A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA

Electricity demand forecasting is a term used for prediction of users’ consumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its market deregulation has greatly amplified its essence. Despite numerous studies on the subject using certain classical approaches, there exists an opportunity for exploration of more sophisticated methods such as the deep learning (DL) techniques. Successful researches about DL applications to com¬puter vision, speech recognition, and acoustic computing problems are motivation. However, such researches are not sufficiently exploited for electricity demand forecasting using DL methods. In this paper, we considered specific DL techniques (LSTM, CNN, and MLP) to short-term load fore¬casting problems, using tropical institutional data obtained from a Transmission Company. We also test how accurate are predictions across the techniques. Our results relatively revealed models appropriateness for the problem

AN OVERVIEW OF DEEP LEARNING TECHNIQUES FOR SHORT-TERM ELECTRICITY LOAD FORECASTING

This paper presents an overview of some Deep Learning (DL) techniques applicable to forecasting electricity consumptions, especially in the short-term horizon. The paper introduced key parts of four DL architectures including the RNN, LSTM, CNN and SAE, which are recently adopted in implementing Short-term (electricity) Load Forecasting problems. It further presented a model approach for solving such problems. The eventual implication of the study is to present an insightful direction about concepts of the DL methods for forecasting electricity loads in the short-term period, especially to a potential researcher in quest of solving similar problems

Quaternary Trimethyl Chitosan Chloride Capped Bismuth Nanoparticles with Positive Surface Charges: Catalytic and Antibacterial Activities

Quaternary trimethyl chitosan-stabilized bismuth nanoparticles (QTMC-BiNPs) with positive surface charges were uniquely synthesized and fully characterized. In the synthesis, Quaternary Trimethyl Chitosan (QTMC), a water-soluble derivative of chitosan (CTS) was prepared using two-step reductive methylation. The new biopolymeric functionalized ligand was further used as capping agent for the synthesis of QTMC-BiNPs which was applied as antibacterial and catalytic agents. The reaction was carried out at room temperature without the use of energy consuming or high-cost instruments. The QTMC and nanocomposites were characterized by proton nuclear magnetic resonance ( 1 H NMR), attenuated total reflection Fourier-transform infrared, UV–visible, X-ray diffraction, X-ray photoelectron spectroscopy and energy dis-persive X-ray spectroscopic techniques. The topology and morphology of the composites were examined with scanning electron microscopy and high-resolution transmission electron microscopy. Thermogravimetric and differential thermal gravimetric analysis were also conducted. The degree of quaternization and degree of dimethylation values of 63.33 and 11.75%, respectively obtained for QTMC confirmed that the main product is a quaternary derivative. The average particle size of QTMC-BiNPs was evaluated to be between 30 and 45 nm. The QTMC-BiNPs revealed clear and uniform lattice fringes with an estimated interplanar d-spacing of 0.32 nm confirming the formation of highly crystalline nanocomposites. A further insight into the antibacterial activities of this nanomaterial were carefully examined on Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) using resazurin based microdilution method for Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC). The obtained results revealed that both bacteria pathogens were effectively inhibited/killed by the QTMC-BiNPs at very low concentrations. The MIC of 15.63 and 125 lg/mL were recorded against E. coli and S. aureus, respectively while the MBC of 31.25 and 500.00 lg/mL were estimated against E. coli and S. aureus, respectively. An extensive evaluation of the catalytic capability of the nanocomposites towards the reduction of 4-nitrophenol to 4-aminophenol was also carried out with highly promising result

A deep learning model for electricity demand forecasting based on a tropical data

Electricity demand forecasting is a term used for prediction of users’ consumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its market deregulation has greatly amplified its essence. Despite numerous studies on the subject using certain classical approaches, there exists an opportunity for exploration of more sophisticated methods such as the deep learning (DL) techniques. Successful researches about DL applications to computer vision, speech recognition, and acoustic computing problems are motivation. However, such researches are not sufficiently exploited for electricity demand forecasting using DL methods. In this paper, we considered specific DL techniques (LSTM, CNN, and MLP) to short-term load forecasting problems, using tropical institutional data obtained from a Transmission Company. We also test how accurate are predictions across the techniques. Our results relatively revealed models appropriateness for the problem

Preference for Artemisinin–based combination therapy among healthcare providers, Lokoja, North-Central Nigeria

Abstract Background In Nigeria, Artemisinin-based Combination Therapy (ACT) is the recommended first line antimalarial medicine for uncomplicated malaria. However, health care providers still continue the use of less efficacious medicines such as Sulphadoxine-pyrimethamine and chloroquine. We therefore determined preference for ACT (PFA) and factors associated with PFA among healthcare providers (HCP) in Lokoja, North-Central Nigeria as well as assessed healthcare providers’ knowledge of malaria case management. Methods We conducted a cross-sectional study among physicians, nurses, pharmacists, community health officers (CHOs), community health extension workers (CHEWs) and, patent and proprietary medicine vendors (PPMVs). Interviewer-administered questionnaires were administered to collect data on respondents’ characteristics, previously received malaria case management training and knowledge of malaria treatment. Knowledge scores ≥3 were categorised as good, maximum obtainable being 5. Results Of the 404 respondents, 214 (53.0%) were males. Overall, 219 (54.2%) respondents who received malaria case management training  included PPMVs: 79 (65.8%), CHEWs: 25 (64.1%), CHOs: 5 (55.6%), nurses: 72 (48.7%), physicians: 35 (47.3%) and pharmacists: 3 (23.1%). Overall, 202 (50.0%) providers including physicians: 69 (93.2%), CHO: 8 (88.9%), CHEWs: 33 (84.6%), pharmacists: 8 (61.5%), nurses: 64 (43.2%) and PPMVs: 20 (16.5%), had good knowledge of malaria treatment guidelines. Overall, preference for ACT among healthcare providers was 39.6%. Physicians: 50 (67.6%), pharmacists: 7 (59.3%) CHOs: 5 (55.6%), CHEWS: 16 (41.0%), nurses: 56 (37.8%) and PPMV: 24 (19.8%) had PFA. Receiving malaria case management training (adjusted odds ratio [aOR]) = 2.3; CI = 1.4 – 3.7) and having good knowledge of malaria treatment (aOR = 4.0; CI = 2.4 – 6.7) were associated with PFA. Conclusions Overall preference for ACT use was low among health care providers in this study. Preference for ACTs and proportion of health workers with good knowledge of malaria case management were even lower among PPMVs who had highest proportion of those who received malaria case management training. We recommend evaluation of current training quality, enhanced targeted training, follow-up supportive supervision of PPMVs and behavior change communication on ACT use