Long –term load forecasting of power systems using Artificial Neural Network and ANFIS

Load forecasting is very important for planning and operation in power system energy management. It reinforces the energy efficiency and reliability of power systems. Problems of power systems are tough to solve because power systems are huge complex graphically, widely distributed and influenced by many unexpected events. It has taken into consideration the various demographic factors like weather, climate, and variation of load demands. In this paper, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models were used to analyse data collection obtained from the Metrological Department of Malaysia. The data sets cover a seven-year period (2009- 2016) on monthly basis. The ANN and ANFIS were used for long-term load forecasting. The performance evaluations of both models that were executed by showing that the results for ANFIS produced much more accurate results compared to ANN model. It also studied the effects of weather variables such as temperature, humidity, wind speed, rainfall, actual load and previous load on load forecasting. The simulation was carried out in the environment of MATLAB software

Effects of Disorder on the Pressure-Induced Mott Transition in κ\kappa-BEDT-TTF)2_2Cu[N(CN)2_2]Cl

We present a study of the influence of disorder on the Mott metal-insulator transition for the organic charge-transfer salt $\kappa$-(BEDT-TTF)$_2$Cu[N(CN)$_2$]Cl. To this end, disorder was introduced into the system in a controlled way by exposing the single crystals to x-ray irradiation. The crystals were then fine-tuned across the Mott transition by the application of continuously controllable He-gas pressure at low temperatures. Measurements of the thermal expansion and resistance show that the first-order character of the Mott transition prevails for low irradiation doses achieved by irradiation times up to 100 h. For these crystals with a moderate degree of disorder, we find a first-order transition line which ends in a second-order critical endpoint, akin to the pristine crystals. Compared to the latter, however, we observe a significant reduction of both, the critical pressure $p_c$ and the critical temperature $T_c$. This result is consistent with the theoretically-predicted formation of a soft Coulomb gap in the presence of strong correlations and small disorder. Furthermore, we demonstrate, similar to the observation for the pristine sample, that the Mott transition after 50 h of irradiation is accompanied by sizable lattice effects, the critical behavior of which can be well described by mean-field theory. Our results demonstrate that the character of the Mott transition remains essentially unchanged at a low disorder level. However, after an irradiation time of 150 h, no clear signatures of a discontinuous metal-insulator transition could be revealed anymore. These results suggest that, above a certain disorder level, the metal-insulator transition becomes a smeared first-order transition with some residual hysteresis.Comment: 20 pages, 7 figures, appeared in the Special Issue "Advances in Organic Conductors and Superconductors" of Crystal

Efficient Design of a Large Storage Tank for Liquefied Natural Gas

يعتبر الغاز الطبيعي واحد من مصادر التي تنتج طاقة نظيفة قليلة الانبعاثات شديدة الكثافة وصديقة للبيئة. لنقل الغاز الطبيعي، من المهم جدا تحويل الغاز الطبيعي الى الحالة السائلة لتقليل خسائر عملية النقل، لذلك تصميم خزانات لحفظ الغازي الطبيعي المسائل يعتبر من العوامل التي تساعد على دعم تجارة الغاز الطبيعي. تتميز خزانات الغاز الطبيعي المسال بحجومها الكبيرة جدا التي تصل الى 2000000 متر مكعب. يعتبر تصميم خزانات الغاز الطبيعي تكنلوجيا جديدة للكثير من الدول. تتميز خزانات الخزن بسمك عزل عالي يصل الى متر في بعض الاحيان لان الغاز المسال يكون عادة بدرجات حرارة منخفظة تصل -165 درجة سيليزية. هذا التصميم يجمع بين عدة كودات تصميم لدراسة تفصيلية لعملية تصميم خزان غاز مسال يشمل مايلي الهيكل الداخلي والخارجي للتصميم، تصميم قاعدة الخزان، تصميم الجزء العلوي من الخزان. بالاضافة الى ذلك، التصميم يتناول محاكاة تفصيلية للحرارة المتسربة من كل جزء من الخزان ليتبين ان التصميم يعمل بكفاءة عالية والحرارة المتسربة ضمن الحدود المقبولة.Natural gas is known as a green source of energy due to high purity, high energy density and environment friendly. To transport natural gas, it is an important to convert it into liquid case to reduce the cost, therefore the design of storage tank can be important factor in the natural gas trade. Liquefied natural gas (LNG) tank as a kind of storage column is quite different with other storage columns. Firstly, the size of this type of LNG tank is highly large, which comes with capacity up to 200,000 m3. LNG storage tank can be considered as a new technique in this field for many countries. Secondly, the low temperature of LNG will increase the isolation part and lead to difficult process through the operation and installation time. This work combines different designs codes to study in details the LNG tank design in terms of inner and outer construction, bottom design with corner protection, and top design with corner protection. In addition, the heat leakages have been calculated for each part to show that the heat leakage is acceptable. &nbsp

