Industrial Electrical Energy Research Articles

A Survey on the Integration of Solar Photovoltaic Energy Conversion Systems into Commercial and Industrial Electrical Energy Usage

A Survey on the Integration of Solar Photovoltaic Energy Conversion Systems into Commercial and Industrial Electrical Energy Usage

Design high performance biomass-derived renewable carbon material for electric energy storage system

Improving the utilization efficiency of waste biomass resources and industrial electric energy is the key research direction in the clean energy field. Here, rhododendron petals with extensive resources and low cost were used as precursors, the nitrogen-doped derived renewable carbon prepared by simple processing technology demonstrates its feasibility and application value in electrical energy storage systems. For the potassium-ion batteries anode, the renewable carbon delivers superior electrochemical properties: the initial reversible capacity has 294.7 mAh/g, and the retention rate after 150 cycles is 83.5%. Even at the high rate of 2000 mA/g, it still maintains 72.4% after a long cycle. In addition, the energy storage mechanism of the material was verified by different characterization methods: the high potential slope area corresponds to the adsorption behavior of potassium-ion on the surface active sites, and the low potential plateau area shows the characteristics of potassium-ion inserted into graphitic layers. The increased active functional groups and expanded graphitic layer spacing are important factors for the excellent cycle performance and rate capacity of the renewable carbon. These design and application methods have provided strategic guidance and new development directions for reusing biomass waste resources.

Industrial Electrical Energy Consumption Forecasting by using Temporal Convolutional Neural Networks

In conjunction with the 4th Industrial Revolution, many industries are implementing systems to collect data on energy consumption to be able to make informed decision on scheduling processes and manufacturing in factories. Companies can now use this historical data to forecast the expected energy consumption for cost management. This research proposes the use of a Temporal Convolutional Neural Network (TCN) with dilated causal convolutional layers to perform forecasting instead of conventional Long-Short Term Memory (LSTM) or Recurrent Neural Networks (RNN) as TCN exhibit lower memory and computational requirements. This approach is also chosen due to traditional regressive methods such as Autoregressive Integrated Moving Average (ARIMA) fails to capture non-linear patterns and features for multi-step time series data. In this research paper, the electrical energy consumption of a factory will be forecasted by implementing a TCN to extract the features and to capture the complex patterns in time series data such daily electrical energy consumption with a limited dataset. The neural network will be built using Keras and TensorFlow libraries in Python. The energy consumption data as training data will be provided by GoAutomate Sdn Bhd. Then, the historical data of economic factors and indexes such as the KLCI will be included alongside the consumption data for neural network training to determine the effects of the economy on industrial energy consumption. The forecasted results with and without the economic data will then be compared and evaluated using Weighted Average Percentage Error (WAPE) and Mean Absolute Percentage Error (MAPE) metrics. The parameters for the neural network will then be evaluated and fined tuned accordingly based on the accuracy and error metrics. This research is able create a CNN to forecast electrical energy consumption with WAPE = 0.083 & MAPE = 0.092, of a factory one (1) week ahead with a small scale dataset with only 427 data points, and has determined that the effects of economic index such as the Bursa Malaysia has no meaningful impact on industrial energy consumption that can be then applied to the forecasting of energy consumption of the factory.

Benefit evaluation of power substitute project for industrial users

Power substitution in industrial field is an important measure for Chinese government to control air pollution, and how to evaluate the benefit of electric energy substitute project effectively is related to the effectiveness of this measure. In this paper, the Comprehensive Benefit evaluation index system of industrial electric energy substitution project is constructed from three dimensions of economy, environment and society, and in order to avoid the deficiency of single comprehensive evaluation method, a combinatorial model based on anp-entropy method and improved TOPSIS method is established. Finally, the rationality of the evaluation method is verified by an example.

