Event Monitoring of Transformer Discharge Sounds based on Voiceprint

This paper investigates the operation inspection and anomaly diagnosis of transformers in substations, and carries out an application study of artificial intelligence-based sound recognition technology in transformer discharge diagnosis to improve the timeliness and diagnostic capability of intelligent monitoring of substation equipment operation. In this study, a sound parameterization technology in the field of sound recognition is used to implement automatic discharge sound detections. The sound samples are pre-processed and then Mel-frequency cepstrum coefficients (MFCCs) are extracted as features, which are used to train Gaussian mixture models (GMMs). Finally, the trained GMMs are used to detect discharge sounds in the place of transformers in substations. The test results demonstrate that the audio anomaly detection based on MFCCs and GMMs can be used to effectively recognize anomalous discharge in the high scenario of transformers.

Projecting the global impact of fossil fuel production from the Former Soviet Union

Detailed projections of the Former Soviet Union (FSU) fossil fuel production has been created. Russian production has been modelled at the region (oblast) level where possible. The projections were made using the Geologic Resource Supply-Demand Model (GeRS-DeMo). Low, Best Guess and High scenarios were created. FSU fossil fuels are projected to peak between 2027 and 2087 with the range due to spread of Ultimately Recoverable Resources (URR) values used. The Best Guess (BG) scenario anticipates FSU will peak in 2087 with production over 170 EJ per year. The FSU projections were combined with rest of the world projections (Mohr et al. 2015b), the emissions from the High scenario for the world are similar to the IPCC A1 AIM scenario.

GHG Balance of Agricultural Intensification & Bioenergy Production in the Orinoquia Region, Colombia

Energy crop expansion can increase land demand and generate displacement of food crops, which impacts greenhouse gas (GHG) emissions mainly through land-use change (LUC). Increased agricultural productivity could compensate for this. Our study aims to evaluate the regional combined GHG emissions of increasing agricultural yields for food crop and beef production and using the generated surplus land for biomass production to replace fossil fuels in the Orinoquia region of Colombia until 2030. The results show that surplus land for biomass production is obtained only when strong measures are applied to increase agricultural productivity. In the medium and high scenario, a land surplus of 0.6 and 2.4 Mha, respectively, could be generated. Such intensification results in up to 83% emission reduction in Orinoquia’s agricultural sector, largely coming from increasing productivity of cattle production and improving degraded pastures. Biofuel potential from the surplus land is projected at 36 to 368 PJ per year, with a low risk of causing indirect LUC, and results in GHG emission reductions of more than 100% compared to its fossil fuel equivalent. An integrated perspective of the agricultural land use enables sustainable production of both food and bioenergy.

Soil organic carbon potential of Bozhurishte municipality: A case study from western Bulgaria

The current study focuses on the soil organic carbon contents in topsoil (0-30 cm) in Bozhurishte Municipality. The municipality is a part of Sofia Province and it represents a major section of the “Sofia-Bozhurishte Economic Zone”. The aim of the investigation is to reveal how soil organic carbon storages may change in the forthcoming 20 years during a medium and a high scenario. The results of the study display several major zones of a large soil organic carbon pool, located to the northeast and the central parts of the municipality. The section, containing the largest soil organic carbon pool, may experience an increase of 376 400 tons/ha for 20 years in the medium scenario and 499 100 tons/ha in the high scenario. The author concludes that promising results may be used as a basis for an expansion of the study in the neighboring municipalities.