Optimal allocation and sizing of capacitor bank and distributed generation using particle swarm optimization

Power systems are complicated to be solved due to vast geographical location and are influenced by many unexpected weather events. The rapidly increasing population growth and the expansion of urban development are undoubtedly the main reasons for increasing electrical power demands that may affect the system voltage stability and the energy loss. Accurate long-term load forecasting (LTLF) is essential for load demand requirements. It is particularly significant under the influence of various weather factors, such as relative humidity and temperature. The research work presented in this thesis had investigated the effect of two additional weather parameters, namely wind speed and rainfall, in addition to the temperature and relative humidity using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in predicting the values of load demands. Moreover, the optimal allocation and sizing of the capacitor bank (C) and distributed generation (DG) were studied with the particle swarm optimization (PSO) technique to maintain the profile of bus voltages while reducing the energy loss of the network. This technique was also applied to the load incremental of 5% annually up to 40% for system planning purposes. As for the LTLF, ANFIS produced better results than ANN; and with two additional parameters of wind speed and rainfall, it delivered a more accurate prediction. The PSO algorithm allocates and determines the size of the capacitor and distributed generation in the power system. The capacitors and distributed generation are compensators that helped the power system network improve the voltage profile and reduce power loss. The proposed PSO algorithm was used with the OpenDSS engine to solve the power flow through the MATLAB and has been successful implemented in finding an optimal allocation and suitable size of the capacitor and distributed generation. In order to validate the functionality of the proposed PSO algorithm, the IEEE 14–bus and 30–bus systems were used as test systems. The research evidently indicated the PSO algorithm can be applied to the power system planning analysis for the placement and sizing of the capacitor and distributed generation while maintaining the acceptable voltage profile and minimizing the power loss

Optimum Allocation Of Capacitor And DG In MV Distribution Network Using PSO And OpenDSS

The optimum capacitor and distributed generation (DG) allocation in medium voltage (MV) distribution network utilizing particle swarm optimization (PSO) for selecting the optimum size and placement of (DG) units can significantly affect the distribution network. Capacitor installation is a standard method for reactive power compensation within a distribution network. The placement and sizing of the capacitor have been optimized in the distribution network for a number of capacitors with the objective of the voltage profile improvement and power loss minimization. Maximum and minimum bus voltage and possible maximum capacitor size were the constraints of the optimum capacitor and sizing problems, which take into account as a penalty factor (PF) within the objective function (OF) and the allocation of DG units. To solve the obtained OF, PSO and Open DSS engines were used in this research to analyse power flow results that obtained from the standard IEEE14 Bus system. The performance evaluation of PSO model was carried out by showing the results that the PSO algorithm. PSO can obtain the optimal solution of the size and location also find the optimum DG size for the loss minimization and voltage profile improvement compared to the standard case without DG and capacitor compensation. All simulations had been performed using MATLAB software

Internet of Things and Health Care in Pandemic COVID-19: System Requirements Evaluation

