good practice to select the actual data and process it before the forecasting

Gender disparity in prevalence and risk factors of chronic Kidney disease among patients with type 2 diabetes in Northeastern Nigeria.

Diabetes mellitus is a metabolic disorder that is responsible for up to 5% of premature deaths worldwide. Diabetic kidney disease is the leading cause of end-stage renal disease. This study aims to evaluate gender disparity in prevalence and risk factors of diabetic kidney disease in northeastern Nigeria. Methodology: The study population consisted of adult patients with type 2 diabetesrecruited consecutively at the diabetes clinic of the University of Maiduguri Teaching Hospital, Maiduguri. Socio-demographic and anthropometric variables including age, sex, weight, height, BMI, as well as laboratory parameters, were obtained from each patient. Glomerular filtration rate was derived from the CKD-EPI formula using serum creatinine. Results: Two hundred and sixty-one adult patients with type 2 diabetes were recruited consecutively from the Diabetes outpatient clinic of the University of Maiduguri Teaching Hospital, Maiduguri. There were 167(64%) females and 94(36%) males. The mean ages of males and females were 51.10±12.23 years and 48.76±11.00 years, respectively (p= 0.115). The mean duration of diabetes was similar between males and females (7.24±7.18 vs 6.87±6.02 years, p= 0.652). Females had a higher BMI 2 2 compared with males (28.49±6.27Kg/M2 vs 26.41±4.86Kg/M2 p= 0.003). Fasting blood glucose, Low- density lipoprotein cholesterol and PCV were more deranged in females than among males (9.53±4.72 mmol/L vs 11.10±5.97mmol/L p= 0.020; 2.84±1.03mmol/L vs 3.19±1.03mmol/L p=0.009; 34.49±5.33% vs 33.11±4.54% p= 0.026). Out of the study population, 83(74.1%) females had renal dysfunction compared with 29(25.9%) males. The risk factors for progressive kidney disease among female patients were age >45 years (Exp (B) 1.799, 95% CI= 1.165-3.805) and systolic blood pressure >140mmHg (Exp (B)= 1.592, 95% CI= 0.772- 3.284). Conclusion: Diabetic kidney disease among our cohorts with type 2 diabetes was more prevalent among females compared with males and the risk factors associated with this disparity were older age, high BMI, poor glycaemic control, low PCV and elevated LDL cholesterol

Knowledge Assessment of Anti-snake Venom Among Healthcare Practitioners in Northern Nigeria

Introduction: Anti-snake venom (ASV) is the standard therapy for the management of snakebite envenoming (SBE). Therefore, the knowledge of ASV among healthcare practitioners (HCPs) is essential for achieving optimal clinical outcomes in snakebite management. This study aimed to assess knowledge of ASV among the HCPs in northern Nigeria. Methods: We conducted a cross-sectional study involving eligible HCPs from different healthcare settings in northern Nigeria. The participants were recruited into the study using a combination of online (via Google Form) and face-to-face paper-based survey methods. The ASV knowledge of the respondents was measured using a validated anti-snake venom knowledge assessment tool (AKAT). Inadequate overall knowledge of ASV was defined as scores of 0-69.9%, and 70-100% were considered adequate overall knowledge scores. The predictors of ASV knowledge were determined using multiple logistic regression. Results: Three hundred and thirty-one (331) eligible HCPs were included in the study analysis (310 from online and 21 from paper-based survey). Overall, an estimated 12.7% of the participants had adequate knowledge of ASV. Adequate ASV knowledge was higher among physicians compared with other HCPs (21.7%; X-2 =8.1; p=0.04). Those without previous training on ASV (adjusted odds ratio [a0R], 0.37; 95% confidence interval [CI], 0.18-0.73; p= 0.004) and who have not previously administered/dispensed ASV (aOR, 0.31; 95% CI, 0.15-0.63; p \u3c 0.001) were less likely to have adequate knowledge of ASV. Conclusion: The knowledge of ASV among healthcare practitioners in northern Nigeria is grossly inadequate. Experience with administering or dispensing ASV predicts ASV knowledge. Therefore, appropriate interventions are needed to improve ASV knowledge, particularly among the HCPs, for optimal healthcare outcomes