Econometric Model for Forecasting Electricity Demand of Industry and Construction Sectors in Vietnam to 2030

An accurate forecasting for long-term electricity demand makes a major role in the planning of the power system in any country. Vietnam is one of the most economically developing countries in the world, and its electricity demand has been increased dramatically high of about 15%/y for the last three decades. Contribution of industry and construction sectors in GDP has been increasing year by year, and are currently holding the leading position of largest consumers with more than 50% sharing in national electricity consumption proportion. How to estimate the electricity consumption of these sectors correctly makes a crucial contribution to the planning of the power system. This paper applies an econometric model with Cobb Douglas production function - a top-down method to forecast electricity demand of the industry and construction sectors in Vietnam to 2030. Four variables used are the value of the sectors in GDP, income per person, the proportion of electricity consumption of the sectors in total, and electric price. Forecasted results show that the proposed method has a quite low MAPE of 7.66% for long-term forecasting. Variable of electric price does not affect the demand. This is a very critical result of the study for authority governors in Vietnam. In the base scenario of the GDP and the income per person, the forecasted electricity demands of the sectors are 112,853 GWh, 172,691 GWh, and 242,027 GWh in 2020, 2025, 2030, respectively. In high scenario one, the demands are 115,947 GWh, 181,591 GWh, and 257,272 GWh, respectively. The above values in the high scenario are less than from 9.0% to 15.8 % of that of the based on in the Revised version of master plan N0. VII.

Control Policy Mix in Measles Transmission Dynamics Using Vaccination, Therapy, and Treatment

This paper considers a deterministic model for the dynamics of measles transmission in a population divided into six classes with respect to the disease states: susceptible, vaccinated, exposed, infected, treated, and recovered. First, we investigate the dynamical properties of the SVEITR model such as its equilibrium points, their stability, and parameter sensitivity by applying constant controls. Criteria for determining the stability of disease-free and endemic equilibrium points are provided in terms of basic reproduction number. The model is then extended by incorporating vaccination, therapy, and treatment rates as time-dependent control variables representing the level of coverages. Application of Pontryagin’s maximum principle provides the necessary conditions that must be satisfied for the existence of optimal controls aiming at minimization of the number of exposed and infected individuals simultaneously with the control effort. Numerical simulations that were carried out using the backward sweep method and Runge–Kutta scheme suggest that optimal controls under moderate and high scenarios can effectively reduce the cases of measles. In particular, the moderate scenario that utilizes the existing coverage level of 86% for MCV1 and 69% for MCV2 can degrade the cost functional by 47% of the low scenario. Meanwhile, high scenario that takes the 2020 target of 96% as coverage only makes a slight difference in reducing the number of exposed and infected individuals.

Flexibility of Electric Vehicle Demand: Analysis of Measured Charging Data and Simulation for the Future

This paper proposes a method for analyzing and simulating the time-dependent flexibility of electric vehicle (EV) demand. This flexibility is influenced by charging power, which depends on the charging stations, the EV characteristics, and several environmental factors. Detailed charging station data from a Dutch case study have been analysed and used as input for a simulation. In the simulation, the interdependencies between plug-in time, connection duration, and required energy are respected. The data analysis of measured data reveals that 59% of the aggregated EV demand can be delayed for more than 8 h, and 16% for even more than 24 h. The evening peak shows high flexibility, confirming the feasibility of congestion management using smart charging within flexibility constraints. The results from the simulation show that the average daily EV demand increases by a factor 21 between the ‘Present-day’ and the ‘High’ scenario, while the maximum EV demand peak increases only by a factor 6, as a result of the limited simultaneity of the transactions. Further, simulations using the average charging power of individual measured transactions yield more accurate results than simulations using a fixed value for charging power. The proposed method for simulating future EV flexibility provides a basis for testing different smart charging algorithms.

Analysis of Causality Relationship Energy Consumption and CO2 Emissions to Economic Growth based on the LEAP Model Case Study of Energy Consumption in Indonesia 2010-2025)

This study discusses scenarios and analyzes the causal relationship of energy consumption and CO2 emissions to economic growth in Indonesia period 2010-2025. The modeling scenario is divided into 6 sections, namely BAU scenario, High scenario, Low scenario, High-Low scenario, Low-High scenario and Policy scenario. The result of scenario data is processed by performing statistical data modeling and econometric period 2010-2025. The research method used interpolation method and causality testing method. The tools are used in this research is LEAP and EViews. LEAP is used for energy modeling as well as CO2 emissions and EViews is used to manage data, analyze econometrics and statistics. The results of this study show that economic growth, energy consumption and CO2 emissions at 6 scenario indicate fluctuated competitive growth. This study proves that only 1 scenario has direct causality relationship that is only energy consumption which statistically significant influence economic growth in Policy scenario. For economic growth and CO2 emissions there are 4 scenarios that have direct causality (BAU, High, High -Low, Low-High scenario), 1 scenario has no causality relationship (Low scenario) and 1 scenario has two -way causality relationship (Policy scenario).

