Spatio-Temporal Assessment of Vegetation Cover Dynamics in the Kurmi Region of Taraba State, Nigeria

The study was conducted on the Spatio-temporal assessment of vegetation cover dynamics in the Kurmi Region of Taraba State, in the Savannah belt of Nigeria, using remotely sensed satellite data from LANDSAT and the Geographic Information System. The result shows that from 2010 to 2015, there was an increase in none vegetative areas (Builtup, Barren, Rock, sand) by 6.1% and a reduction of vegetation by 6.1%, also from the year 2015 to 2020, there was an increase in the none vegetative areas (Builtup, Barren, Rock, sand) by 17.9% and reduction of vegetation by 17.9%, while from 2010 to 2020 there was an increase in none vegetative areas (Builtup, Barren, Rock, sand) by 24% and reduction of vegetation by 24%. Thus, the results of this study confirm that Spatio-temporal assessment of vegetation cover dynamics using NDVI by LANDSAT TM, ETM+ and OLI data offer an excellent potential tool for characterizing and understanding vegetation changes occurring in transitional areas like the Kurmi region of Taraba State. Furthermore, the study recommended that alternative domestic energy sources, e.g. kerosene, be provided to the low-income earners to avoid over-dependence on fuelwood sourced from deforestation.

Cyber security as an important factor of ensuring the life of the domestic energy industry

The issue of cybersecurity of the domestic energy industry has been studied. The strategic principles of increasing the level of cyber resilience of communication and technological systems of energy industry enterprises are considered. The positive experience of the United States and the United Kingdom on the organizational and legal framework for preventing and minimizing encroachment on critical energy infrastructure is highlighted. The methodology of cyber threat analysis and risk assessment of cybersecurity violations of energy infrastructure facilities is detailed. The issue of cybersecurity of energy facilities and automated systems is generalized. The initiatives and directions of activity of the National Security and Defense Council of Ukraine for the purpose of development of cybersecurity of power systems are opened. The directions of the improvement to the formation of conceptual foundations for cybersecurity in the energy sector of Ukraine are identified.

Evaluating Consumer Acceptance of the Commercial Fleet of Methanol Vehicles in China

To enhance domestic energy security and reduce air pollution, China has accelerated the deployment of alternative fuel vehicles including methanol vehicles since the 2010s. Already completed pilot projects have demonstrated that methanol vehicles (commercial fleet) are economical, environmentally friendly, and technically mature. Therefore, the Chinese government aims to continually deploy methanol vehicles in coal-rich provinces. There are more than 20,000 methanol taxis in operation in China, it is important to evaluate the existing consumer acceptance of such commercial fleet before commercialization in a wider range. This paper proposes a conceptual model to identify consumer acceptance of methanol taxis. The model generates hypotheses that have been tested using surveys completed by taxi drivers of methanol vehicles in the cities of Xi’an (Shaanxi province) and Guiyang (Guizhou province). Results demonstrate that market, economic, and technological concerns strongly determine the consumer acceptance of the commercial fleet of methanol vehicles in China.

Sensitivity analysis of heating a typical UK dwelling and implications for retrofit design

The aim of this research is to quantify the impact of heating set point on space heating energy demand for a typical UK dwelling. Retrofit includes fabric energy efficiency improvements. Energy performance certificates (EPCs) inform the householder of typical savings per measure, but this has previously been found to inaccurately estimate space heating energy demand, leading to errors in 'typical savings' presented to householders. The most sensitive inputs have been found to be temperature set point, followed by fabric efficiency. The BREDEM methodology assumes a temperature of 21°C for nine hours a day, rather than ~16°C and ~20°C found in research. The methods used to inform this study are local sensitivity analysis of the domestic energy model, based on a typical dwelling example with calibrated inputs. This is done using an open calibrated Python model, based on BREDEM. The impact of heating patterns on space heating energy demand are modelled pre retrofit; according to differing heating set points, following wall and loft fabric upgrade and full fabric upgrade. The BREDEM heating set point assumptions lead to space heating energy demand predicted ~50-100 kWh/m2/yr higher than real heating set points. Implications for retrofit design and EPCs are discussed.

Practical Aspects of Modernization of Electrical Equipment of the Substation

Currently, one of the important problems for domestic energy is the replacement of an outdated fleet of equipment at substations of electric power systems (EPS). The paper addressed the issue of upgrading the "Ice" substation in part of the selection of switches, checking the reliability of switches and the reliability of the substation in general. The substation characteristic is given, the changes in the main scheme of the substation electrical connections are substantiated, switches for all substation voltages are selected according to the current short circuit currents. To assess the degree of reliability of the selected switches, the substation is determined by their probability of refusal. Based on the assessment of the likelihood of failures and other equipment for the substation, the estimated time of trouble-free operation was determined, the average time of trouble-free operation and the average substation recovery time.

Modeling the Levelized Cost of Energy for Concentrating Solar Thermal Power Systems Based on a Nonlinear AutoRegressive Neural Network With Exogenous Inputs

This article presents the results of the development of a mathematical model for predicting the levelized cost of energy (LCOE) for solar concentrating thermal power systems (CSP systems) based on a nonlinear autoregressive neural network with exogenous inputs (NARX). A two-layer NARX network with sigmoid hidden neurons and linear output neurons has been developed. The input layer is made up of the following variables: the volume of input power of CSP systems in the world, the total world energy consumption, domestic energy consumption, domestic gas consumption, domestic consumption of coal and lignite, domestic energy consumption, the share of renewable energy in electricity generation, the share of wind and solar energy in the production of electricity, carbon dioxide emissions from fuel combustion, the price of Brent oil against the US dollar, and the average price for natural gas auctions. The output layer specifies LCOE values for CSP systems.