Modeling of Seasonal Variations of Thermal Energy Production by an Electric Power Company based on Neural Network Technology

The paper considers the problem of modeling the dependence of the value of thermal energy production by an electric power company on the air temperature using neural network technology. As an example of an electric power company producing thermal energy, the Public Joint-Stock Company (PJSC) Yakutskenergo. As consumers of thermal energy, organizations, enterprises and the population of the city of Yakutsk, are located at latitude 62 and characterized by a cold northern climate. The numerical experiments carried out in this paper have shown that the general trend of the temperature dependence of thermal energy production, observed empirically, is well described by a neural network

ABC and HML-methods application for determination and optimization of stock for electrical equipment accessory parts

THE PURPOSE. To describe the use of ABC and HML-methods for predicting the volume of emergency stock for main electrical equipment accessory parts. To describe the content of the methods, consider the problems and complexity of use. To propose an algorithm constructing a new method for forming an emergency stock based on the statistical data of an electric power company. METHODS. Various practical tasks can arise in electric power company. We have determined the most effective method for solving them using a numerical experiment. The highest efficiency of the ABC method is shown. The results of statistical processing will help to improve forecast precision using the ABC-method. RESULTS. The complex of statistical data and ABC-analysis showed high efficiency in short-term forecasting of electrical equipment components emergency stocks. Authors found that HML-analysis provides less precision in predicting the requirement of equipment. CONCLUSION. The ABC method is the most promising for implementation the specific task. The use of the method helps to accurately predict emergency stocks of company electrical equipment. The HML-method does not allow to precision planing the amount of required equipment.

ABC and HML-methods application for determination and optimization of stock for electrical equipment accessory parts

THE PURPOSE. To describe the use of ABC and HML-methods for predicting the volume of emergency stock for main electrical equipment accessory parts. To describe the content of the methods, consider the problems and complexity of use. To propose an algorithm constructing a new method for forming an emergency stock based on the statistical data of an electric power company. METHODS. Various practical tasks can arise in electric power company. We have determined the most effective method for solving them using a numerical experiment. The highest efficiency of the ABC method is shown. The results of statistical processing will help to improve forecast precision using the ABC-method. RESULTS. The complex of statistical data and ABC-analysis showed high efficiency in short-term forecasting of electrical equipment components emergency stocks. Authors found that HML-analysis provides less precision in predicting the requirement of equipment. CONCLUSION. The ABC method is the most promising for implementation the specific task. The use of the method helps to accurately predict emergency stocks of company electrical equipment. The HML-method does not allow to precision planing the amount of required equipment.

Evaluation of distributions on radioactive materials around the port of Fukushima Daiichi Nuclear Power Plant through both monitoring and numerical simulations on seawater flow

<p>A large amount of radionuclides has been released into the environment by the accident of Fukushima Daiichi Nuclear Power Plant (1F). About 10 years have passed since the accident, and the effects have significantly decreased. However, it should be noted that they still remain in spite of their tiny contributions. On the other hand, there is now an important demand that the radioactive materials should be monitored continuously due to the decommissioning work at 1F being carried. Thus, we evaluate the behavior of the radioactive materials in seawater around the port of 1F using both the monitoring result measured by TEPCO (Tokyo Electric Power Company Holdings) and the simulation results on seawater flow. In this presentation, we introduce the simulation methods and report the insights through the monitoring results. Finally, we reveal the mechanism of spreading the materials in the seawater around 1F port by evaluating with both the monitoring data and the simulation results.</p><p> </p>

Realizing the Company’s Energy Conservation Goal and Optimizing the Salary Management System – A Case Study of C Electric Power Company

It can be predicted that the development of energy conservation economy will be the long-term theme of struggle from the “energy conservation action of 1000 enterprises” launched during the “Eleventh Five Year Plan” to the “energy conservation action of 10000 enterprises” launched during the “Twelfth Five Year Plan”. Salary management is an important part of enterprise management. Appropriate salary management can motivate employees to work hard towards the direction set by the organization and ultimately achieve the goal of the organization. Under the background of the development of energy conservation economy, this paper takes C Electric Power Company as the research object, analyzes the current situation of its salary management, and then points out the main problems existing in the current salary management of C Company, and puts forward reasonable solutions to these problems, so as to improve the salary management system level of C Company, eventually promote the gradual development of C Company towards the direction of energy conservation.