Scaling up family planning in Sierra Leone: A prospective cost–benefit analysis

Family planning is commonly regarded as a highly cost-effective health intervention with wider social and economic benefits. Yet use of family planning services in Sierra Leone is currently low and 25.0% of married women have an unmet need for contraception. This study aims to estimate the costs and benefits of scaling up family planning in Sierra Leone. Using the OneHealth Tool, two scenarios of scaling up family planning coverage to currently married women in Sierra Leone over 2013–2035 were assessed and compared to a ‘no-change’ counterfactual. Our costing included direct costs of drugs, supplies and personnel time, programme costs and a share of health facility overhead costs. To monetise the benefits, we projected the cost savings of the government providing five essential social services – primary education, child immunisation, malaria prevention, maternal health services and improved drinking water – in the scale-up scenarios compared to the counterfactual. The total population, estimated at 6.1 million in 2013, is projected to reach 8.3 million by 2035 in the high scenario compared to a counterfactual of 9.6 million. We estimate that by 2035, there will be 1400 fewer maternal deaths and 700 fewer infant deaths in the high scenario compared to the counterfactual. Our modelling suggests that total costs of the family planning programme in Sierra Leone will increase from US$4.2 million in 2013 to US$10.6 million a year by 2035 in the high scenario. For every dollar spent on family planning, Sierra Leone is estimated to save US$2.10 in expenditure on the five selected social sector services over the period. There is a strong investment case for scaling up family planning services in Sierra Leone. The ambitious scale-up scenarios have historical precedent in other sub-Saharan African countries, but the extent to which they will be achieved depends on a commitment from both the government and donors to strengthening Sierra Leone’s health system post-Ebola.

Environmental impacts of shipping in 2030 with a particular focus on the Arctic region

We quantify the concentrations changes and Radiative Forcing (RF) of short-lived atmospheric pollutants due to shipping emissions of NOx, SOx, CO, NMVOCs, BC and OC. We use high resolution ship emission inventories for the Arctic that are more suitable for regional scale evaluation than those used in former studies. A chemical transport model and a RF model are used to evaluate the time period 2004–2030, when we expect increasing traffic in the Arctic region. Two datasets for ship emissions are used that characterize the potential impact from shipping and the degree to which shipping controls may mitigate impacts: a high (HIGH) scenario and a low scenario with Maximum Feasible Reduction (MFR) of black carbon in the Arctic. In MFR, BC emissions in the Arctic are reduced with 70% representing a combination technology performance and/or reasonable advances in single-technology performance. Both scenarios result in moderate to substantial increases in concentrations of pollutants both globally and in the Arctic. Exceptions are black carbon in the MFR scenario, and sulfur species and organic carbon in both scenarios due to the future phase-in of current regulation that reduces fuel sulfur content. In the season with potential transit traffic through the Arctic in 2030 we find increased concentrations of all pollutants in large parts of the Arctic. Net global RFs from 2004–2030 of 53 mW m−2 (HIGH) and 73 mW m−2 (MFR) are similar to those found for preindustrial to present net global aircraft RF. The found warming contrasts with the cooling from historical ship emissions. The reason for this difference and the higher global forcing for the MFR scenario is mainly the reduced future fuel sulfur content resulting in less cooling from sulfate aerosols. The Arctic RF is largest in the HIGH scenario. In the HIGH scenario ozone dominates the RF during the transit season (August–October). RF due to BC in air, and snow and ice becomes significant during Arctic spring. For the HIGH scenario the net Arctic RF during spring is 5 times higher than in winter.