Research on intelligent supply and management of power energy for manufacturing enterprises
In order to achieve the goal of carbon peak and carbon neutrality,it is necessary to continuously promote the high-quality development of manufacturing enterprises and establish a new development pattern of energy saving and consumption reduction.In response to the new development needs,the energy structure of manufacturing enterprises needs to be continuously optimized,and the power energy management method directly affects the energy structure layout of manufacturing enterprises.Different functional workshops in manufacturing enterprises have different equipment operation modes and energy consumption characteristics.Many years of production and operation have accumulated a large amount of energy usage data,but these data have not been fully utilized,resulting in data isolation and seriously affecting the production efficiency of manufacturing enterprises.Therefore,there is an urgent need for a power energy management method for different working conditions of manufacturing enterprises.In order to solve the above problems,taking steam energy as an example,the steam prediction models for process and air-conditioning were constructed to realize the intelligent supply and management of steam to meet the production process requirements of manufacturing enterprises.Firstly,a steam prediction model for process based on work section division was proposed to forecast process steam consumption with strong periodic characteristics.After the model improvement,the average standard energy consumption for process was reduced by 5.12%.Then,a steam prediction model for modular air-conditioning based on hybrid deep learning and a steam prediction model for independent air-conditioning based on multiple scenarios were constructed.Through comparing with other prediction models,the effectiveness and accuracy of the proposed model were verified.The results showed that the proposed power energy prediction model had wide applicability and could be applied to the management of different power energy in other manufacturing enterprises after proper modification and adjustment.The research results are helpful for relevant manufacturing enterprises to make full use of historical energy usage data,achieve energy conservation and efficiency improvement in power energy,and provide strong support for the digital transformation and upgrading of manufacturing enterprises in China.
power energyintelligent managementsteam supplymulti-condition predictionhybrid deep learning
国家科技期刊平台
NSTL
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