Prediction of Industrial Electric Energy Consumption in Anhui Province Based on GA-BP Neural Network

In order to improve the prediction accuracy of industrial electrical energy consumption, a prediction model of industrial electrical energy consumption was proposed based on genetic algorithm and neural network. The model use genetic algorithm to optimize the weights and thresholds of BP neural network, and the model is used to predict the energy consumption of industrial power in Anhui Province, to improve the prediction accuracy of industrial electric energy consumption in Anhui province. By comparing experiment of GA-BP prediction model and BP neural network model, the GA-BP model is more accurate with smaller number of neurons in the hidden layer.

Realization of a Complex Control & Diagnosis System on Simplified Hardware

Energy is an important factor in today's industrial environment. Pump systems account for about 20% of the total industrial electrical energy consumption. Several studies show that with proper monitoring, control and maintenance, the efficiency of pump systems can be increased. Controlling pump systems with intelligent systems can help to reduce a pump's energy consumption by up to one third of its original consumption. The research in this paper was carried out in the scope of a research project which involves modelling and simulation of pump systems. This paper focuses on the future implementation of modelling capabilities in PLCs (programmable logic controllers). The whole project aims to use a pump itself as the sensor rather than introducing external sensors into the system, which would increase the cost considerably. One promising approach for an economic and robust industrial implementation of this intelligence is the use of PLCs. PLCs can be simulated in multiple ways; in this project, Codesys was chosen for several reasons which are explained in this paper. The first part of this paper explains the modelling of a pump itself, the process load of the asynchronous motor with a control system, and the simulation possibilities of the motor in Codesys. The second part describes the simulation and testing of a system realized. The third part elaborates the Codesys system structure and interfacing of the system with external files. The final part consists of comparing the result with an earlier Matlab/SIMULINK model and original test data.

Industrial Electrical Energy Efficiency Research Based on DEA Optimization Model

Traditional DEA does not take into account the environmental variables when analysis the electrical energy efficiency. In order to weigh the objective and subjective factors’ impact on industrial electrical energy efficiency , this paper attempts to analyze the China's 2011 industrial electrical energy efficiency step by step using DEA optimization model. The results showed that, electrical energy efficiency value in considering the case of environmental factors was increased , effect of the external environment on electrical energy efficiency indeed have a significant impact, On these analysis, the paper proposed some feasible recommendations like increasing the scale of industrial energy utilization, eliminating monopolies as soon as possible, allocating the science and technology funding actively and feasibly. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4454 Full Text: PDF

Hypothesis: the reversal of the relation between economic growth and health progress in Sweden in the nineteenth and twentieth centuries was caused by electrification

The expected decline of health indicators with economic recessions and improvement with economic growth in the nineteenth century Sweden was reversed in the twentieth century, giving the counterintuitive pattern of higher mortality and lower life expectancy in economic expansions and improvement of these indices in recessions. The change or “tipping point” occurred at the end of the nineteenth century or early in the twentieth century when electrification was introduced into Sweden. All 5 of the reversals of annual industrial electric energy use in the US between 1912 and 1970 were accompanied by recessions with lowered GDP, increased unemployment, decreased mortality and increased life expectancy. The health indices were not related to residential electricity use. The mortality improvement between 1931 and 1932 by state in the US strongly favored urban areas over rural areas. Rural unemployment by state in 1930 was significantly positively correlated with residential electrification percentage by state in 1930. The health effects of economic change are mediated by electrical exposure.

A simple forecasting model for industrial electric energy consumption

A simple forecasting model for industrial electric energy consumption

A simple forecasting model for industrial electric energy consumption

A single-equation model is developed and employed for forecasting industrial electric energy consumption in the Saudi Consolidated Electric Company in the Eastern Province (SCECO-East) of Saudi Arabia. SCECO-East's industrial loads are composed mainly of oil-related and petrochemical industries. Even though industrial loads are generally characterized by their steadiness, the harsh weather conditions of the Eastern Province cause great variations in the industrial electric energy consumption at SCECO-East. The developed model reflects these variations. MATLAB is used to solve the model. Copyright © 2000 John Wiley & Sons, Ltd.

Conception of a Combined Icf Power Plant and a Fission Reactor-Laser Driver

This paper is devoted to the concept of combined thermonuclear plant with a reactor-laser driver for industrial electric energy production, based on the inertial plasma confinement. The plant is re...