Nowadays, our life needs more and more electricity, and our lives cannot be without electricity, which requires our power to develop more quickly. Power plants are undoubtedly the place where electricity is produced. And now most of the power plant or chemical energy can be converted into heat, and then through the heat to do power production. The boiler is the main part of the power plant. Boiler unit consists of boiler body equipment and auxiliary equipment. The main body of the boiler consists of 'pot' (soft drinks system) and 'furnace' (combustion system). Baotou thermal power plant is mainly burning gas. The gas and air are at a certain rate into the furnace burning. This can greatly reduce the pollution of the environment, but also the full use of fuel. The soda system is mainly carried out in the drum. The heat generated by the combustion system heats the water in the drum, producing steam and then pushing the steam turbine into mechanical energy and finally into electrical energy. This has a high demand for water level, water composition, and the temperature of the steam produced in the drum. The water level should have upper and lower bounds, keeping it within a certain range. Water level is too high, will affect the steam drum soda separation effect, so that the steam drum exports of saturated steam with water increased, causing damage to the turbine, will cause serious explosion. And the water level is too low, it will affect the natural circulation of the normal, serious will make the individual water pipe to form a free water, resulting in flow stagnation, resulting in local metal wall overheating and burst pipe. Water in the heating at the same time will form a lot of scale, if not the chemical treatment of water will be in the formation of scale in the drum, cleaning more difficult, so the damage to the drum. The pressure of the drum is also an important control variable, and pressure control is highly correlated with liquid level control. It is necessary to ensure the integrity of the equipment, but also to ensure safety, followed by ensuring that the process of normal operation of the drum water. This time, the design is mainly for the unit steam temperature control system design. Steam temperature is one of the important indicators of boiler operation quality. It is too high and too low will significantly affect the power plant safety and economy. If the temperature of the steam is low, it will cause the power plant to increase the heat consumption and increase the axial thrust of the turbine to cause the thrust bearing to overload, but also cause the steam turbine to increase the final steam humidity, thus reducing the efficiency of the turbine, aggravating the erosion of the blade. On the contrary, the steam temperature is too high will make the super-heater wall metal strength decreased, and even burn the high temperature of the super-heater, the steam pipe and steam turbine high-pressure part will be damaged, seriously affecting safety. The boiler temperature control system mainly includes the adjustment of the superheated steam and the reheat steam temperature. The superheated steam temperature is the highest temperature in the boiler soda system. The stability of the steam temperature is very important for the safe and economical operation of the unit. Therefore, in the boiler operation, must ensure that the steam temperature in the vicinity of the specified value, and the temperature of the super-heater tube wall does not exceed the allowable working temperature.

The increasing epileptic electricity supply, mainly in the residential areas of Nigerian cities, has been linked to the incorrect knowledge of the numerous socio-economic and physical indices that influence household electricity usage. Most of the seemingly identified explanatory factors were done at macro level which does not give a clear estimate of this electricity demand. The thrust of the study is to analyse empirically the household electricity determinants in Nigerian cities with a view to evolving a more informed and sustainable energy policy decision. Multistage area cluster sampling method was adopted in the study where 769 copies of structured questionnaire were distributed to electricity users of prepaid meters in five major Nigerian cities. The research hypothesis was tested using the multiple linear regression statistical tool. The result revealed that nine variables which include age (r = 0.05, p-value: 0.05), household income (r = 0.00, p-value: 0.05), number of hours that people stay outside the house (r = 0.043, p-value: 0.05), number of teenagers at home, (r = 0.006, p-value: 0.01) number of electrical appliances (r = 0.016, p-value: 0.01), type of house (r = 0.012, p-value: 0.01), hours that the electrical appliances are used (r = 0.043, p-value: 0.05), weather condition, (r = 0.011, p-value: 0.05) and the location of the building (r = 0.045, p-value: 0.05) were significant in determining the household electricity consumption. Policies based on the findings will give energy and urban planners an empirical basis for accurate and robust forecasting of the determinants that influence household electricity consumption in Nigeria that is devoid of any speculation or unfounded predictions.

Smart electric meters play a pivotal role in making energy systems decarbonized and automating the energy system. Smart electric meters denote huge business opportunities for both public and private companies. Utility players can manage the electricity demand more efficiently whereas customers can monitor and control the electricity bill through the adoption of smart electric meters. The study examines the factors affecting the adoption intention of smart electric meters in Indian households. This study draws a roadmap that how utility providers and customers can improve the smart electric meters adoption. The study has five independent variables (performance expectancy, effort expectancy, social influence, environmentalism, and hedonic motivation) and one dependent variable (adoption intention). The sample size for the study is four hundred and sixty-two respondents from Delhi and the National Capital Region (NCR). The data was analysed using structural equation modelling (SEM). The results of this study have confirmed that performance expectancy, environmentalism, and social influence have a significant impact on the intention of adopting smart electric meters. Therefore, utility providers can improve their strategies to attract more customers to adopt smart electric meters by focusing more on the performance of smart electric meters and by making them environmentally friendly. This research offers meaningful insights to both customers and utility providers to make energy systems decarbonized and control energy consumption.

The increasing demand for electricity and the need to reduce carbon emissions have made optimizing energy usage and promoting sustainability critical in the modern economy. This research paper explores the design and implementation of an Intelligent-Electricity Consumption and Billing Information System (IEBCIS), focusing on its role in addressing electricity sustainability challenges. Using the Design Science Research (DSR) methodology, the system's architecture collects, analyses, and visualizes electricity usage data, providing users with valuable insights into their consumption patterns. The research involved developing and validating the IEBCIS prototype, with results demonstrating enhanced real-time monitoring, load shedding schedules, and billing information. These results were validated through user testing and feedback, contributing to the scientific knowledge of intelligent energy management systems. The contributions of this research include the development of a framework for intelligent energy management and the integration of data-driven insights to optimize electricity consumption, reduce costs, and promote sustainable energy use. This research was conducted over a time scope of two years (24 months) and entails design, development, pilot test implementation and validation phases.

Electricity consumption in Europe has risen significantly in recent years, with households being the largest consumers of final electricity. Managing and reducing residential power consumption is critical for achieving efficient and sustainable energy management, conserving financial resources, and mitigating environmental effects. Many studies have used statistical models such as linear, multinomial, ridge, polynomial, and LASSO regression to examine and understand the determinants of residential energy consumption. However, these models are limited to capturing only direct effects among the determinants of household energy consumption. This study addresses these limitations by applying a path analysis model that captures the direct and indirect effects. Numerical and theoretical comparisons that demonstrate its advantages and efficiency are also given. The results show that Sub-metering components associated with specific uses, like cooking or water heating, have significant indirect impacts on global intensity through active power and that the voltage affects negatively the global power (active and reactive) due to the physical and behavioral mechanisms. Our findings provide an in-depth understanding of household electricity power consumption. This will improve forecasting and enable real-time energy management tools, extending to the design of precise energy efficiency policies to achieve SDG 7’s objectives.