Technology adoption in healthcare services has resulted in advancing care delivery services and improving the experiences of patients. This paper presents research that aims to find the important requirements for a remote monitoring system for patients with COVID-19. As this pandemic is growing more and more, there is a critical need for such systems. In this paper, the requirements and the value are determined for the proposed system, which integrates a smart bracelet that helps to signal patient vital signs. (376) participants completed the online quantitative survey. According to the study results, Most Healthcare Experts, (97.9%) stated that the automated wearable device is very useful, it plays an essential role in routine healthcare tasks (in early diagnosis, quarantine enforcement, and patient status monitoring), and it simplifies their routine healthcare activities. I addition, the main vital signs based on their expert opinion should include temperature (66% of participants) and oxygenation level (95% of participants). These findings are essential to any academic and industrial future efforts to develop these vital wearable systems. The future work will involve implementing the design based on the results of this study and use machine-learning algorithm to better detect the COVID-19 cases based on the monitoring of vital signs and symptoms.Comment: 6 pages, 5 figure

Adaptive Mobile Chargers Scheduling Scheme based on AHP-MCDM for WRSN

Wireless Sensor Networks (WSNs) are used to sense and monitor physical conditions in various services and applications.However, there are a number of challenges in deploying WSNs, especially those pertaining to energy replenishment. Using the current solutions, when a significant number of sensors need to replenish their energy, this would be costly in terms of time, efforts and resources. Thus, this paper aims to solve this problem by efficiently deploying wireless power transfer technologies and scheduling Mobile Charging Vehicles (MCVs) in WRSN. The proposed method deploys multi-criteria decision-making (i.e., Analytical Hierarchy Process (AHP)) to schedule the charging tasks. To the best of our knowledge, this paper is the first to depend solely on AHP in MCVs scheduling. The paper demonstrates the validity of the proposed method by illustrating that the matrices that are created are within the accepted values of consistency ratio. In addition, the paper proposes a method of partitioning the values of our criteria to avoid the problem of different criteria having different measurement units. Unlike existing works, the paper aims to schedule an MCV for charging based on both the distance and residual energy of the sensor. The proposed method exhibits superiority in terms of the average remaining energy available in the system, having the shortest queue length, shorter MCV response time, shorter charging duration, and shorter queue waiting time against the state-of-the-art methods. Our study paves the way for next generation efficient charging and MCV scheduling

Treatment of Forty Adult Patients with Hodgkin Disease; Baghdad Teaching Hospital Experience

Background: Hodgkin disease was the first cancer in which the curative potential of combination chemotherapy was demonstrated. The affected patients are often young and there is a great potential for adding years of productive life by giving curative therapy even when the disease is advanced. Objective: to describe the experience of the hematology unit,Baghdad Teaching Hospital, in the management of 40 adult patients with Hodgkin disease. Patients and Methods: a retrospective cohort study of forty adult Iraqi patients with Hodgkin disease between 2005 and 2013 in the hematology unit. Patients were treated initially with 6-8 cycles of ABVD chemotherapy protocol (doxorubicine+ bleomycin+ vinblastin+ dacarbazine) , nine patients received additional involved field radiotherapy for residual masses or bulky disease. Overall survival and progression free survivals were estimated using Kaplan Meier survival plot. Results: The mean age was 28.6±12.88 years with females forming 61.5% of patients, mean duration of follow up was 27.9± 20.6 months. Staging showed that 55% and 27.5% had stage II and III respectively. B symptoms were found in 72.5% patients , bulky disease in 42.5% patients. Complete Response+ Complete Response undetermined was seen in 85% of cases. First Relapse occurred in 14%, and death in 7.5% of the patients. The 8 year overall survival and progression free survival were 82% and 50% respectively while the mean overall survival and progression free survival times were 84.7 and 59.9 months respectively. Conclusion: The results of the treatment of adult patients with Hodgkin disease in our unit is rather comparable to the results from other studies

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation