Abstract
In order to promote my country’s comprehensive green development, this paper conducts a research on the correlation mapping between electricity consumption and environmental protection data. Based on the existing short-term electricity load forecasting model, the electricity load forecast data is divided into active electricity and reactive electricity, so as to realize the comprehensive tracking of the operation status of the enterprise’s electrical equipment. The comparative learning algorithm is used to obtain the association mapping of the electricity consumption-environmental data distribution, so that the Encoder data of the pollution discharge data can be obtained based on the electricity consumption. Subsequently, the generator generates enterprise short-term emission forecast data based on the coded data, and combines the Wasserstein distance with the mean square error of similar samples to construct a loss function to improve the quality of the generated data. Analysis and comparison of calculation examples through simulation experiments has show the effectiveness and feasibility of the proposed.
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Liang, X., Ma, C., Ma, C., Xin, S. (2022). Dynamic Monitoring Method Based on Comparative Study of Power and Environmental Protection Indicators. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1637. Springer, Singapore. https://doi.org/10.1007/978-981-19-6142-7_5
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DOI: https://doi.org/10.1007/978-981-19-6142-7_5
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